Merge pull request #173 from presenton/feat/claude-support

feat/claude support

.dockerignore

@@ -8,4 +8,5 @@ build
 .gitignore
 tmp
 debug
-.fastembed_cache
+.fastembed_cache
+generated_models

.gitignore

@@ -11,4 +11,5 @@ app_data
 tmp
 debug
 .fastembed_cache
-my-doc.txt
+my-doc.txt
+generated_models

Dockerfile

@@ -2,12 +2,11 @@ FROM python:3.11-slim-bookworm
 
 # Install Node.js and npm
 RUN apt-get update && apt-get install -y \
-   
     nginx \
     curl \
     redis-server
 
-    # Install Node.js 20 using NodeSource repository
+# Install Node.js 20 using NodeSource repository
 RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
     apt-get install -y nodejs
 
@@ -43,7 +42,7 @@ RUN npm run build
 
 WORKDIR /app
 
-# Copy FastAPI and start script
+# Copy FastAPI
 COPY servers/fastapi/ ./servers/fastapi/
 COPY start.js LICENSE NOTICE ./
 

Dockerfile.dev

@@ -7,9 +7,9 @@ RUN apt-get update && apt-get install -y \
   redis-server
 
 
-    # Install Node.js 20 using NodeSource repository
+# Install Node.js 20 using NodeSource repository
 RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - && \
-    apt-get install -y nodejs
+  apt-get install -y nodejs
 
 
 # Change working directory

docker-compose.yml

@@ -14,12 +14,15 @@ services:
       - LLM=${LLM}
       - OPENAI_API_KEY=${OPENAI_API_KEY}
       - GOOGLE_API_KEY=${GOOGLE_API_KEY}
+      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
+      - ANTHROPIC_MODEL=${ANTHROPIC_MODEL}
       - OLLAMA_URL=${OLLAMA_URL}
       - OLLAMA_MODEL=${OLLAMA_MODEL}
       - CUSTOM_LLM_URL=${CUSTOM_LLM_URL}
       - CUSTOM_LLM_API_KEY=${CUSTOM_LLM_API_KEY}
       - CUSTOM_MODEL=${CUSTOM_MODEL}
       - PEXELS_API_KEY=${PEXELS_API_KEY}
+      - EXTENDED_REASONING=${EXTENDED_REASONING}
       - DATABASE_URL=${DATABASE_URL}
 
   production-gpu:
@@ -44,12 +47,15 @@ services:
       - LLM=${LLM}
       - OPENAI_API_KEY=${OPENAI_API_KEY}
       - GOOGLE_API_KEY=${GOOGLE_API_KEY}
+      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
+      - ANTHROPIC_MODEL=${ANTHROPIC_MODEL}
       - OLLAMA_URL=${OLLAMA_URL}
       - OLLAMA_MODEL=${OLLAMA_MODEL}
       - CUSTOM_LLM_URL=${CUSTOM_LLM_URL}
       - CUSTOM_LLM_API_KEY=${CUSTOM_LLM_API_KEY}
       - CUSTOM_MODEL=${CUSTOM_MODEL}
       - PEXELS_API_KEY=${PEXELS_API_KEY}
+      - EXTENDED_REASONING=${EXTENDED_REASONING}
       - DATABASE_URL=${DATABASE_URL}
 
   development:
@@ -67,12 +73,15 @@ services:
       - LLM=${LLM}
       - OPENAI_API_KEY=${OPENAI_API_KEY}
       - GOOGLE_API_KEY=${GOOGLE_API_KEY}
+      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
+      - ANTHROPIC_MODEL=${ANTHROPIC_MODEL}
       - OLLAMA_URL=${OLLAMA_URL}
       - OLLAMA_MODEL=${OLLAMA_MODEL}
       - CUSTOM_LLM_URL=${CUSTOM_LLM_URL}
       - CUSTOM_LLM_API_KEY=${CUSTOM_LLM_API_KEY}
       - CUSTOM_MODEL=${CUSTOM_MODEL}
       - PEXELS_API_KEY=${PEXELS_API_KEY}
+      - EXTENDED_REASONING=${EXTENDED_REASONING}
       - DATABASE_URL=${DATABASE_URL}
 
   development-gpu:
@@ -97,10 +106,13 @@ services:
       - LLM=${LLM}
       - OPENAI_API_KEY=${OPENAI_API_KEY}
       - GOOGLE_API_KEY=${GOOGLE_API_KEY}
+      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
+      - ANTHROPIC_MODEL=${ANTHROPIC_MODEL}
       - OLLAMA_URL=${OLLAMA_URL}
       - OLLAMA_MODEL=${OLLAMA_MODEL}
       - CUSTOM_LLM_URL=${CUSTOM_LLM_URL}
       - CUSTOM_LLM_API_KEY=${CUSTOM_LLM_API_KEY}
       - CUSTOM_MODEL=${CUSTOM_MODEL}
       - PEXELS_API_KEY=${PEXELS_API_KEY}
+      - EXTENDED_REASONING=${EXTENDED_REASONING}
       - DATABASE_URL=${DATABASE_URL}

servers/fastapi/api/main.py

@@ -1,10 +1,8 @@
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.staticfiles import StaticFiles
 from api.lifespan import app_lifespan
 from api.middlewares import UserConfigEnvUpdateMiddleware
 from api.v1.ppt.router import API_V1_PPT_ROUTER
-from utils.asset_directory_utils import get_exports_directory, get_images_directory, get_uploads_directory
 
 
 app = FastAPI(lifespan=app_lifespan)
@@ -13,25 +11,6 @@ app = FastAPI(lifespan=app_lifespan)
 # Routers
 app.include_router(API_V1_PPT_ROUTER)
 
-# Static files
-app.mount("/static", StaticFiles(directory="static"), name="static")
-app.mount(
-    "/app_data/images",
-    StaticFiles(directory=get_images_directory()),
-    name="app_data/images",
-)
-app.mount(
-    "/app_data/exports",
-    StaticFiles(directory=get_exports_directory()),
-    name="app_data/exports",
-)
-app.mount(
-    "/app_data/uploads",
-    StaticFiles(directory=get_uploads_directory()),
-    name="app_data/uploads",
-)
-
-
 # Middlewares
 origins = ["*"]
 app.add_middleware(

servers/fastapi/api/v1/ppt/endpoints/anthropic.py

@@ -0,0 +1,16 @@
+from typing import Annotated, List
+from fastapi import APIRouter, Body, HTTPException
+
+from utils.available_models import list_available_anthropic_models
+
+ANTHROPIC_ROUTER = APIRouter(prefix="/anthropic", tags=["Anthropic"])
+
+
+@ANTHROPIC_ROUTER.post("/models/available", response_model=List[str])
+async def get_available_models(
+    api_key: Annotated[str, Body(embed=True)],
+):
+    try:
+        return await list_available_anthropic_models(api_key)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

servers/fastapi/api/v1/ppt/endpoints/custom_llm.py

@@ -1,14 +0,0 @@
-from typing import Annotated, List, Optional
-from fastapi import APIRouter, Body
-
-from utils.custom_llm_provider import list_available_custom_models
-
-CUSTOM_LLM_ROUTER = APIRouter(prefix="/custom_llm", tags=["Custom LLM"])
-
-
-@CUSTOM_LLM_ROUTER.post("/models/available", response_model=List[str])
-async def get_available_models(
-    url: Annotated[Optional[str], Body()] = None,
-    api_key: Annotated[Optional[str], Body()] = None,
-):
-    return await list_available_custom_models(url, api_key)

servers/fastapi/api/v1/ppt/endpoints/google.py

@@ -0,0 +1,14 @@
+from typing import Annotated, List
+from fastapi import APIRouter, Body, HTTPException
+
+from utils.available_models import list_available_google_models
+
+GOOGLE_ROUTER = APIRouter(prefix="/google", tags=["Google"])
+
+
+@GOOGLE_ROUTER.post("/models/available", response_model=List[str])
+async def get_available_models(api_key: Annotated[str, Body(embed=True)]):
+    try:
+        return await list_available_google_models(api_key)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

servers/fastapi/api/v1/ppt/endpoints/openai.py

@@ -0,0 +1,17 @@
+from typing import Annotated, List
+from fastapi import APIRouter, Body, HTTPException
+
+from utils.available_models import list_available_openai_compatible_models
+
+OPENAI_ROUTER = APIRouter(prefix="/openai", tags=["OpenAI"])
+
+
+@OPENAI_ROUTER.post("/models/available", response_model=List[str])
+async def get_available_models(
+    url: Annotated[str, Body()],
+    api_key: Annotated[str, Body()],
+):
+    try:
+        return await list_available_openai_compatible_models(url, api_key)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

servers/fastapi/api/v1/ppt/endpoints/outlines.py

@@ -54,7 +54,6 @@ async def stream_outlines(
         presentation.outlines = [
             each.model_dump() for each in presentation_content.slides
         ]
-        presentation.notes = presentation_content.notes
 
         sql_session.add(presentation)
         await sql_session.commit()

servers/fastapi/api/v1/ppt/endpoints/presentation.py

@@ -2,10 +2,11 @@ import asyncio
 import json
 import os
 import random
+import importlib
 from typing import Annotated, List, Literal, Optional
 from fastapi import APIRouter, Body, Depends, File, HTTPException, UploadFile
 from fastapi.responses import StreamingResponse
-from sqlalchemy import String, cast, delete
+from sqlalchemy import delete
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlmodel import select
 from constants.documents import UPLOAD_ACCEPTED_FILE_TYPES
@@ -19,7 +20,7 @@ from models.pptx_models import PptxPresentationModel
 from models.presentation_layout import PresentationLayoutModel
 from models.presentation_structure_model import PresentationStructureModel
 from models.presentation_with_slides import PresentationWithSlides
-from services.get_layout_by_name import get_layout_by_name
+from utils.get_layout_by_name import get_layout_by_name
 from services.icon_finder_service import IconFinderService
 from services.image_generation_service import ImageGenerationService
 from utils.dict_utils import deep_update
@@ -217,9 +218,11 @@ async def stream_presentation(
         ).to_string()
         for i, slide_layout_index in enumerate(structure.slides):
             slide_layout = layout.slides[slide_layout_index]
+
             slide_content = await get_slide_content_from_type_and_outline(
                 slide_layout, outline.slides[i], presentation.language
             )
+
             slide = SlideModel(
                 presentation=presentation_id,
                 layout_group=layout.name,
@@ -236,9 +239,6 @@ async def stream_presentation(
                 )
             )
 
-            # Give control to the event loop
-            await asyncio.sleep(0)
-
             yield SSEResponse(
                 event="response",
                 data=json.dumps({"type": "chunk", "chunk": slide.model_dump_json()}),
@@ -475,7 +475,6 @@ async def from_template(
         new_slide_data = list(filter(lambda x: x.index == each_slide.index, data.data))
         if new_slide_data:
             updated_content = deep_update(each_slide.content, new_slide_data[0].content)
-            print(f"Updated content for slide {each_slide.index}: {updated_content}")
         new_slides.append(
             each_slide.get_new_slide(new_presentation.id, updated_content)
         )

servers/fastapi/api/v1/ppt/endpoints/slide.py

@@ -1,3 +1,4 @@
+import importlib
 from typing import Annotated, Optional
 from fastapi import APIRouter, Body, Depends, HTTPException
 from sqlalchemy.ext.asyncio import AsyncSession
@@ -13,6 +14,7 @@ from utils.llm_calls.edit_slide_html import get_edited_slide_html
 from utils.llm_calls.select_slide_type_on_edit import get_slide_layout_from_prompt
 from utils.process_slides import process_old_and_new_slides_and_fetch_assets
 from utils.randomizers import get_random_uuid
+from utils.schema_utils import remove_fields_from_schema
 
 
 SLIDE_ROUTER = APIRouter(prefix="/slide", tags=["Slide"])
@@ -32,12 +34,12 @@ async def edit_slide(
         raise HTTPException(status_code=404, detail="Presentation not found")
 
     presentation_layout = presentation.get_layout()
-
     slide_layout = await get_slide_layout_from_prompt(
         prompt, presentation_layout, slide
     )
+
     edited_slide_content = await get_edited_slide_content(
-        prompt, slide_layout, slide, presentation.language
+        prompt, slide, presentation.language, slide_layout
     )
 
     image_generation_service = ImageGenerationService(get_images_directory())

servers/fastapi/api/v1/ppt/router.py

@@ -1,6 +1,8 @@
 from fastapi import APIRouter
 
-from api.v1.ppt.endpoints.custom_llm import CUSTOM_LLM_ROUTER
+from api.v1.ppt.endpoints.anthropic import ANTHROPIC_ROUTER
+from api.v1.ppt.endpoints.google import GOOGLE_ROUTER
+from api.v1.ppt.endpoints.openai import OPENAI_ROUTER
 from api.v1.ppt.endpoints.files import FILES_ROUTER
 from api.v1.ppt.endpoints.icons import ICONS_ROUTER
 from api.v1.ppt.endpoints.images import IMAGES_ROUTER
@@ -19,4 +21,6 @@ API_V1_PPT_ROUTER.include_router(SLIDE_ROUTER)
 API_V1_PPT_ROUTER.include_router(IMAGES_ROUTER)
 API_V1_PPT_ROUTER.include_router(ICONS_ROUTER)
 API_V1_PPT_ROUTER.include_router(OLLAMA_ROUTER)
-API_V1_PPT_ROUTER.include_router(CUSTOM_LLM_ROUTER)
+API_V1_PPT_ROUTER.include_router(OPENAI_ROUTER)
+API_V1_PPT_ROUTER.include_router(ANTHROPIC_ROUTER)
+API_V1_PPT_ROUTER.include_router(GOOGLE_ROUTER)

servers/fastapi/constants/llm.py

@@ -0,0 +1,6 @@
+OPENAI_URL = "https://api.openai.com/v1"
+
+# Default models
+DEFAULT_OPENAI_MODEL = "gpt-4.1"
+DEFAULT_GOOGLE_MODEL = "models/gemini-2.0-flash"
+DEFAULT_ANTHROPIC_MODEL = "claude-3-5-sonnet-20240620"

servers/fastapi/constants/supported_ollama_models.py

@@ -7,7 +7,6 @@ SUPPORTED_OLLAMA_MODELS = {
         value="llama3:8b",
         description="❌ Graphs not supported.",
         size="4.7GB",
-        supports_graph=False,
         icon="/static/icons/meta.png",
     ),
     "llama3:70b": OllamaModelMetadata(
@@ -15,7 +14,6 @@ SUPPORTED_OLLAMA_MODELS = {
         value="llama3:70b",
         description="✅ Graphs supported.",
         size="40GB",
-        supports_graph=True,
         icon="/static/icons/meta.png",
     ),
     "llama3.1:8b": OllamaModelMetadata(
@@ -23,7 +21,6 @@ SUPPORTED_OLLAMA_MODELS = {
         value="llama3.1:8b",
         description="❌ Graphs not supported.",
         size="4.9GB",
-        supports_graph=False,
         icon="/static/icons/meta.png",
     ),
     "llama3.1:70b": OllamaModelMetadata(
@@ -31,7 +28,6 @@ SUPPORTED_OLLAMA_MODELS = {
         value="llama3.1:70b",
         description="✅ Graphs supported.",
         size="43GB",
-        supports_graph=True,
         icon="/static/icons/meta.png",
     ),
     "llama3.1:405b": OllamaModelMetadata(
@@ -39,7 +35,6 @@ SUPPORTED_OLLAMA_MODELS = {
         value="llama3.1:405b",
         description="✅ Graphs supported.",
         size="243GB",
-        supports_graph=True,
         icon="/static/icons/meta.png",
     ),
     "llama3.2:1b": OllamaModelMetadata(
@@ -47,7 +42,6 @@ SUPPORTED_OLLAMA_MODELS = {
         value="llama3.2:1b",
         description="❌ Graphs not supported.",
         size="1.3GB",
-        supports_graph=False,
         icon="/static/icons/meta.png",
     ),
     "llama3.2:3b": OllamaModelMetadata(
@@ -55,7 +49,6 @@ SUPPORTED_OLLAMA_MODELS = {
         value="llama3.2:3b",
         description="❌ Graphs not supported.",
         size="2GB",
-        supports_graph=False,
         icon="/static/icons/meta.png",
     ),
     "llama3.3:70b": OllamaModelMetadata(
@@ -63,7 +56,6 @@ SUPPORTED_OLLAMA_MODELS = {
         value="llama3.3:70b",
         description="✅ Graphs supported.",
         size="43GB",
-        supports_graph=True,
         icon="/static/icons/meta.png",
     ),
     "llama4:16x17b": OllamaModelMetadata(
@@ -71,7 +63,6 @@ SUPPORTED_OLLAMA_MODELS = {
         value="llama4:16x17b",
         description="✅ Graphs supported.",
         size="67GB",
-        supports_graph=True,
         icon="/static/icons/meta.png",
     ),
     "llama4:128x17b": OllamaModelMetadata(
@@ -79,7 +70,6 @@ SUPPORTED_OLLAMA_MODELS = {
         value="llama4:128x17b",
         description="✅ Graphs supported.",
         size="245GB",
-        supports_graph=True,
         icon="/static/icons/meta.png",
     ),
 }
@@ -90,7 +80,6 @@ SUPPORTED_GEMMA_MODELS = {
         value="gemma3:1b",
         description="❌ Graphs not supported.",
         size="815MB",
-        supports_graph=False,
         icon="/static/icons/gemma.png",
     ),
     "gemma3:4b": OllamaModelMetadata(
@@ -98,7 +87,6 @@ SUPPORTED_GEMMA_MODELS = {
         value="gemma3:4b",
         description="❌ Graphs not supported.",
         size="3.3GB",
-        supports_graph=False,
         icon="/static/icons/gemma.png",
     ),
     "gemma3:12b": OllamaModelMetadata(
@@ -106,7 +94,6 @@ SUPPORTED_GEMMA_MODELS = {
         value="gemma3:12b",
         description="❌ Graphs not supported.",
         size="8.1GB",
-        supports_graph=False,
         icon="/static/icons/gemma.png",
     ),
     "gemma3:27b": OllamaModelMetadata(
@@ -114,7 +101,6 @@ SUPPORTED_GEMMA_MODELS = {
         value="gemma3:27b",
         description="✅ Graphs supported.",
         size="17GB",
-        supports_graph=True,
         icon="/static/icons/gemma.png",
     ),
 }
@@ -125,7 +111,6 @@ SUPPORTED_DEEPSEEK_MODELS = {
         value="deepseek-r1:1.5b",
         description="❌ Graphs not supported.",
         size="1.1GB",
-        supports_graph=False,
         icon="/static/icons/deepseek.png",
     ),
     "deepseek-r1:7b": OllamaModelMetadata(
@@ -133,7 +118,6 @@ SUPPORTED_DEEPSEEK_MODELS = {
         value="deepseek-r1:7b",
         description="❌ Graphs not supported.",
         size="4.7GB",
-        supports_graph=False,
         icon="/static/icons/deepseek.png",
     ),
     "deepseek-r1:8b": OllamaModelMetadata(
@@ -141,7 +125,6 @@ SUPPORTED_DEEPSEEK_MODELS = {
         value="deepseek-r1:8b",
         description="❌ Graphs not supported.",
         size="5.2GB",
-        supports_graph=False,
         icon="/static/icons/deepseek.png",
     ),
     "deepseek-r1:14b": OllamaModelMetadata(
@@ -149,7 +132,6 @@ SUPPORTED_DEEPSEEK_MODELS = {
         value="deepseek-r1:14b",
         description="❌ Graphs not supported.",
         size="9GB",
-        supports_graph=False,
         icon="/static/icons/deepseek.png",
     ),
     "deepseek-r1:32b": OllamaModelMetadata(
@@ -157,7 +139,6 @@ SUPPORTED_DEEPSEEK_MODELS = {
         value="deepseek-r1:32b",
         description="✅ Graphs supported.",
         size="20GB",
-        supports_graph=True,
         icon="/static/icons/deepseek.png",
     ),
     "deepseek-r1:70b": OllamaModelMetadata(
@@ -165,7 +146,6 @@ SUPPORTED_DEEPSEEK_MODELS = {
         value="deepseek-r1:70b",
         description="✅ Graphs supported.",
         size="43GB",
-        supports_graph=True,
         icon="/static/icons/deepseek.png",
     ),
     "deepseek-r1:671b": OllamaModelMetadata(
@@ -173,7 +153,6 @@ SUPPORTED_DEEPSEEK_MODELS = {
         value="deepseek-r1:671b",
         description="✅ Graphs supported.",
         size="404GB",
-        supports_graph=True,
         icon="/static/icons/deepseek.png",
     ),
 }
@@ -184,7 +163,6 @@ SUPPORTED_QWEN_MODELS = {
         value="qwen3:0.6b",
         description="❌ Graphs not supported.",
         size="523MB",
-        supports_graph=False,
         icon="/static/icons/qwen.png",
     ),
     "qwen3:1.7b": OllamaModelMetadata(
@@ -192,7 +170,6 @@ SUPPORTED_QWEN_MODELS = {
         value="qwen3:1.7b",
         description="❌ Graphs not supported.",
         size="1.4GB",
-        supports_graph=False,
         icon="/static/icons/qwen.png",
     ),
     "qwen3:4b": OllamaModelMetadata(
@@ -200,7 +177,6 @@ SUPPORTED_QWEN_MODELS = {
         value="qwen3:4b",
         description="❌ Graphs not supported.",
         size="2.6GB",
-        supports_graph=False,
         icon="/static/icons/qwen.png",
     ),
     "qwen3:8b": OllamaModelMetadata(
@@ -208,7 +184,6 @@ SUPPORTED_QWEN_MODELS = {
         value="qwen3:8b",
         description="❌ Graphs not supported.",
         size="5.2GB",
-        supports_graph=False,
         icon="/static/icons/qwen.png",
     ),
     "qwen3:14b": OllamaModelMetadata(
@@ -216,7 +191,6 @@ SUPPORTED_QWEN_MODELS = {
         value="qwen3:14b",
         description="❌ Graphs not supported.",
         size="9.3GB",
-        supports_graph=False,
         icon="/static/icons/qwen.png",
     ),
     "qwen3:30b": OllamaModelMetadata(
@@ -224,7 +198,6 @@ SUPPORTED_QWEN_MODELS = {
         value="qwen3:30b",
         description="✅ Graphs supported.",
         size="19GB",
-        supports_graph=True,
         icon="/static/icons/qwen.png",
     ),
     "qwen3:32b": OllamaModelMetadata(
@@ -232,7 +205,6 @@ SUPPORTED_QWEN_MODELS = {
         value="qwen3:32b",
         description="✅ Graphs supported.",
         size="20GB",
-        supports_graph=True,
         icon="/static/icons/qwen.png",
     ),
     "qwen3:235b": OllamaModelMetadata(
@@ -240,7 +212,6 @@ SUPPORTED_QWEN_MODELS = {
         value="qwen3:235b",
         description="✅ Graphs supported.",
         size="142GB",
-        supports_graph=True,
         icon="/static/icons/qwen.png",
     ),
 }

servers/fastapi/enums/llm_provider.py

@@ -5,4 +5,5 @@ class LLMProvider(Enum):
     OLLAMA = "ollama"
     OPENAI = "openai"
     GOOGLE = "google"
+    ANTHROPIC = "anthropic"
     CUSTOM = "custom"

servers/fastapi/models/llm_message.py

@@ -0,0 +1,7 @@
+from typing import Literal
+from pydantic import BaseModel
+
+
+class LLMMessage(BaseModel):
+    role: Literal["user", "system"]
+    content: str

servers/fastapi/models/ollama_model_metadata.py

@@ -7,4 +7,3 @@ class OllamaModelMetadata(BaseModel):
     description: str
     icon: str
     size: str
-    supports_graph: bool

servers/fastapi/models/presentation_layout.py

@@ -1,4 +1,5 @@
 from typing import List, Optional
+from fastapi import HTTPException
 from pydantic import BaseModel, Field
 
 from models.presentation_structure_model import PresentationStructureModel
@@ -12,10 +13,18 @@ class SlideLayoutModel(BaseModel):
 
 
 class PresentationLayoutModel(BaseModel):
-    name: Optional[str] = None
+    name: str
     ordered: bool = Field(default=False)
     slides: List[SlideLayoutModel]
 
+    def get_slide_layout_index(self, slide_layout_id: str) -> int:
+        for index, slide in enumerate(self.slides):
+            if slide.id == slide_layout_id:
+                return index
+        raise HTTPException(
+            status_code=404, detail=f"Slide layout {slide_layout_id} not found"
+        )
+
     def to_presentation_structure(self):
         return PresentationStructureModel(
             slides=[index for index in range(len(self.slides))]

servers/fastapi/models/presentation_outline_model.py

@@ -15,7 +15,6 @@ class PresentationOutlineModel(BaseModel):
     title: str = Field(
         description="Title of the presentation in about 3 to 8 words",
     )
-    notes: Optional[List[str]] = Field(default=None, description="Notes for the presentation")
     slides: List[SlideOutlineModel] = Field(description="List of slides")
 
     def to_string(self):
@@ -25,8 +24,8 @@ class PresentationOutlineModel(BaseModel):
             message += f"  - Title: {slide.title} \n"
             message += f"  - Body: {slide.body} \n"
 
-        if self.notes:
-            message += f"# Notes: \n"
-            for note in self.notes:
-                message += f"  - {note} \n"
+        # if self.notes:
+        #     message += f"# Notes: \n"
+        #     for note in self.notes:
+        #         message += f"  - {note} \n"
         return message

servers/fastapi/models/sql/presentation.py

@@ -46,7 +46,7 @@ class PresentationModel(SQLModel, table=True):
         return PresentationOutlineModel(
             title=self.title,
             slides=[SlideOutlineModel(**each) for each in self.outlines],
-            notes=self.notes,
+            # notes=self.notes,
         )
 
     def get_layout(self):

servers/fastapi/models/user_config.py

@@ -4,13 +4,32 @@ from pydantic import BaseModel
 
 class UserConfig(BaseModel):
     LLM: Optional[str] = None
+
+    # OpenAI
     OPENAI_API_KEY: Optional[str] = None
+    OPENAI_MODEL: Optional[str] = None
+
+    # Google
     GOOGLE_API_KEY: Optional[str] = None
+    GOOGLE_MODEL: Optional[str] = None
+
+    # Anthropic
+    ANTHROPIC_API_KEY: Optional[str] = None
+    ANTHROPIC_MODEL: Optional[str] = None
+
+    # Ollama
     OLLAMA_URL: Optional[str] = None
     OLLAMA_MODEL: Optional[str] = None
+
+    # Custom LLM
     CUSTOM_LLM_URL: Optional[str] = None
     CUSTOM_LLM_API_KEY: Optional[str] = None
     CUSTOM_MODEL: Optional[str] = None
-    PEXELS_API_KEY: Optional[str] = None
+
+    # Image Provider
     IMAGE_PROVIDER: Optional[str] = None
+    PEXELS_API_KEY: Optional[str] = None
     PIXABAY_API_KEY: Optional[str] = None
+
+    # Reasoning
+    EXTENDED_REASONING: Optional[bool] = None

servers/fastapi/requirements.txt

@@ -4,12 +4,15 @@ aiomysql==0.2.0
 aiosignal==1.4.0
 aiosqlite==0.21.0
 annotated-types==0.7.0
+anthropic==0.60.0
 anyio==4.9.0
+argcomplete==3.6.2
 async-timeout==5.0.1
 asyncpg==0.30.0
 attrs==25.3.0
 backoff==2.2.1
 bcrypt==4.3.0
+black==25.1.0
 build==1.2.2.post1
 cachetools==5.5.2
 certifi==2025.7.14
@@ -31,6 +34,7 @@ filelock==3.18.0
 flatbuffers==25.2.10
 frozenlist==1.7.0
 fsspec==2025.7.0
+genson==1.3.0
 google-auth==2.40.3
 google-genai==1.25.0
 googleapis-common-protos==1.70.0
@@ -49,7 +53,9 @@ hyperframe==6.1.0
 idna==3.10
 importlib_metadata==8.7.0
 importlib_resources==6.5.2
+inflect==7.5.0
 iniconfig==2.1.0
+isort==6.0.1
 Jinja2==3.1.6
 jiter==0.10.0
 jsonschema==4.25.0
@@ -61,8 +67,10 @@ markdown-it-py==3.0.0
 MarkupSafe==3.0.2
 mdurl==0.1.2
 mmh3==5.1.0
+more-itertools==10.7.0
 mpmath==1.3.0
 multidict==6.6.3
+mypy_extensions==1.1.0
 numpy==2.3.2
 oauthlib==3.3.1
 onnxruntime==1.22.1
@@ -76,10 +84,12 @@ opentelemetry-semantic-conventions==0.56b0
 orjson==3.11.1
 overrides==7.7.0
 packaging==25.0
+pathspec==0.12.1
 pathvalidate==3.3.1
 pdfminer.six==20250506
 pdfplumber==0.11.7
 pillow==11.3.0
+platformdirs==4.3.8
 pluggy==1.6.0
 portalocker==3.2.0
 posthog==5.4.0
@@ -122,7 +132,9 @@ starlette==0.47.1
 sympy==1.14.0
 tenacity==8.5.0
 tokenizers==0.21.2
+tomli==2.2.1
 tqdm==4.67.1
+typeguard==4.4.4
 typer==0.16.0
 typing-inspection==0.4.1
 typing_extensions==4.14.1

servers/fastapi/server.py

@@ -8,14 +8,15 @@ if __name__ == "__main__":
         "--port", type=int, required=True, help="Port number to run the server on"
     )
     parser.add_argument(
-        "--reload", type=bool, default=False, help="Reload the server on code changes"
+        "--reload", type=str, default="false", help="Reload the server on code changes"
     )
     args = parser.parse_args()
+    reload = args.reload == "true"
 
     uvicorn.run(
         "api.main:app",
         host="0.0.0.0",
         port=args.port,
         log_level="info",
-        reload=args.reload,
+        reload=reload,
     )

servers/fastapi/services/image_generation_service.py

@@ -3,12 +3,12 @@ import os
 import aiohttp
 from google import genai
 from google.genai.types import GenerateContentConfig
+from openai import AsyncOpenAI
 from models.image_prompt import ImagePrompt
 from models.sql.image_asset import ImageAsset
 from utils.download_helpers import download_file
 from utils.get_env import get_pexels_api_key_env
 from utils.get_env import get_pixabay_api_key_env
-from utils.llm_provider import get_llm_client
 from utils.image_provider import (
     is_pixels_selected,
     is_pixabay_selected,
@@ -80,7 +80,7 @@ class ImageGenerationService:
             return "/static/images/placeholder.jpg"
 
     async def generate_image_openai(self, prompt: str, output_directory: str) -> str:
-        client = get_llm_client()
+        client = AsyncOpenAI()
         result = await client.images.generate(
             model="dall-e-3",
             prompt=prompt,

servers/fastapi/services/llm_client.py

@@ -0,0 +1,509 @@
+import asyncio
+import json
+from typing import List
+from fastapi import HTTPException
+from openai import AsyncOpenAI
+from google import genai
+from google.genai.types import GenerateContentConfig
+from anthropic import AsyncAnthropic
+from anthropic.types import Message as AnthropicMessage
+from anthropic import MessageStreamEvent as AnthropicMessageStreamEvent
+from enums.llm_provider import LLMProvider
+from models.llm_message import LLMMessage
+from utils.async_iterator import iterator_to_async
+from utils.get_env import (
+    get_anthropic_api_key_env,
+    get_custom_llm_api_key_env,
+    get_custom_llm_url_env,
+    get_google_api_key_env,
+    get_ollama_url_env,
+    get_openai_api_key_env,
+)
+from utils.llm_provider import get_llm_provider
+from utils.schema_utils import ensure_strict_json_schema
+
+
+class LLMClient:
+    def __init__(self, max_tokens: int = 4000):
+        self.llm_provider = get_llm_provider()
+        self._client = self._get_client()
+        self.max_tokens = max_tokens
+
+    # ? Clients
+    def _get_client(self):
+        match self.llm_provider:
+            case LLMProvider.OPENAI:
+                return self._get_openai_client()
+            case LLMProvider.GOOGLE:
+                return self._get_google_client()
+            case LLMProvider.ANTHROPIC:
+                return self._get_anthropic_client()
+            case LLMProvider.OLLAMA:
+                return self._get_ollama_client()
+            case LLMProvider.CUSTOM:
+                return self._get_custom_client()
+            case _:
+                raise HTTPException(
+                    status_code=400,
+                    detail="LLM Provider must be either openai, google, anthropic, ollama, or custom",
+                )
+
+    def _get_openai_client(self):
+        if not get_openai_api_key_env():
+            raise HTTPException(
+                status_code=400,
+                detail="OpenAI API Key is not set",
+            )
+        return AsyncOpenAI()
+
+    def _get_google_client(self):
+        if not get_google_api_key_env():
+            raise HTTPException(
+                status_code=400,
+                detail="Google API Key is not set",
+            )
+        return genai.Client()
+
+    def _get_anthropic_client(self):
+        if not get_anthropic_api_key_env():
+            raise HTTPException(
+                status_code=400,
+                detail="Anthropic API Key is not set",
+            )
+        return AsyncAnthropic()
+
+    def _get_ollama_client(self):
+        return AsyncOpenAI(
+            base_url=(get_ollama_url_env() or "http://localhost:11434") + "/v1",
+            api_key="ollama",
+        )
+
+    def _get_custom_client(self):
+        if not (get_custom_llm_api_key_env() and get_custom_llm_url_env()):
+            raise HTTPException(
+                status_code=400,
+                detail="Custom LLM API Key is not set",
+            )
+        return AsyncOpenAI(
+            base_url=get_custom_llm_url_env(),
+            api_key=get_custom_llm_api_key_env(),
+        )
+
+    # ? Prompts
+    def _get_system_prompt(self, messages: List[LLMMessage]) -> str:
+        for message in messages:
+            if message.role == "system":
+                return message.content
+        return ""
+
+    def _get_user_prompts(self, messages: List[LLMMessage]) -> List[str]:
+        return [message.content for message in messages if message.role == "user"]
+
+    def _get_user_llm_messages(self, messages: List[LLMMessage]) -> List[LLMMessage]:
+        return [message for message in messages if message.role == "user"]
+
+    # ? Generate Unstructured Content
+    async def _generate_openai(self, model: str, messages: List[LLMMessage]):
+        client: AsyncOpenAI = self._client
+        response = await client.chat.completions.create(
+            model=model,
+            messages=[message.model_dump() for message in messages],
+            max_completion_tokens=self.max_tokens,
+        )
+        return response.choices[0].message.content
+
+    async def _generate_google(self, model: str, messages: List[LLMMessage]):
+        client: genai.Client = self._client
+        response = await asyncio.to_thread(
+            client.models.generate_content,
+            model=model,
+            contents=self._get_user_prompts(messages),
+            config=GenerateContentConfig(
+                system_instruction=self._get_system_prompt(messages),
+                response_mime_type="text/plain",
+                max_output_tokens=self.max_tokens,
+            ),
+        )
+        return response.text
+
+    async def _generate_anthropic(self, model: str, messages: List[LLMMessage]):
+        client: AsyncAnthropic = self._client
+        response: AnthropicMessage = await client.messages.create(
+            model=model,
+            system=self._get_system_prompt(messages),
+            messages=[
+                message.model_dump()
+                for message in self._get_user_llm_messages(messages)
+            ],
+            max_tokens=self.max_tokens,
+        )
+        text = ""
+        for content in response.content:
+            if content.type == "text" and isinstance(content.text, str):
+                text += content.text
+        if text == "":
+            return None
+        return text
+
+    async def _generate_ollama(self, model: str, messages: List[LLMMessage]):
+        return await self._generate_openai(model, messages)
+
+    async def _generate_custom(self, model: str, messages: List[LLMMessage]):
+        return await self._generate_openai(model, messages)
+
+    async def generate(self, model: str, messages: List[LLMMessage]):
+        content = None
+        match self.llm_provider:
+            case LLMProvider.OPENAI:
+                content = await self._generate_openai(model, messages)
+            case LLMProvider.GOOGLE:
+                content = await self._generate_google(model, messages)
+            case LLMProvider.ANTHROPIC:
+                content = await self._generate_anthropic(model, messages)
+            case LLMProvider.OLLAMA:
+                content = await self._generate_ollama(model, messages)
+            case LLMProvider.CUSTOM:
+                content = await self._generate_custom(model, messages)
+        if content is None:
+            raise HTTPException(
+                status_code=400,
+                detail="LLM did not return any content",
+            )
+        return content
+
+    # ? Generate Structured Content
+    async def _generate_openai_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+        strict: bool = False,
+    ):
+        client: AsyncOpenAI = self._client
+        response_schema = response_format
+        if strict:
+            response_schema = ensure_strict_json_schema(
+                response_schema,
+                path=(),
+                root=response_schema,
+            )
+        response = await client.chat.completions.create(
+            model=model,
+            messages=[message.model_dump() for message in messages],
+            response_format={
+                "type": "json_schema",
+                "json_schema": (
+                    {
+                        "name": "ResponseSchema",
+                        "strict": strict,
+                        "schema": response_schema,
+                    }
+                ),
+            },
+            max_completion_tokens=self.max_tokens,
+        )
+        content = response.choices[0].message.content
+        if content:
+            return json.loads(content)
+        return None
+
+    async def _generate_google_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+    ):
+        client: genai.Client = self._client
+        response = await asyncio.to_thread(
+            client.models.generate_content,
+            model=model,
+            contents=self._get_user_prompts(messages),
+            config=GenerateContentConfig(
+                system_instruction=self._get_system_prompt(messages),
+                response_mime_type="application/json",
+                response_json_schema=response_format,
+                max_output_tokens=self.max_tokens,
+            ),
+        )
+        content = None
+        if response.text:
+            content = json.loads(response.text)
+
+        return content
+
+    async def _generate_anthropic_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+    ):
+        client: AsyncAnthropic = self._client
+        response: AnthropicMessage = await client.messages.create(
+            model=model,
+            system=self._get_system_prompt(messages),
+            messages=[
+                message.model_dump()
+                for message in self._get_user_llm_messages(messages)
+            ],
+            max_tokens=self.max_tokens,
+            tools=[
+                {
+                    "name": "ResponseSchema",
+                    "description": "A response to the user's message",
+                    "input_schema": response_format,
+                }
+            ],
+        )
+        content: dict | None = None
+        for content_block in response.content:
+            if content_block.type == "tool_use":
+                content = content_block.input
+
+        return content
+
+    async def _generate_ollama_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+        strict: bool = False,
+    ):
+        return await self._generate_openai_structured(
+            model, messages, response_format, strict
+        )
+
+    async def _generate_custom_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+        strict: bool = False,
+    ):
+        return await self._generate_openai_structured(
+            model, messages, response_format, strict
+        )
+
+    async def generate_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+        strict: bool = False,
+    ) -> dict:
+        content = None
+        match self.llm_provider:
+            case LLMProvider.OPENAI:
+                content = await self._generate_openai_structured(
+                    model, messages, response_format, strict
+                )
+            case LLMProvider.GOOGLE:
+                content = await self._generate_google_structured(
+                    model, messages, response_format
+                )
+            case LLMProvider.ANTHROPIC:
+                content = await self._generate_anthropic_structured(
+                    model, messages, response_format
+                )
+            case LLMProvider.OLLAMA:
+                content = await self._generate_ollama_structured(
+                    model, messages, response_format, strict
+                )
+            case LLMProvider.CUSTOM:
+                content = await self._generate_custom_structured(
+                    model, messages, response_format, strict
+                )
+        if content is None:
+            raise HTTPException(
+                status_code=400,
+                detail="LLM did not return any content",
+            )
+        return content
+
+    # ? Stream Unstructured Content
+    async def _stream_openai(self, model: str, messages: List[LLMMessage]):
+        client: AsyncOpenAI = self._client
+        async with client.chat.completions.stream(
+            model=model,
+            messages=[message.model_dump() for message in messages],
+            max_completion_tokens=self.max_tokens,
+        ) as stream:
+            async for event in stream:
+                if event.type == "content.delta":
+                    yield event.delta
+
+    async def _stream_google(self, model: str, messages: List[LLMMessage]):
+        client: genai.Client = self._client
+        async for event in iterator_to_async(client.models.generate_content_stream)(
+            model=model,
+            contents=self._get_user_prompts(messages),
+            config=GenerateContentConfig(
+                system_instruction=self._get_system_prompt(messages),
+                response_mime_type="text/plain",
+                max_output_tokens=self.max_tokens,
+            ),
+        ):
+            if event.text:
+                yield event.text
+
+    async def _stream_anthropic(self, model: str, messages: List[LLMMessage]):
+        client: AsyncAnthropic = self._client
+        async with client.messages.stream(
+            model=model,
+            system=self._get_system_prompt(messages),
+            messages=[
+                message.model_dump()
+                for message in self._get_user_llm_messages(messages)
+            ],
+            max_tokens=self.max_tokens,
+        ) as stream:
+            async for event in stream:
+                event: AnthropicMessageStreamEvent = event
+                if event.type == "text" and isinstance(event.text, str):
+                    yield event.text
+
+    def _stream_ollama(self, model: str, messages: List[LLMMessage]):
+        return self._stream_openai(model, messages)
+
+    def _stream_custom(self, model: str, messages: List[LLMMessage]):
+        return self._stream_openai(model, messages)
+
+    def stream(self, model: str, messages: List[LLMMessage]):
+        match self.llm_provider:
+            case LLMProvider.OPENAI:
+                return self._stream_openai(model, messages)
+            case LLMProvider.GOOGLE:
+                return self._stream_google(model, messages)
+            case LLMProvider.ANTHROPIC:
+                return self._stream_anthropic(model, messages)
+            case LLMProvider.OLLAMA:
+                return self._stream_ollama(model, messages)
+            case LLMProvider.CUSTOM:
+                return self._stream_custom(model, messages)
+
+    # ? Stream Structured Content
+    async def _stream_openai_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+        strict: bool = False,
+    ):
+        client: AsyncOpenAI = self._client
+        response_schema = response_format
+        if strict:
+            response_schema = ensure_strict_json_schema(
+                response_schema,
+                path=(),
+                root=response_schema,
+            )
+        async with client.chat.completions.stream(
+            model=model,
+            messages=[message.model_dump() for message in messages],
+            max_completion_tokens=self.max_tokens,
+            response_format=(
+                {
+                    "type": "json_schema",
+                    "json_schema": {
+                        "name": "ResponseSchema",
+                        "strict": strict,
+                        "schema": response_schema,
+                    },
+                }
+            ),
+        ) as stream:
+            async for event in stream:
+                if event.type == "content.delta":
+                    yield event.delta
+
+    async def _stream_google_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+    ):
+        client: genai.Client = self._client
+        async for event in iterator_to_async(client.models.generate_content_stream)(
+            model=model,
+            contents=self._get_user_prompts(messages),
+            config=GenerateContentConfig(
+                system_instruction=self._get_system_prompt(messages),
+                response_mime_type="application/json",
+                response_json_schema=response_format,
+                max_output_tokens=self.max_tokens,
+            ),
+        ):
+            if event.text:
+                yield event.text
+
+    async def _stream_anthropic_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+    ):
+        client: AsyncAnthropic = self._client
+        async with client.messages.stream(
+            model=model,
+            system=self._get_system_prompt(messages),
+            messages=[
+                message.model_dump()
+                for message in self._get_user_llm_messages(messages)
+            ],
+            max_tokens=self.max_tokens,
+            tools=[
+                {
+                    "name": "ResponseSchema",
+                    "description": "A response to the user's message",
+                    "input_schema": response_format,
+                }
+            ],
+        ) as stream:
+            async for event in stream:
+                event: AnthropicMessageStreamEvent = event
+                if event.type == "input_json" and isinstance(event.partial_json, str):
+                    yield event.partial_json
+
+    def _stream_ollama_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+        strict: bool = False,
+    ):
+        return self._stream_openai_structured(model, messages, response_format, strict)
+
+    def _stream_custom_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+        strict: bool = False,
+    ):
+        return self._stream_openai_structured(model, messages, response_format, strict)
+
+    def stream_structured(
+        self,
+        model: str,
+        messages: List[LLMMessage],
+        response_format: dict,
+        strict: bool = False,
+    ):
+        match self.llm_provider:
+            case LLMProvider.OPENAI:
+                return self._stream_openai_structured(
+                    model, messages, response_format, strict
+                )
+            case LLMProvider.GOOGLE:
+                return self._stream_google_structured(model, messages, response_format)
+            case LLMProvider.ANTHROPIC:
+                return self._stream_anthropic_structured(
+                    model, messages, response_format
+                )
+            case LLMProvider.OLLAMA:
+                return self._stream_ollama_structured(
+                    model, messages, response_format, strict
+                )
+            case LLMProvider.CUSTOM:
+                return self._stream_custom_structured(
+                    model, messages, response_format, strict
+                )

servers/fastapi/services/temp_file_service.py

@@ -9,8 +9,7 @@ class TempFileService:
 
     def __init__(self):
         self.base_dir = get_temp_directory_env() or "/tmp/presenton"
-        # TODO: Uncomment this when we want to cleanup the base dir on startup
-        # self.cleanup_base_dir()
+        self.cleanup_base_dir()
         os.makedirs(self.base_dir, exist_ok=True)
 
     def create_dir_in_dir(self, base_dir: str, dir_name: Optional[str] = None) -> str:

servers/fastapi/utils/available_models.py

@@ -0,0 +1,21 @@
+from anthropic import AsyncAnthropic
+from openai import AsyncOpenAI
+from google import genai
+
+
+async def list_available_openai_compatible_models(url: str, api_key: str) -> list[str]:
+    client = AsyncOpenAI(api_key=api_key, base_url=url)
+    models = (await client.models.list()).data
+    if models:
+        return list(map(lambda x: x.id, models))
+    return []
+
+
+async def list_available_anthropic_models(api_key: str) -> list[str]:
+    client = AsyncAnthropic(api_key=api_key)
+    return list(map(lambda x: x.id, (await client.models.list(limit=50)).data))
+
+
+async def list_available_google_models(api_key: str) -> list[str]:
+    client = genai.Client(api_key=api_key)
+    return list(map(lambda x: x.name, client.models.list(config={"page_size": 50})))

servers/fastapi/utils/custom_llm_provider.py

@@ -1,18 +0,0 @@
-from typing import Optional
-from openai import AsyncOpenAI
-
-from utils.llm_provider import get_llm_client
-
-
-async def list_available_custom_models(
-    url: Optional[str] = None, api_key: Optional[str] = None
-) -> list[str]:
-    if not url:
-        client = get_llm_client()
-    else:
-        client = AsyncOpenAI(api_key=api_key or "null", base_url=url)
-    models = []
-    async for model in client.models.list():
-        print(model)
-        models.append(model.id)
-    return models

servers/fastapi/utils/dict_utils.py

@@ -78,3 +78,12 @@ def deep_update(original: dict, updates: dict) -> dict:
             if not isinstance(value, (dict, list)):
                 original[key] = value
     return original
+
+
+def has_more_than_n_keys(obj: dict[str, object], n: int) -> bool:
+    i = 0
+    for _ in obj.keys():
+        i += 1
+        if i > n:
+            return True
+    return False

servers/fastapi/utils/get_dynamic_models.py

@@ -22,12 +22,6 @@ def get_presentation_outline_model_with_n_slides(n_slides: int):
             min_length=10,
             max_length=50,
         )
-        notes: Optional[List[str]] = Field(
-            default=None,
-            description="Important notes for the presentation styling and formatting",
-            min_length=0,
-            max_length=10,
-        )
         slides: List[SlideOutlineModelWithValidation] = Field(
             description="List of slides", min_items=n_slides, max_items=n_slides
         )

servers/fastapi/utils/get_env.py

@@ -25,6 +25,14 @@ def get_llm_provider_env():
     return os.getenv("LLM")
 
 
+def get_anthropic_api_key_env():
+    return os.getenv("ANTHROPIC_API_KEY")
+
+
+def get_anthropic_model_env():
+    return os.getenv("ANTHROPIC_MODEL")
+
+
 def get_ollama_url_env():
     return os.getenv("OLLAMA_URL")
 
@@ -37,10 +45,18 @@ def get_openai_api_key_env():
     return os.getenv("OPENAI_API_KEY")
 
 
+def get_openai_model_env():
+    return os.getenv("OPENAI_MODEL")
+
+
 def get_google_api_key_env():
     return os.getenv("GOOGLE_API_KEY")
 
 
+def get_google_model_env():
+    return os.getenv("GOOGLE_MODEL")
+
+
 def get_custom_llm_api_key_env():
     return os.getenv("CUSTOM_LLM_API_KEY")
 
@@ -79,3 +95,7 @@ def get_redis_db_env():
 
 def get_redis_password_env():
     return os.getenv("REDIS_PASSWORD")
+
+
+def get_extended_reasoning_env():
+    return os.getenv("EXTENDED_REASONING")

servers/fastapi/utils/llm_calls/edit_slide.py

@@ -1,15 +1,8 @@
-import asyncio
-import json
-
+from models.llm_message import LLMMessage
 from models.presentation_layout import SlideLayoutModel
 from models.sql.slide import SlideModel
-from google.genai.types import GenerateContentConfig
-from utils.llm_provider import (
-    get_google_llm_client,
-    get_large_model,
-    get_llm_client,
-    is_google_selected,
-)
+from services.llm_client import LLMClient
+from utils.llm_provider import get_model
 from utils.schema_utils import remove_fields_from_schema
 
 system_prompt = """
@@ -42,64 +35,40 @@ def get_user_prompt(prompt: str, slide_data: dict, language: str):
     """
 
 
-def get_prompt_to_edit_slide_content(
+def get_messages(
     prompt: str,
     slide_data: dict,
     language: str,
 ):
     return [
-        {
-            "role": "system",
-            "content": system_prompt,
-        },
-        {
-            "role": "user",
-            "content": get_user_prompt(prompt, slide_data, language),
-        },
+        LLMMessage(
+            role="system",
+            content=system_prompt,
+        ),
+        LLMMessage(
+            role="user",
+            content=get_user_prompt(prompt, slide_data, language),
+        ),
     ]
 
 
 async def get_edited_slide_content(
     prompt: str,
-    slide_layout: SlideLayoutModel,
     slide: SlideModel,
     language: str,
+    slide_layout: SlideLayoutModel,
 ):
-    model = get_large_model()
+    model = get_model()
+
     response_schema = remove_fields_from_schema(
         slide_layout.json_schema, ["__image_url__", "__icon_url__"]
     )
 
-    if is_google_selected():
-        client = get_google_llm_client()
-        response = await asyncio.to_thread(
-            client.models.generate_content,
-            model=model,
-            contents=[get_user_prompt(prompt, slide.content, language)],
-            config=GenerateContentConfig(
-                system_instruction=system_prompt,
-                response_mime_type="application/json",
-                response_json_schema=response_schema,
-            ),
-        )
-        slide_content_json = json.loads(response.text)
-    else:
-        client = get_llm_client()
-        response = await client.beta.chat.completions.parse(
-            model=model,
-            messages=get_prompt_to_edit_slide_content(
-                prompt,
-                slide.content,
-                language,
-            ),
-            response_format={
-                "type": "json_schema",
-                "json_schema": {
-                    "name": "slide_content",
-                    "schema": response_schema,
-                },
-            },
-        )
-        slide_content_json = json.loads(response.choices[0].message.content)
-
-    return slide_content_json
+    client = LLMClient()
+    response = await client.generate_structured(
+        model=model,
+        messages=get_messages(prompt, slide.content, language),
+        response_format=response_schema,
+        strict=False,
+    )
+    return response

servers/fastapi/utils/llm_calls/edit_slide_html.py

@@ -1,12 +1,7 @@
-import asyncio
 from typing import Optional
-from google.genai.types import GenerateContentConfig
-from utils.llm_provider import (
-    get_google_llm_client,
-    get_large_model,
-    is_google_selected,
-    get_llm_client,
-)
+from models.llm_message import LLMMessage
+from services.llm_client import LLMClient
+from utils.llm_provider import get_model
 
 system_prompt = """
     You are an expert HTML slide editor. Your task is to modify slide HTML content based on user prompts while maintaining proper structure, styling, and functionality.
@@ -52,35 +47,17 @@ def get_user_prompt(prompt: str, html: str):
 
 
 async def get_edited_slide_html(prompt: str, html: str):
-    model = get_large_model()
-    llm_response = None
-    if is_google_selected():
-        client = get_google_llm_client()
-        response = await asyncio.to_thread(
-            client.models.generate_content,
-            model=model,
-            contents=[get_user_prompt(prompt, html)],
-            config=GenerateContentConfig(
-                system_instruction=system_prompt,
-                response_mime_type="text/plain",
-            ),
-        )
-        llm_response = response.text
-    else:
-        client = get_llm_client()
-        response = await client.chat.completions.create(
-            model=model,
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": get_user_prompt(prompt, html)},
-            ],
-        )
-        llm_response = response.choices[0].message.content
+    model = get_model()
 
-    if not llm_response:
-        return html
-
-    return extract_html_from_response(llm_response) or html
+    client = LLMClient()
+    response = await client.generate(
+        model=model,
+        messages=[
+            LLMMessage(role="system", content=system_prompt),
+            LLMMessage(role="user", content=get_user_prompt(prompt, html)),
+        ],
+    )
+    return extract_html_from_response(response) or html
 
 
 def extract_html_from_response(response_text: str) -> Optional[str]:

servers/fastapi/utils/llm_calls/generate_document_summary.py

@@ -1,8 +1,9 @@
 import asyncio
 from typing import List
-from openai.types.chat.chat_completion import ChatCompletion
 
-from utils.llm_provider import get_llm_client, get_nano_model
+from models.llm_message import LLMMessage
+from services.llm_client import LLMClient
+from utils.llm_provider import get_model
 
 
 sysmte_prompt = """
@@ -23,23 +24,21 @@ Maintain as much information as possible.
 
 
 async def generate_document_summary(documents: List[str]):
-    client = get_llm_client()
-    model = get_nano_model()
+    client = LLMClient()
+    model = get_model()
 
     coroutines = []
     for document in documents:
         truncated_text = document[:200000]
-        coroutine = client.chat.completions.create(
+        coroutine = client.generate(
             model=model,
             messages=[
-                {"role": "system", "content": sysmte_prompt},
-                {"role": "user", "content": truncated_text},
+                LLMMessage(role="system", content=sysmte_prompt),
+                LLMMessage(role="user", content=truncated_text),
             ],
         )
         coroutines.append(coroutine)
 
-    completions: List[ChatCompletion] = await asyncio.gather(*coroutines)
-    combined = "\n\n\n\n".join(
-        [completion.choices[0].message.content for completion in completions]
-    )
+    completions: List[str] = await asyncio.gather(*coroutines)
+    combined = "\n\n\n\n".join(completions)
     return combined

servers/fastapi/utils/llm_calls/generate_presentation_outlines.py

@@ -1,16 +1,10 @@
+import asyncio
 from typing import Optional
-from google.genai.types import GenerateContentConfig
-from openai.types.chat.chat_completion_chunk import ChoiceDelta
 
-from utils.async_iterator import iterator_to_async
+from models.llm_message import LLMMessage
+from services.llm_client import LLMClient
 from utils.get_dynamic_models import get_presentation_outline_model_with_n_slides
-from utils.llm_provider import (
-    get_google_llm_client,
-    get_large_model,
-    get_llm_client,
-    is_google_selected,
-)
-from pydantic import BaseModel
+from utils.llm_provider import get_model
 
 system_prompt = """
 You are an expert presentation creator. Generate structured presentations based on user requirements and format them according to the specified JSON schema with markdown content.
@@ -62,61 +56,34 @@ def get_user_prompt(prompt: str, n_slides: int, language: str, content: str):
     """
 
 
-def get_prompt_template(prompt: str, n_slides: int, language: str, content: str):
+def get_messages(prompt: str, n_slides: int, language: str, content: str):
     return [
-        {
-            "role": "system",
-            "content": system_prompt,
-        },
-        {
-            "role": "user",
-            "content": get_user_prompt(prompt, n_slides, language, content),
-        },
+        LLMMessage(
+            role="system",
+            content=system_prompt,
+        ),
+        LLMMessage(
+            role="user",
+            content=get_user_prompt(prompt, n_slides, language, content),
+        ),
     ]
 
 
-def get_response_format(response_model: BaseModel):
-    return {
-        "type": "json_schema",
-        "json_schema": {
-            "name": "PresentationOutlineModel",
-            "schema": response_model.model_json_schema(),
-        },
-    }
-
-
 async def generate_ppt_outline(
     prompt: Optional[str],
     n_slides: int,
     language: Optional[str] = None,
     content: Optional[str] = None,
 ):
-    model = get_large_model()
+    model = get_model()
     response_model = get_presentation_outline_model_with_n_slides(n_slides)
 
-    if not is_google_selected():
-        client = get_llm_client()
-        async for response in await client.chat.completions.create(
-            model=model,
-            messages=get_prompt_template(prompt, n_slides, language, content),
-            stream=True,
-            response_format=get_response_format(response_model),
-        ):
-            delta: ChoiceDelta = response.choices[0].delta
-            if delta.content:
-                yield delta.content
+    client = LLMClient()
 
-    else:
-        client = get_google_llm_client()
-        generate_stream = iterator_to_async(client.models.generate_content_stream)
-        async for event in generate_stream(
-            model=model,
-            contents=[get_user_prompt(prompt, n_slides, language, content)],
-            config=GenerateContentConfig(
-                system_instruction=system_prompt,
-                response_mime_type="application/json",
-                response_json_schema=response_model.model_json_schema(),
-            ),
-        ):
-            if event.text:
-                yield event.text
+    async for chunk in client.stream_structured(
+        model,
+        get_messages(prompt, n_slides, language, content),
+        response_model.model_json_schema(),
+        strict=True,
+    ):
+        yield chunk

servers/fastapi/utils/llm_calls/generate_presentation_structure.py

@@ -1,24 +1,19 @@
+from models.llm_message import LLMMessage
 from models.presentation_layout import PresentationLayoutModel
 from models.presentation_outline_model import PresentationOutlineModel
-from utils.llm_provider import (
-    get_large_model,
-    get_llm_client,
-    get_nano_model,
-    get_small_model,
-)
-from utils.get_dynamic_models import (
-    get_presentation_structure_model_with_n_slides,
-)
-from models.presentation_structure_model import (
-    PresentationStructureModel,
-)
+from services.llm_client import LLMClient
+from utils.llm_provider import get_model
+from utils.get_dynamic_models import get_presentation_structure_model_with_n_slides
+from models.presentation_structure_model import PresentationStructureModel
 
 
-def get_prompt(presentation_layout: PresentationLayoutModel, n_slides: int, data: str):
+def get_messages(
+    presentation_layout: PresentationLayoutModel, n_slides: int, data: str
+):
     return [
-        {
-            "role": "system",
-            "content": f"""
+        LLMMessage(
+            role="system",
+            content=f"""
                 You're a professional presentation designer with creative freedom to design engaging presentations.
 
                 {presentation_layout.to_string()}
@@ -51,13 +46,13 @@ def get_prompt(presentation_layout: PresentationLayoutModel, n_slides: int, data
 
                 Select layout index for each of the {n_slides} slides based on what will best serve the presentation's goals.
             """,
-        },
-        {
-            "role": "user",
-            "content": f"""
+        ),
+        LLMMessage(
+            role="user",
+            content=f"""
                 {data}
             """,
-        },
+        ),
     ]
 
 
@@ -66,20 +61,20 @@ async def generate_presentation_structure(
     presentation_layout: PresentationLayoutModel,
 ) -> PresentationStructureModel:
 
-    client = get_llm_client()
-    model = get_large_model()
+    client = LLMClient()
+    model = get_model()
     response_model = get_presentation_structure_model_with_n_slides(
         len(presentation_outline.slides)
     )
 
-    response = await client.beta.chat.completions.parse(
+    response = await client.generate_structured(
         model=model,
-        messages=get_prompt(
+        messages=get_messages(
             presentation_layout,
             len(presentation_outline.slides),
             presentation_outline.to_string(),
         ),
-        response_format=response_model,
+        response_format=response_model.model_json_schema(),
+        strict=True,
     )
-    print(response.choices[0].message.parsed)
-    return response.choices[0].message.parsed
+    return PresentationStructureModel(**response)

servers/fastapi/utils/llm_calls/generate_slide_content.py

@@ -1,14 +1,8 @@
-import asyncio
-import json
-from google.genai.types import GenerateContentConfig
+from models.llm_message import LLMMessage
 from models.presentation_layout import SlideLayoutModel
 from models.presentation_outline_model import SlideOutlineModel
-from utils.llm_provider import (
-    get_google_llm_client,
-    get_large_model,
-    get_llm_client,
-    is_google_selected,
-)
+from services.llm_client import LLMClient
+from utils.llm_provider import get_model
 from utils.schema_utils import remove_fields_from_schema
 
 system_prompt = """
@@ -45,57 +39,38 @@ def get_user_prompt(title: str, outline: str, language: str):
     """
 
 
-def get_prompt_to_generate_slide_content(title: str, outline: str, language: str):
+def get_messages(title: str, outline: str, language: str):
 
     return [
-        {
-            "role": "system",
-            "content": system_prompt,
-        },
-        {
-            "role": "user",
-            "content": get_user_prompt(title, outline, language),
-        },
+        LLMMessage(
+            role="system",
+            content=system_prompt,
+        ),
+        LLMMessage(
+            role="user",
+            content=get_user_prompt(title, outline, language),
+        ),
     ]
 
 
 async def get_slide_content_from_type_and_outline(
     slide_layout: SlideLayoutModel, outline: SlideOutlineModel, language: str
 ):
-    model = get_large_model()
+    client = LLMClient()
+    model = get_model()
 
     response_schema = remove_fields_from_schema(
         slide_layout.json_schema, ["__image_url__", "__icon_url__"]
     )
 
-    if not is_google_selected():
-        client = get_llm_client()
-        response = await client.beta.chat.completions.parse(
-            model=model,
-            messages=get_prompt_to_generate_slide_content(
-                outline.title,
-                outline.body,
-                language,
-            ),
-            response_format={
-                "type": "json_schema",
-                "json_schema": {
-                    "name": "SlideContent",
-                    "schema": response_schema,
-                },
-            },
-        )
-        return json.loads(response.choices[0].message.content)
-    else:
-        client = get_google_llm_client()
-        response = await asyncio.to_thread(
-            client.models.generate_content,
-            model=model,
-            contents=[get_user_prompt(outline.title, outline.body, language)],
-            config=GenerateContentConfig(
-                system_instruction=system_prompt,
-                response_mime_type="application/json",
-                response_json_schema=response_schema,
-            ),
-        )
-        return json.loads(response.text)
+    response = await client.generate_structured(
+        model=model,
+        messages=get_messages(
+            outline.title,
+            outline.body,
+            language,
+        ),
+        response_format=response_schema,
+        strict=False,
+    )
+    return response

servers/fastapi/utils/llm_calls/select_slide_type_on_edit.py

@@ -1,19 +1,21 @@
+from models.llm_message import LLMMessage
 from models.presentation_layout import PresentationLayoutModel, SlideLayoutModel
 from models.slide_layout_index import SlideLayoutIndex
 from models.sql.slide import SlideModel
-from utils.llm_provider import get_large_model, get_llm_client
+from services.llm_client import LLMClient
+from utils.llm_provider import get_model
 
 
-def get_prompt_to_select_slide_layout(
+def get_messages(
     prompt: str,
     slide_data: dict,
     layout: PresentationLayoutModel,
     current_slide_layout: int,
 ):
     return [
-        {
-            "role": "system",
-            "content": f"""
+        LLMMessage(
+            role="system",
+            content=f"""
                 Select a Slide Layout index based on provided user prompt and current slide data.
                 {layout.to_string()}
 
@@ -23,15 +25,15 @@ def get_prompt_to_select_slide_layout(
                 - If user prompt is not clear, select the layout that is most relevant to the slide data.
                 **Go through all notes and steps and make sure they are followed, including mentioned constraints**
             """,
-        },
-        {
-            "role": "user",
-            "content": f"""
+        ),
+        LLMMessage(
+            role="user",
+            content=f"""
                 - User Prompt: {prompt}
                 - Current Slide Data: {slide_data}
                 - Current Slide Layout: {current_slide_layout}
             """,
-        },
+        ),
     ]
 
 
@@ -41,21 +43,21 @@ async def get_slide_layout_from_prompt(
     slide: SlideModel,
 ) -> SlideLayoutModel:
 
-    client = get_llm_client()
-    model = get_large_model()
+    client = LLMClient()
+    model = get_model()
 
-    slide_layout_ids = list(map(lambda x: x.id, layout.slides))
+    slide_layout_index = layout.get_slide_layout_index(slide.layout)
 
-    response = await client.beta.chat.completions.parse(
+    response = await client.generate_structured(
         model=model,
-        temperature=0.2,
-        messages=get_prompt_to_select_slide_layout(
+        messages=get_messages(
             prompt,
             slide.content,
             layout,
-            slide_layout_ids.index(slide.layout),
+            slide_layout_index,
         ),
-        response_format=SlideLayoutIndex,
+        response_format=SlideLayoutIndex.model_json_schema(),
+        strict=True,
     )
-    index = response.choices[0].message.parsed.index
+    index = SlideLayoutIndex(**response).index
     return layout.slides[index]

servers/fastapi/utils/llm_provider.py

@@ -1,36 +1,31 @@
-import os
 from fastapi import HTTPException
-from openai import AsyncOpenAI
-from google import genai
 
+from constants.llm import (
+    DEFAULT_ANTHROPIC_MODEL,
+    DEFAULT_GOOGLE_MODEL,
+    DEFAULT_OPENAI_MODEL,
+)
 from enums.llm_provider import LLMProvider
 from utils.get_env import (
-    get_custom_llm_api_key_env,
-    get_custom_llm_url_env,
+    get_anthropic_model_env,
     get_custom_model_env,
-    get_google_api_key_env,
+    get_google_model_env,
     get_llm_provider_env,
     get_ollama_model_env,
-    get_ollama_url_env,
-    get_openai_api_key_env,
+    get_openai_model_env,
 )
 
 
-
 def get_llm_provider():
     try:
         return LLMProvider(get_llm_provider_env())
     except:
         raise HTTPException(
             status_code=500,
-            detail=f"Invalid LLM provider. Please select one of: openai, google, ollama, custom",
+            detail=f"Invalid LLM provider. Please select one of: openai, google, anthropic, ollama, custom",
         )
 
 
-def get_ollama_url():
-    return get_ollama_url_env() or "http://localhost:11434"
-
-
 def is_openai_selected():
     return get_llm_provider() == LLMProvider.OPENAI
 
@@ -39,6 +34,10 @@ def is_google_selected():
     return get_llm_provider() == LLMProvider.GOOGLE
 
 
+def is_anthropic_selected():
+    return get_llm_provider() == LLMProvider.ANTHROPIC
+
+
 def is_ollama_selected():
     return get_llm_provider() == LLMProvider.OLLAMA
 
@@ -47,85 +46,20 @@ def is_custom_llm_selected():
     return get_llm_provider() == LLMProvider.CUSTOM
 
 
-def get_model_base_url():
-    selected_llm = get_llm_provider()
-
-    if selected_llm == LLMProvider.OPENAI:
-        return "https://api.openai.com/v1"
-    elif selected_llm == LLMProvider.GOOGLE:
-        return "https://generativelanguage.googleapis.com/v1beta/openai"
-    elif selected_llm == LLMProvider.OLLAMA:
-        return os.path.join(get_ollama_url(), "v1")
-    elif selected_llm == LLMProvider.CUSTOM:
-        return get_custom_llm_url_env()
-    else:
-        raise HTTPException(f"LLM provider {selected_llm} is not supported")
-
-
-def get_llm_api_key():
+def get_model():
     selected_llm = get_llm_provider()
     if selected_llm == LLMProvider.OPENAI:
-        return get_openai_api_key_env()
+        return get_openai_model_env() or DEFAULT_OPENAI_MODEL
     elif selected_llm == LLMProvider.GOOGLE:
-        return get_google_api_key_env()
+        return get_google_model_env() or DEFAULT_GOOGLE_MODEL
+    elif selected_llm == LLMProvider.ANTHROPIC:
+        return get_anthropic_model_env() or DEFAULT_ANTHROPIC_MODEL
     elif selected_llm == LLMProvider.OLLAMA:
-        return "ollama"
+        return get_ollama_model_env()
     elif selected_llm == LLMProvider.CUSTOM:
-        return get_custom_llm_api_key_env() or "none"
+        return get_custom_model_env()
     else:
-        raise HTTPException(f"LLM provider {selected_llm} is not supported")
-
-
-def get_llm_client():
-    client = AsyncOpenAI(
-            base_url=get_model_base_url(),
-            api_key=get_llm_api_key(),
+        raise HTTPException(
+            status_code=500,
+            detail=f"Invalid LLM provider. Please select one of: openai, google, anthropic, ollama, custom",
         )
-    return client
-
-
-def get_google_llm_client():
-    client = genai.Client(api_key=get_llm_api_key())
-    return client
-
-
-def get_large_model():
-    selected_llm = get_llm_provider()
-    if selected_llm == LLMProvider.OPENAI:
-        return "gpt-4.1"
-    elif selected_llm == LLMProvider.GOOGLE:
-        return "gemini-2.0-flash"
-    elif selected_llm == LLMProvider.OLLAMA:
-        return get_ollama_model_env()
-    elif selected_llm == LLMProvider.CUSTOM:
-        return get_custom_model_env()
-    else:
-        raise ValueError(f"Invalid LLM model")
-
-
-def get_small_model():
-    selected_llm = get_llm_provider()
-    if selected_llm == LLMProvider.OPENAI:
-        return "gpt-4.1-mini"
-    elif selected_llm == LLMProvider.GOOGLE:
-        return "gemini-2.0-flash"
-    elif selected_llm == LLMProvider.OLLAMA:
-        return get_ollama_model_env()
-    elif selected_llm == LLMProvider.CUSTOM:
-        return get_custom_model_env()
-    else:
-        raise ValueError(f"Invalid LLM model")
-
-
-def get_nano_model():
-    selected_llm = get_llm_provider()
-    if selected_llm == LLMProvider.OPENAI:
-        return "gpt-4.1-nano"
-    elif selected_llm == LLMProvider.GOOGLE:
-        return "gemini-2.0-flash"
-    elif selected_llm == LLMProvider.OLLAMA:
-        return get_ollama_model_env()
-    elif selected_llm == LLMProvider.CUSTOM:
-        return get_custom_model_env()
-    else:
-        raise ValueError(f"Invalid LLM model")

servers/fastapi/utils/model_availability.py

@@ -1,11 +1,19 @@
-import os
 from constants.supported_ollama_models import SUPPORTED_OLLAMA_MODELS
+from constants.llm import OPENAI_URL
 from enums.image_provider import ImageProvider
 from enums.llm_provider import LLMProvider
-from utils.custom_llm_provider import list_available_custom_models
+from utils.available_models import (
+    list_available_anthropic_models,
+    list_available_google_models,
+    list_available_openai_compatible_models,
+)
 from utils.get_env import (
+    get_anthropic_api_key_env,
+    get_anthropic_model_env,
     get_can_change_keys_env,
+    get_google_model_env,
     get_openai_api_key_env,
+    get_openai_model_env,
     get_pixabay_api_key_env,
     get_pexels_api_key_env,
 )
@@ -20,13 +28,7 @@ from utils.llm_provider import (
     is_ollama_selected,
 )
 from utils.ollama import pull_ollama_model
-from utils.image_provider import (
-    get_selected_image_provider,
-    is_pixels_selected,
-    is_pixabay_selected,
-    is_gemini_flash_selected,
-    is_dalle3_selected,
-)
+from utils.image_provider import get_selected_image_provider
 
 
 async def check_llm_and_image_provider_api_or_model_availability():
@@ -36,11 +38,41 @@ async def check_llm_and_image_provider_api_or_model_availability():
             openai_api_key = get_openai_api_key_env()
             if not openai_api_key:
                 raise Exception("OPENAI_API_KEY must be provided")
+            openai_model = get_openai_model_env()
+            if openai_model:
+                available_models = await list_available_openai_compatible_models(
+                    OPENAI_URL, openai_api_key
+                )
+                if openai_model not in available_models:
+                    print("-" * 50)
+                    print("Available models: ", available_models)
+                    raise Exception(f"Model {openai_model} is not available")
 
         elif get_llm_provider() == LLMProvider.GOOGLE:
             google_api_key = get_google_api_key_env()
             if not google_api_key:
                 raise Exception("GOOGLE_API_KEY must be provided")
+            google_model = get_google_model_env()
+            if google_model:
+                available_models = await list_available_google_models(google_api_key)
+                if google_model not in available_models:
+                    print("-" * 50)
+                    print("Available models: ", available_models)
+                    raise Exception(f"Model {google_model} is not available")
+
+        elif get_llm_provider() == LLMProvider.ANTHROPIC:
+            anthropic_api_key = get_anthropic_api_key_env()
+            if not anthropic_api_key:
+                raise Exception("ANTHROPIC_API_KEY must be provided")
+            anthropic_model = get_anthropic_model_env()
+            if anthropic_model:
+                available_models = await list_available_anthropic_models(
+                    anthropic_api_key
+                )
+                if anthropic_model not in available_models:
+                    print("-" * 50)
+                    print("Available models: ", available_models)
+                    raise Exception(f"Model {anthropic_model} is not available")
 
         elif is_ollama_selected():
             ollama_model = get_ollama_model_env()
@@ -67,14 +99,12 @@ async def check_llm_and_image_provider_api_or_model_availability():
                 raise Exception("CUSTOM_LLM_URL must be provided")
             if not custom_llm_api_key:
                 raise Exception("CUSTOM_LLM_API_KEY must be provided")
-            print("-" * 50)
-            print("Selecting model: ", custom_model)
-            models = await list_available_custom_models(
+            available_models = await list_available_openai_compatible_models(
                 custom_llm_url, custom_llm_api_key
             )
-            print("Available models: ", models)
             print("-" * 50)
-            if custom_model not in models:
+            print("Available models: ", available_models)
+            if custom_model not in available_models:
                 raise Exception(f"Model {custom_model} is not available")
 
         # Check for Image Provider and API keys

servers/fastapi/utils/ollama.py

@@ -1,7 +1,7 @@
-from http.client import HTTPException
 import json
 from typing import AsyncGenerator
 import aiohttp
+from fastapi import HTTPException
 
 from models.ollama_model_status import OllamaModelStatus
 from utils.get_env import get_ollama_url_env

servers/fastapi/utils/process_slides.py

@@ -21,19 +21,19 @@ async def process_slide_and_fetch_assets(
     icon_paths = get_dict_paths_with_key(slide.content, "__icon_query__")
 
     for image_path in image_paths:
-        image_prompt_parent = get_dict_at_path(slide.content, image_path)
+        __image_prompt__parent = get_dict_at_path(slide.content, image_path)
         async_tasks.append(
             image_generation_service.generate_image(
                 ImagePrompt(
-                    prompt=image_prompt_parent["__image_prompt__"],
+                    prompt=__image_prompt__parent["__image_prompt__"],
                 )
             )
         )
 
     for icon_path in icon_paths:
-        icon_query_parent = get_dict_at_path(slide.content, icon_path)
+        __icon_query__parent = get_dict_at_path(slide.content, icon_path)
         async_tasks.append(
-            icon_finder_service.search_icons(icon_query_parent["__icon_query__"])
+            icon_finder_service.search_icons(__icon_query__parent["__icon_query__"])
         )
 
     results = await asyncio.gather(*async_tasks)

servers/fastapi/utils/schema_utils.py

@@ -1,30 +1,25 @@
 from copy import deepcopy
-from typing import List
+from typing import Any, List
 
-from utils.dict_utils import get_dict_paths_with_key, get_dict_at_path
+from openai import NOT_GIVEN
 
+from utils.dict_utils import (
+    get_dict_paths_with_key,
+    get_dict_at_path,
+    has_more_than_n_keys,
+)
 
-def resolve_refs(schema, defs):
-    if isinstance(schema, dict):
-        if "$ref" in schema:
-            ref_path = schema["$ref"]
-            if ref_path.startswith("#/$defs/"):
-                def_key = ref_path.replace("#/$defs/", "")
-                return resolve_refs(defs[def_key], defs)
-            else:
-                raise ValueError(f"Unsupported $ref path: {ref_path}")
-        else:
-            return {k: resolve_refs(v, defs) for k, v in schema.items()}
-    elif isinstance(schema, list):
-        return [resolve_refs(item, defs) for item in schema]
-    else:
-        return schema
-
-
-def flatten_schema(schema):
-    schema = deepcopy(schema)
-    defs = schema.pop("$defs", {})
-    return resolve_refs(schema, defs)
+supported_string_formats = [
+    "date-time",
+    "time",
+    "date",
+    "duration",
+    "email",
+    "hostname",
+    "ipv4",
+    "ipv6",
+    "uuid",
+]
 
 
 def remove_fields_from_schema(schema: dict, fields_to_remove: List[str]):
@@ -50,6 +45,138 @@ def remove_fields_from_schema(schema: dict, fields_to_remove: List[str]):
     return schema
 
 
+# From OpenAI
+def ensure_strict_json_schema(
+    json_schema: object,
+    *,
+    path: tuple[str, ...],
+    root: dict[str, object],
+) -> dict[str, Any]:
+    """Mutates the given JSON schema to ensure it conforms to the `strict` standard
+    that the API expects.
+    """
+    if not isinstance(json_schema, dict):
+        raise TypeError(f"Expected {json_schema} to be a dictionary; path={path}")
+
+    defs = json_schema.get("$defs")
+    if isinstance(defs, dict):
+        for def_name, def_schema in defs.items():
+            ensure_strict_json_schema(
+                def_schema, path=(*path, "$defs", def_name), root=root
+            )
+
+    definitions = json_schema.get("definitions")
+    if isinstance(definitions, dict):
+        for definition_name, definition_schema in definitions.items():
+            ensure_strict_json_schema(
+                definition_schema,
+                path=(*path, "definitions", definition_name),
+                root=root,
+            )
+
+    typ = json_schema.get("type")
+    if typ == "object" and "additionalProperties" not in json_schema:
+        json_schema["additionalProperties"] = False
+
+    # object types
+    # { 'type': 'object', 'properties': { 'a':  {...} } }
+    properties = json_schema.get("properties")
+    if isinstance(properties, dict):
+        json_schema["required"] = [prop for prop in properties.keys()]
+        json_schema["properties"] = {
+            key: ensure_strict_json_schema(
+                prop_schema, path=(*path, "properties", key), root=root
+            )
+            for key, prop_schema in properties.items()
+        }
+
+    # arrays
+    # { 'type': 'array', 'items': {...} }
+    items = json_schema.get("items")
+    if isinstance(items, dict):
+        json_schema["items"] = ensure_strict_json_schema(
+            items, path=(*path, "items"), root=root
+        )
+
+    # unions
+    any_of = json_schema.get("anyOf")
+    if isinstance(any_of, list):
+        json_schema["anyOf"] = [
+            ensure_strict_json_schema(variant, path=(*path, "anyOf", str(i)), root=root)
+            for i, variant in enumerate(any_of)
+        ]
+
+    # intersections
+    all_of = json_schema.get("allOf")
+    if isinstance(all_of, list):
+        if len(all_of) == 1:
+            json_schema.update(
+                ensure_strict_json_schema(
+                    all_of[0], path=(*path, "allOf", "0"), root=root
+                )
+            )
+            json_schema.pop("allOf")
+        else:
+            json_schema["allOf"] = [
+                ensure_strict_json_schema(
+                    entry, path=(*path, "allOf", str(i)), root=root
+                )
+                for i, entry in enumerate(all_of)
+            ]
+
+    # string
+    if typ == "string":
+        if "format" in json_schema:
+            if json_schema["format"] not in supported_string_formats:
+                del json_schema["format"]
+
+    # strip `None` defaults as there's no meaningful distinction here
+    # the schema will still be `nullable` and the model will default
+    # to using `None` anyway
+    if json_schema.get("default", NOT_GIVEN) is None:
+        json_schema.pop("default")
+
+    # we can't use `$ref`s if there are also other properties defined, e.g.
+    # `{"$ref": "...", "description": "my description"}`
+    #
+    # so we unravel the ref
+    # `{"type": "string", "description": "my description"}`
+    ref = json_schema.get("$ref")
+    if ref and has_more_than_n_keys(json_schema, 1):
+        assert isinstance(ref, str), f"Received non-string $ref - {ref}"
+
+        resolved = resolve_ref(root=root, ref=ref)
+        if not isinstance(resolved, dict):
+            raise ValueError(
+                f"Expected `$ref: {ref}` to resolved to a dictionary but got {resolved}"
+            )
+
+        # properties from the json schema take priority over the ones on the `$ref`
+        json_schema.update({**resolved, **json_schema})
+        json_schema.pop("$ref")
+        # Since the schema expanded from `$ref` might not have `additionalProperties: false` applied,
+        # we call `_ensure_strict_json_schema` again to fix the inlined schema and ensure it's valid.
+        return ensure_strict_json_schema(json_schema, path=path, root=root)
+
+    return json_schema
+
+
+def resolve_ref(*, root: dict[str, object], ref: str) -> object:
+    if not ref.startswith("#/"):
+        raise ValueError(f"Unexpected $ref format {ref!r}; Does not start with #/")
+
+    path = ref[2:].split("/")
+    resolved = root
+    for key in path:
+        value = resolved[key]
+        assert isinstance(
+            value, dict
+        ), f"encountered non-dictionary entry while resolving {ref} - {resolved}"
+        resolved = value
+
+    return resolved
+
+
 # ? Not used
 def generate_constraint_sentences(schema: dict) -> str:
     """

servers/fastapi/utils/set_env.py

@@ -25,10 +25,26 @@ def set_openai_api_key_env(value):
     os.environ["OPENAI_API_KEY"] = value
 
 
+def set_openai_model_env(value):
+    os.environ["OPENAI_MODEL"] = value
+
+
 def set_google_api_key_env(value):
     os.environ["GOOGLE_API_KEY"] = value
 
 
+def set_google_model_env(value):
+    os.environ["GOOGLE_MODEL"] = value
+
+
+def set_anthropic_api_key_env(value):
+    os.environ["ANTHROPIC_API_KEY"] = value
+
+
+def set_anthropic_model_env(value):
+    os.environ["ANTHROPIC_MODEL"] = value
+
+
 def set_custom_llm_api_key_env(value):
     os.environ["CUSTOM_LLM_API_KEY"] = value
 
@@ -51,3 +67,7 @@ def set_image_provider_env(value):
 
 def set_pixabay_api_key_env(value):
     os.environ["PIXABAY_API_KEY"] = value
+
+
+def set_extended_reasoning_env(value):
+    os.environ["EXTENDED_REASONING"] = value

servers/fastapi/utils/user_config.py

@@ -3,31 +3,41 @@ import json
 
 from models.user_config import UserConfig
 from utils.get_env import (
+    get_anthropic_api_key_env,
+    get_anthropic_model_env,
     get_custom_llm_api_key_env,
     get_custom_llm_url_env,
     get_custom_model_env,
     get_google_api_key_env,
+    get_google_model_env,
     get_llm_provider_env,
     get_ollama_model_env,
     get_ollama_url_env,
     get_openai_api_key_env,
+    get_openai_model_env,
     get_pexels_api_key_env,
     get_user_config_path_env,
     get_image_provider_env,
-    get_pixabay_api_key_env
+    get_pixabay_api_key_env,
+    get_extended_reasoning_env,
 )
 from utils.set_env import (
+    set_anthropic_api_key_env,
+    set_anthropic_model_env,
     set_custom_llm_api_key_env,
     set_custom_llm_url_env,
     set_custom_model_env,
+    set_extended_reasoning_env,
     set_google_api_key_env,
+    set_google_model_env,
     set_llm_provider_env,
     set_ollama_model_env,
     set_ollama_url_env,
     set_openai_api_key_env,
+    set_openai_model_env,
     set_pexels_api_key_env,
     set_image_provider_env,
-    set_pixabay_api_key_env
+    set_pixabay_api_key_env,
 )
 
 
@@ -43,10 +53,21 @@ def get_user_config():
         print("Error while loading user config")
         pass
 
+    new_extended_reasoning = (
+        existing_config.EXTENDED_REASONING or get_extended_reasoning_env()
+    )
+    if new_extended_reasoning is not None:
+        new_extended_reasoning = bool(new_extended_reasoning)
+
     return UserConfig(
         LLM=existing_config.LLM or get_llm_provider_env(),
         OPENAI_API_KEY=existing_config.OPENAI_API_KEY or get_openai_api_key_env(),
+        OPENAI_MODEL=existing_config.OPENAI_MODEL or get_openai_model_env(),
         GOOGLE_API_KEY=existing_config.GOOGLE_API_KEY or get_google_api_key_env(),
+        GOOGLE_MODEL=existing_config.GOOGLE_MODEL or get_google_model_env(),
+        ANTHROPIC_API_KEY=existing_config.ANTHROPIC_API_KEY
+        or get_anthropic_api_key_env(),
+        ANTHROPIC_MODEL=existing_config.ANTHROPIC_MODEL or get_anthropic_model_env(),
         OLLAMA_URL=existing_config.OLLAMA_URL or get_ollama_url_env(),
         OLLAMA_MODEL=existing_config.OLLAMA_MODEL or get_ollama_model_env(),
         CUSTOM_LLM_URL=existing_config.CUSTOM_LLM_URL or get_custom_llm_url_env(),
@@ -56,6 +77,7 @@ def get_user_config():
         IMAGE_PROVIDER=existing_config.IMAGE_PROVIDER or get_image_provider_env(),
         PIXABAY_API_KEY=existing_config.PIXABAY_API_KEY or get_pixabay_api_key_env(),
         PEXELS_API_KEY=existing_config.PEXELS_API_KEY or get_pexels_api_key_env(),
+        EXTENDED_REASONING=new_extended_reasoning,
     )
 
 
@@ -65,8 +87,16 @@ def update_env_with_user_config():
         set_llm_provider_env(user_config.LLM)
     if user_config.OPENAI_API_KEY:
         set_openai_api_key_env(user_config.OPENAI_API_KEY)
+    if user_config.OPENAI_MODEL:
+        set_openai_model_env(user_config.OPENAI_MODEL)
     if user_config.GOOGLE_API_KEY:
         set_google_api_key_env(user_config.GOOGLE_API_KEY)
+    if user_config.GOOGLE_MODEL:
+        set_google_model_env(user_config.GOOGLE_MODEL)
+    if user_config.ANTHROPIC_API_KEY:
+        set_anthropic_api_key_env(user_config.ANTHROPIC_API_KEY)
+    if user_config.ANTHROPIC_MODEL:
+        set_anthropic_model_env(user_config.ANTHROPIC_MODEL)
     if user_config.OLLAMA_URL:
         set_ollama_url_env(user_config.OLLAMA_URL)
     if user_config.OLLAMA_MODEL:
@@ -83,3 +113,6 @@ def update_env_with_user_config():
         set_pixabay_api_key_env(user_config.PIXABAY_API_KEY)
     if user_config.PEXELS_API_KEY:
         set_pexels_api_key_env(user_config.PEXELS_API_KEY)
+    if user_config.EXTENDED_REASONING:
+        if user_config.EXTENDED_REASONING:
+            set_extended_reasoning_env(str(user_config.EXTENDED_REASONING))

servers/nextjs/app/api/user-config/route.ts

@@ -35,7 +35,11 @@ export async function POST(request: Request) {
   const mergedConfig: LLMConfig = {
     LLM: userConfig.LLM || existingConfig.LLM,
     OPENAI_API_KEY: userConfig.OPENAI_API_KEY || existingConfig.OPENAI_API_KEY,
+    OPENAI_MODEL: userConfig.OPENAI_MODEL || existingConfig.OPENAI_MODEL,
     GOOGLE_API_KEY: userConfig.GOOGLE_API_KEY || existingConfig.GOOGLE_API_KEY,
+    GOOGLE_MODEL: userConfig.GOOGLE_MODEL || existingConfig.GOOGLE_MODEL,
+    ANTHROPIC_API_KEY: userConfig.ANTHROPIC_API_KEY || existingConfig.ANTHROPIC_API_KEY,
+    ANTHROPIC_MODEL: userConfig.ANTHROPIC_MODEL || existingConfig.ANTHROPIC_MODEL,
     OLLAMA_URL: userConfig.OLLAMA_URL || existingConfig.OLLAMA_URL,
     OLLAMA_MODEL: userConfig.OLLAMA_MODEL || existingConfig.OLLAMA_MODEL,
     CUSTOM_LLM_URL: userConfig.CUSTOM_LLM_URL || existingConfig.CUSTOM_LLM_URL,
@@ -50,6 +54,10 @@ export async function POST(request: Request) {
       userConfig.USE_CUSTOM_URL === undefined
         ? existingConfig.USE_CUSTOM_URL
         : userConfig.USE_CUSTOM_URL,
+    EXTENDED_REASONING:
+      userConfig.EXTENDED_REASONING === undefined
+        ? existingConfig.EXTENDED_REASONING
+        : userConfig.EXTENDED_REASONING,
   };
   fs.writeFileSync(userConfigPath, JSON.stringify(mergedConfig));
   return NextResponse.json(mergedConfig);

servers/nextjs/app/settings/SettingPage.tsx

@@ -29,7 +29,6 @@ const SettingsPage = () => {
   const userConfigState = useSelector((state: RootState) => state.userConfig);
   const [llmConfig, setLlmConfig] = useState<LLMConfig>(userConfigState.llm_config);
   const canChangeKeys = userConfigState.can_change_keys;
-  const [isLoading, setIsLoading] = useState<boolean>(false);
   const [buttonState, setButtonState] = useState<ButtonState>({
     isLoading: false,
     isDisabled: false,
@@ -55,16 +54,13 @@ const SettingsPage = () => {
 
   const handleSaveConfig = async () => {
     try {
-      setIsLoading(true);
       setButtonState(prev => ({
         ...prev,
         isLoading: true,
         isDisabled: true,
         text: "Saving Configuration..."
       }));
-
       await handleSaveLLMConfig(llmConfig);
-
       if (llmConfig.LLM === "ollama" && llmConfig.OLLAMA_MODEL) {
         const isPulled = await checkIfSelectedOllamaModelIsPulled(llmConfig.OLLAMA_MODEL);
         if (!isPulled) {
@@ -72,24 +68,16 @@ const SettingsPage = () => {
           await handleModelDownload();
         }
       }
-
       toast.info("Configuration saved successfully");
-      setIsLoading(false);
       setButtonState(prev => ({
         ...prev,
         isLoading: false,
         isDisabled: false,
         text: "Save Configuration"
       }));
-      router.back();
+      router.push("/upload");
     } catch (error) {
-      console.error("Error:", error);
-      toast.info(
-        error instanceof Error
-          ? error.message
-          : "Failed to save configuration"
-      );
-      setIsLoading(false);
+      toast.info(error instanceof Error ? error.message : "Failed to save configuration");
       setButtonState(prev => ({
         ...prev,
         isLoading: false,
@@ -102,8 +90,8 @@ const SettingsPage = () => {
   const handleModelDownload = async () => {
     try {
       await pullOllamaModel(llmConfig.OLLAMA_MODEL!, setDownloadingModel);
-    } catch (error) {
-      console.error("Error downloading model:", error);
+    }
+    finally {
       setDownloadingModel(null);
       setShowDownloadModal(false);
     }

servers/nextjs/app/storeInitializer.tsx

@@ -53,7 +53,7 @@ export function StoreInitializer({ children }: { children: React.ReactNode }) {
           }
         }
         if (llmConfig.LLM === 'custom') {
-          const isAvailable = await checkIfSelectedCustomModelIsAvailable(llmConfig.CUSTOM_MODEL);
+          const isAvailable = await checkIfSelectedCustomModelIsAvailable(llmConfig);
           if (!isAvailable) {
             router.push('/');
             setLoadingToFalseAfterNavigatingTo('/');
@@ -83,16 +83,20 @@ export function StoreInitializer({ children }: { children: React.ReactNode }) {
   }
 
 
-  const checkIfSelectedCustomModelIsAvailable = async (customModel: string) => {
+  const checkIfSelectedCustomModelIsAvailable = async (llmConfig: LLMConfig) => {
     try {
-      const response = await fetch('/api/v1/ppt/custom_llm/models/available', {
+      const response = await fetch('/api/v1/ppt/openai/models/available', {
         method: 'POST',
         headers: {
           'Content-Type': 'application/json',
         },
+        body: JSON.stringify({
+          url: llmConfig.CUSTOM_LLM_URL,
+          api_key: llmConfig.CUSTOM_LLM_API_KEY,
+        }),
       });
       const data = await response.json();
-      return data.includes(customModel);
+      return data.includes(llmConfig.CUSTOM_MODEL);
     } catch (error) {
       console.error('Error fetching custom models:', error);
       return false;

servers/nextjs/components/AnthropicConfig.tsx

@@ -0,0 +1,230 @@
+"use client";
+import { useEffect, useState } from "react";
+import { Check, ChevronsUpDown, Loader2 } from "lucide-react";
+import { Button } from "./ui/button";
+import {
+  Command,
+  CommandEmpty,
+  CommandGroup,
+  CommandInput,
+  CommandItem,
+  CommandList,
+} from "./ui/command";
+import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover";
+import { cn } from "@/lib/utils";
+import { toast } from "sonner";
+import { Switch } from "./ui/switch";
+
+interface AnthropicConfigProps {
+  anthropicApiKey: string;
+  anthropicModel: string;
+  extendedReasoning: boolean;
+  onInputChange: (value: string | boolean, field: string) => void;
+}
+
+
+export default function AnthropicConfig({
+  anthropicApiKey,
+  anthropicModel,
+  extendedReasoning,
+  onInputChange,
+}: AnthropicConfigProps) {
+  const [openModelSelect, setOpenModelSelect] = useState(false);
+  const [availableModels, setAvailableModels] = useState<string[]>([]);
+  const [modelsLoading, setModelsLoading] = useState(false);
+  const [modelsChecked, setModelsChecked] = useState(false);
+  const [apiKey, setApiKey] = useState(anthropicApiKey);
+
+  useEffect(() => {
+    setAvailableModels([]);
+    setModelsChecked(false);
+    onInputChange("", "anthropic_model");
+  }, [apiKey]);
+
+  const onApiKeyChange = (value: string) => {
+    setApiKey(value);
+    onInputChange(value, "anthropic_api_key");
+  };
+
+  const fetchAvailableModels = async () => {
+    if (!anthropicApiKey) return;
+
+    setModelsLoading(true);
+    try {
+      const response = await fetch('/api/v1/ppt/anthropic/models/available', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({
+          api_key: anthropicApiKey
+        }),
+      });
+
+      if (response.ok) {
+        const data = await response.json();
+        setAvailableModels(data);
+        setModelsChecked(true);
+      } else {
+        console.error('Failed to fetch models');
+        setAvailableModels([]);
+        setModelsChecked(true);
+      }
+    } catch (error) {
+      console.error('Error fetching models:', error);
+      toast.error('Error fetching models');
+      setAvailableModels([]);
+      setModelsChecked(true);
+    } finally {
+      setModelsLoading(false);
+    }
+  };
+
+  return (
+    <div className="space-y-6">
+      {/* API Key Input */}
+      <div className="mb-4">
+        <label className="block text-sm font-medium text-gray-700 mb-2">
+          Anthropic API Key
+        </label>
+        <div className="relative">
+          <input
+            type="text"
+            value={anthropicApiKey}
+            onChange={(e) => onApiKeyChange(e.target.value)}
+            className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
+            placeholder="Enter your Anthropic API key"
+          />
+        </div>
+        <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
+          <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
+          Your API key will be stored locally and never shared
+        </p>
+      </div>
+
+      {/* Extended Reasoning Toggle */}
+      {/* <div>
+        <div className="flex items-center justify-between mb-4 bg-green-50 p-2 rounded-sm">
+          <label className="text-sm font-medium text-gray-700">
+            Extended Reasoning
+          </label>
+          <Switch
+            checked={extendedReasoning}
+            onCheckedChange={(checked) => onInputChange(checked, "extended_reasoning")}
+          />
+        </div>
+        <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
+          <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
+          Enable extended reasoning for more detailed and thorough responses
+        </p>
+      </div> */}
+
+      {/* Check for available models button - show when no models checked or no models found */}
+      {(!modelsChecked || (modelsChecked && availableModels.length === 0)) && (
+        <div className="mb-4">
+          <button
+            onClick={fetchAvailableModels}
+            disabled={modelsLoading || !anthropicApiKey}
+            className={`w-full py-2.5 px-4 rounded-lg transition-all duration-200 border-2 ${modelsLoading || !anthropicApiKey
+              ? "bg-gray-100 border-gray-300 cursor-not-allowed text-gray-500"
+              : "bg-white border-blue-600 text-blue-600 hover:bg-blue-50 focus:ring-2 focus:ring-blue-500/20"
+              }`}
+          >
+            {modelsLoading ? (
+              <div className="flex items-center justify-center gap-2">
+                <Loader2 className="w-4 h-4 animate-spin" />
+                Checking for models...
+              </div>
+            ) : (
+              "Check for available models"
+            )}
+          </button>
+        </div>
+      )}
+
+      {/* Show message if no models found */}
+      {modelsChecked && availableModels.length === 0 && (
+        <div className="mb-4 p-3 bg-yellow-50 border border-yellow-200 rounded-lg">
+          <p className="text-sm text-yellow-800">
+            No models found. Please make sure your API key is valid and has access to Anthropic models.
+          </p>
+        </div>
+      )}
+
+      {/* Model Selection - only show if models are available */}
+      {modelsChecked && availableModels.length > 0 ? (
+        <div>
+          <label className="block text-sm font-medium text-gray-700 mb-3">
+            Select Anthropic Model
+          </label>
+          <div className="w-full">
+            <Popover
+              open={openModelSelect}
+              onOpenChange={setOpenModelSelect}
+            >
+              <PopoverTrigger asChild>
+                <Button
+                  variant="outline"
+                  role="combobox"
+                  aria-expanded={openModelSelect}
+                  className="w-full h-12 px-4 py-4 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors hover:border-gray-400 justify-between"
+                >
+                  <div className="flex gap-3 items-center">
+                    <span className="text-sm font-medium text-gray-900">
+                      {anthropicModel
+                        ? availableModels.find(model => model === anthropicModel) || anthropicModel
+                        : "Select a model"}
+                    </span>
+                  </div>
+                  <ChevronsUpDown className="w-4 h-4 text-gray-500" />
+                </Button>
+              </PopoverTrigger>
+              <PopoverContent
+                className="p-0"
+                align="start"
+                style={{ width: "var(--radix-popover-trigger-width)" }}
+              >
+                <Command>
+                  <CommandInput placeholder="Search models..." />
+                  <CommandList>
+                    <CommandEmpty>No model found.</CommandEmpty>
+                    <CommandGroup>
+                      {availableModels.map((model, index) => (
+                        <CommandItem
+                          key={index}
+                          value={model}
+                          onSelect={(value) => {
+                            onInputChange(value, "anthropic_model");
+                            setOpenModelSelect(false);
+                          }}
+                        >
+                          <Check
+                            className={cn(
+                              "mr-2 h-4 w-4",
+                              anthropicModel === model
+                                ? "opacity-100"
+                                : "opacity-0"
+                            )}
+                          />
+                          <div className="flex gap-3 items-center">
+                            <div className="flex flex-col space-y-1 flex-1">
+                              <div className="flex items-center justify-between gap-2">
+                                <span className="text-sm font-medium text-gray-900">
+                                  {model}
+                                </span>
+                              </div>
+                            </div>
+                          </div>
+                        </CommandItem>
+                      ))}
+                    </CommandGroup>
+                  </CommandList>
+                </Command>
+              </PopoverContent>
+            </Popover>
+          </div>
+        </div>
+      ) : null}
+    </div>
+  );
+} 

servers/nextjs/components/CustomConfig.tsx

@@ -1,4 +1,5 @@
 "use client";
+import { useState, useEffect } from "react";
 import { Check, ChevronsUpDown, Loader2 } from "lucide-react";
 import { Button } from "./ui/button";
 import {
@@ -11,34 +12,83 @@ import {
 } from "./ui/command";
 import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover";
 import { cn } from "@/lib/utils";
+import { toast } from "sonner";
 
 interface CustomConfigProps {
   customLlmUrl: string;
   customLlmApiKey: string;
   customModel: string;
-  customModels: string[];
-  customModelsLoading: boolean;
-  customModelsChecked: boolean;
-  openModelSelect: boolean;
   onInputChange: (value: string, field: string) => void;
-  onOpenModelSelectChange: (open: boolean) => void;
-  onFetchCustomModels: () => void;
 }
 
 export default function CustomConfig({
   customLlmUrl,
   customLlmApiKey,
   customModel,
-  customModels,
-  customModelsLoading,
-  customModelsChecked,
-  openModelSelect,
   onInputChange,
-  onOpenModelSelectChange,
-  onFetchCustomModels,
 }: CustomConfigProps) {
+  const [customModels, setCustomModels] = useState<string[]>([]);
+  const [customModelsLoading, setCustomModelsLoading] = useState(false);
+  const [customModelsChecked, setCustomModelsChecked] = useState(false);
+  const [openModelSelect, setOpenModelSelect] = useState(false);
+  const [url, setUrl] = useState(customLlmUrl);
+  const [apiKey, setApiKey] = useState(customLlmApiKey);
+
+  useEffect(() => {
+    setCustomModels([]);
+    setCustomModelsChecked(false);
+    onInputChange("", "custom_model");
+  }, [url, apiKey]);
+
+  const onUrlChange = (value: string) => {
+    setUrl(value);
+    onInputChange(value, "custom_llm_url");
+  };
+
+  const onApiKeyChange = (value: string) => {
+    setApiKey(value);
+    onInputChange(value, "custom_llm_api_key");
+  };
+
+  const fetchCustomModels = async () => {
+    if (!customLlmUrl) return;
+
+    try {
+      setCustomModelsLoading(true);
+      const response = await fetch("/api/v1/ppt/openai/models/available", {
+        method: "POST",
+        headers: {
+          "Content-Type": "application/json",
+        },
+        body: JSON.stringify({
+          url: customLlmUrl,
+          api_key: customLlmApiKey,
+        }),
+      });
+
+      if (response.ok) {
+        const data = await response.json();
+        setCustomModels(data);
+        setCustomModelsChecked(true);
+      } else {
+        console.error('Failed to fetch custom models');
+        setCustomModels([]);
+        setCustomModelsChecked(true);
+        toast.error('Failed to fetch custom models');
+      }
+    } catch (error) {
+      console.error('Error fetching custom models:', error);
+      toast.error('Error fetching custom models');
+      setCustomModels([]);
+      setCustomModelsChecked(true);
+    } finally {
+      setCustomModelsLoading(false);
+    }
+  };
+
   return (
-    <>
+    <div className="space-y-6">
+      {/* URL Input */}
       <div className="mb-4">
         <label className="block text-sm font-medium text-gray-700 mb-2">
           OpenAI Compatible URL
@@ -50,12 +100,12 @@ export default function CustomConfig({
             placeholder="Enter your URL"
             className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
             value={customLlmUrl}
-            onChange={(e) =>
-              onInputChange(e.target.value, "custom_llm_url")
-            }
+            onChange={(e) => onUrlChange(e.target.value)}
           />
         </div>
       </div>
+
+      {/* API Key Input */}
       <div className="mb-4">
         <label className="block text-sm font-medium text-gray-700 mb-2">
           OpenAI Compatible API Key
@@ -67,13 +117,43 @@ export default function CustomConfig({
             placeholder="Enter your API Key"
             className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
             value={customLlmApiKey}
-            onChange={(e) =>
-              onInputChange(e.target.value, "custom_llm_api_key")
-            }
+            onChange={(e) => onApiKeyChange(e.target.value)}
           />
         </div>
       </div>
 
+      {/* Check for available models button - show when no models checked or no models found */}
+      {(!customModelsChecked || (customModelsChecked && customModels.length === 0)) && (
+        <div className="mb-4">
+          <button
+            onClick={fetchCustomModels}
+            disabled={customModelsLoading || !customLlmUrl}
+            className={`w-full py-2.5 px-4 rounded-lg transition-all duration-200 border-2 ${customModelsLoading || !customLlmUrl
+              ? "bg-gray-100 border-gray-300 cursor-not-allowed text-gray-500"
+              : "bg-white border-blue-600 text-blue-600 hover:bg-blue-50 focus:ring-2 focus:ring-blue-500/20"
+              }`}
+          >
+            {customModelsLoading ? (
+              <div className="flex items-center justify-center gap-2">
+                <Loader2 className="w-4 h-4 animate-spin" />
+                Checking for models...
+              </div>
+            ) : (
+              "Check for available models"
+            )}
+          </button>
+        </div>
+      )}
+
+      {/* Show message if no models found */}
+      {customModelsChecked && customModels.length === 0 && (
+        <div className="mb-4 p-3 bg-yellow-50 border border-yellow-200 rounded-lg">
+          <p className="text-sm text-yellow-800">
+            No models found. Please make sure your API key is valid and has access to models.
+          </p>
+        </div>
+      )}
+
       {/* Model selection dropdown - only show if models are available */}
       {customModelsChecked && customModels.length > 0 && (
         <div className="mb-4">
@@ -90,7 +170,7 @@ export default function CustomConfig({
           <div className="w-full">
             <Popover
               open={openModelSelect}
-              onOpenChange={onOpenModelSelectChange}
+              onOpenChange={setOpenModelSelect}
             >
               <PopoverTrigger asChild>
                 <Button
@@ -121,7 +201,7 @@ export default function CustomConfig({
                           value={model}
                           onSelect={(value) => {
                             onInputChange(value, "custom_model");
-                            onOpenModelSelectChange(false);
+                            setOpenModelSelect(false);
                           }}
                         >
                           <Check
@@ -145,39 +225,6 @@ export default function CustomConfig({
           </div>
         </div>
       )}
-
-      {/* Check for available models button - show when no models checked or no models found */}
-      {(!customModelsChecked ||
-        (customModelsChecked && customModels.length === 0)) && (
-          <div className="mb-4">
-            <button
-              onClick={onFetchCustomModels}
-              disabled={customModelsLoading || !customLlmUrl}
-              className={`w-full py-2.5 px-4 rounded-lg transition-all duration-200 border-2 ${customModelsLoading || !customLlmUrl
-                ? "bg-gray-100 border-gray-300 cursor-not-allowed text-gray-500"
-                : "bg-white border-blue-600 text-blue-600 hover:bg-blue-50 focus:ring-2 focus:ring-blue-500/20"
-                }`}
-            >
-              {customModelsLoading ? (
-                <div className="flex items-center justify-center gap-2">
-                  <Loader2 className="w-4 h-4 animate-spin" />
-                  Checking for models...
-                </div>
-              ) : (
-                "Check for available models"
-              )}
-            </button>
-          </div>
-        )}
-
-      {/* Show message if no models found */}
-      {customModelsChecked && customModels.length === 0 && (
-        <div className="mb-4 p-3 bg-yellow-50 border border-yellow-200 rounded-lg">
-          <p className="text-sm text-yellow-800">
-            No models found. Please make sure models are available.
-          </p>
-        </div>
-      )}
-    </>
+    </div>
   );
 } 

servers/nextjs/components/GoogleConfig.tsx

@@ -1,27 +1,209 @@
+"use client";
+import { useEffect, useState } from "react";
+import { Check, ChevronsUpDown, Loader2 } from "lucide-react";
+import { Button } from "./ui/button";
+import {
+  Command,
+  CommandEmpty,
+  CommandGroup,
+  CommandInput,
+  CommandItem,
+  CommandList,
+} from "./ui/command";
+import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover";
+import { cn } from "@/lib/utils";
+import { toast } from "sonner";
+
 interface GoogleConfigProps {
   googleApiKey: string;
+  googleModel: string;
   onInputChange: (value: string, field: string) => void;
 }
 
-export default function GoogleConfig({ googleApiKey, onInputChange }: GoogleConfigProps) {
+export default function GoogleConfig({
+  googleApiKey,
+  googleModel,
+  onInputChange
+}: GoogleConfigProps) {
+  const [openModelSelect, setOpenModelSelect] = useState(false);
+  const [availableModels, setAvailableModels] = useState<string[]>([]);
+  const [modelsLoading, setModelsLoading] = useState(false);
+  const [modelsChecked, setModelsChecked] = useState(false);
+  const [apiKey, setApiKey] = useState(googleApiKey);
+
+  useEffect(() => {
+    setAvailableModels([]);
+    setModelsChecked(false);
+    onInputChange("", "google_model");
+  }, [apiKey]);
+
+  const onApiKeyChange = (value: string) => {
+    setApiKey(value);
+    onInputChange(value, "google_api_key");
+  };
+
+  const fetchAvailableModels = async () => {
+    if (!googleApiKey) return;
+
+    setModelsLoading(true);
+    try {
+      const response = await fetch('/api/v1/ppt/google/models/available', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({
+          api_key: googleApiKey
+        }),
+      });
+
+      if (response.ok) {
+        const data = await response.json();
+        setAvailableModels(data);
+        setModelsChecked(true);
+      } else {
+        console.error('Failed to fetch models');
+        setAvailableModels([]);
+        setModelsChecked(true);
+      }
+    } catch (error) {
+      console.error('Error fetching models:', error);
+      toast.error('Error fetching models');
+      setAvailableModels([]);
+      setModelsChecked(true);
+    } finally {
+      setModelsLoading(false);
+    }
+  };
+
   return (
-    <div className="mb-8">
-      <label className="block text-sm font-medium text-gray-700 mb-2">
-        Google API Key
-      </label>
-      <div className="relative">
-        <input
-          type="text"
-          value={googleApiKey}
-          onChange={(e) => onInputChange(e.target.value, "google_api_key")}
-          className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
-          placeholder="Enter your API key"
-        />
+    <div className="space-y-6">
+      {/* API Key Input */}
+      <div className="mb-4">
+        <label className="block text-sm font-medium text-gray-700 mb-2">
+          Google API Key
+        </label>
+        <div className="relative">
+          <input
+            type="text"
+            value={googleApiKey}
+            onChange={(e) => onApiKeyChange(e.target.value)}
+            className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
+            placeholder="Enter your API key"
+          />
+        </div>
+        <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
+          <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
+          Your API key will be stored locally and never shared
+        </p>
       </div>
-      <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
-        <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
-        Your API key will be stored locally and never shared
-      </p>
+
+      {/* Check for available models button - show when no models checked or no models found */}
+      {(!modelsChecked || (modelsChecked && availableModels.length === 0)) && (
+        <div className="mb-4">
+          <button
+            onClick={fetchAvailableModels}
+            disabled={modelsLoading || !googleApiKey}
+            className={`w-full py-2.5 px-4 rounded-lg transition-all duration-200 border-2 ${modelsLoading || !googleApiKey
+              ? "bg-gray-100 border-gray-300 cursor-not-allowed text-gray-500"
+              : "bg-white border-blue-600 text-blue-600 hover:bg-blue-50 focus:ring-2 focus:ring-blue-500/20"
+              }`}
+          >
+            {modelsLoading ? (
+              <div className="flex items-center justify-center gap-2">
+                <Loader2 className="w-4 h-4 animate-spin" />
+                Checking for models...
+              </div>
+            ) : (
+              "Check for available models"
+            )}
+          </button>
+        </div>
+      )}
+
+      {/* Show message if no models found */}
+      {modelsChecked && availableModels.length === 0 && (
+        <div className="mb-4 p-3 bg-yellow-50 border border-yellow-200 rounded-lg">
+          <p className="text-sm text-yellow-800">
+            No models found. Please make sure your API key is valid and has access to Google models.
+          </p>
+        </div>
+      )}
+
+      {/* Model Selection - only show if models are available */}
+      {modelsChecked && availableModels.length > 0 ? (
+        <div>
+          <label className="block text-sm font-medium text-gray-700 mb-3">
+            Select Google Model
+          </label>
+          <div className="w-full">
+            <Popover
+              open={openModelSelect}
+              onOpenChange={setOpenModelSelect}
+            >
+              <PopoverTrigger asChild>
+                <Button
+                  variant="outline"
+                  role="combobox"
+                  aria-expanded={openModelSelect}
+                  className="w-full h-12 px-4 py-4 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors hover:border-gray-400 justify-between"
+                >
+                  <div className="flex gap-3 items-center">
+                    <span className="text-sm font-medium text-gray-900">
+                      {googleModel
+                        ? availableModels.find(model => model === googleModel) || googleModel
+                        : "Select a model"}
+                    </span>
+                  </div>
+                  <ChevronsUpDown className="w-4 h-4 text-gray-500" />
+                </Button>
+              </PopoverTrigger>
+              <PopoverContent
+                className="p-0"
+                align="start"
+                style={{ width: "var(--radix-popover-trigger-width)" }}
+              >
+                <Command>
+                  <CommandInput placeholder="Search models..." />
+                  <CommandList>
+                    <CommandEmpty>No model found.</CommandEmpty>
+                    <CommandGroup>
+                      {availableModels.map((model, index) => (
+                        <CommandItem
+                          key={index}
+                          value={model}
+                          onSelect={(value) => {
+                            onInputChange(value, "google_model");
+                            setOpenModelSelect(false);
+                          }}
+                        >
+                          <Check
+                            className={cn(
+                              "mr-2 h-4 w-4",
+                              googleModel === model
+                                ? "opacity-100"
+                                : "opacity-0"
+                            )}
+                          />
+                          <div className="flex gap-3 items-center">
+                            <div className="flex flex-col space-y-1 flex-1">
+                              <div className="flex items-center justify-between gap-2">
+                                <span className="text-sm font-medium text-gray-900">
+                                  {model}
+                                </span>
+                              </div>
+                            </div>
+                          </div>
+                        </CommandItem>
+                      ))}
+                    </CommandGroup>
+                  </CommandList>
+                </Command>
+              </PopoverContent>
+            </Popover>
+          </div>
+        </div>
+      ) : null}
     </div>
   );
 } 

servers/nextjs/components/Home.tsx

@@ -27,7 +27,6 @@ export default function Home() {
   const router = useRouter();
   const config = useSelector((state: RootState) => state.userConfig);
   const [llmConfig, setLlmConfig] = useState<LLMConfig>(config.llm_config);
-  const [isLoading, setIsLoading] = useState<boolean>(false);
 
   const [downloadingModel, setDownloadingModel] = useState<{
     name: string;
@@ -54,7 +53,6 @@ export default function Home() {
 
   const handleSaveConfig = async () => {
     try {
-      setIsLoading(true);
       setButtonState(prev => ({
         ...prev,
         isLoading: true,
@@ -70,7 +68,6 @@ export default function Home() {
         }
       }
       toast.info("Configuration saved successfully");
-      setIsLoading(false);
       setButtonState(prev => ({
         ...prev,
         isLoading: false,
@@ -79,8 +76,7 @@ export default function Home() {
       }));
       router.push("/upload");
     } catch (error) {
-      toast.info("Failed to save configuration");
-      setIsLoading(false);
+      toast.info(error instanceof Error ? error.message : "Failed to save configuration");
       setButtonState(prev => ({
         ...prev,
         isLoading: false,
@@ -93,8 +89,8 @@ export default function Home() {
   const handleModelDownload = async () => {
     try {
       await pullOllamaModel(llmConfig.OLLAMA_MODEL!, setDownloadingModel);
-    } catch (error) {
-      console.info("Error downloading model:", error);
+    }
+    finally {
       setDownloadingModel(null);
       setShowDownloadModal(false);
     }

servers/nextjs/components/LLMSelection.tsx

@@ -15,16 +15,13 @@ import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover";
 import { cn } from "@/lib/utils";
 import OpenAIConfig from "./OpenAIConfig";
 import GoogleConfig from "./GoogleConfig";
+import AnthropicConfig from "./AnthropicConfig";
 import OllamaConfig from "./OllamaConfig";
 import CustomConfig from "./CustomConfig";
 import {
-  OllamaModel,
   LLMConfig,
   updateLLMConfig,
   changeProvider as changeProviderUtil,
-  fetchOllamaModelsWithConfig,
-  setOllamaConfig,
-  fetchCustomModels,
 } from "@/utils/providerUtils";
 import { IMAGE_PROVIDERS, LLM_PROVIDERS } from "@/utils/providerConstants";
 
@@ -51,29 +48,34 @@ export default function LLMProviderSelection({
   setButtonState,
 }: LLMProviderSelectionProps) {
   const [llmConfig, setLlmConfig] = useState<LLMConfig>(initialLLMConfig);
-  const [ollamaModels, setOllamaModels] = useState<OllamaModel[]>([]);
-  const [customModels, setCustomModels] = useState<string[]>([]);
-  const [customModelsLoading, setCustomModelsLoading] = useState<boolean>(false);
-  const [customModelsChecked, setCustomModelsChecked] = useState<boolean>(false);
-  const [ollamaModelsLoading, setOllamaModelsLoading] = useState<boolean>(false);
-  const [useCustomOllamaUrl, setUseCustomOllamaUrl] = useState<boolean>(
-    initialLLMConfig.USE_CUSTOM_URL || false
-  );
-  const [openModelSelect, setOpenModelSelect] = useState(false);
   const [openImageProviderSelect, setOpenImageProviderSelect] = useState(false);
 
+  useEffect(() => {
+    if (!llmConfig.USE_CUSTOM_URL) {
+      setLlmConfig({ ...llmConfig, OLLAMA_URL: "http://localhost:11434" });
+    } else {
+      if (!llmConfig.OLLAMA_URL) {
+        setLlmConfig({ ...llmConfig, OLLAMA_URL: "http://localhost:11434" });
+      }
+    }
+  }, [llmConfig.USE_CUSTOM_URL]);
+
   useEffect(() => {
     onConfigChange(llmConfig);
   }, [llmConfig]);
 
   useEffect(() => {
     const needsModelSelection =
+      (llmConfig.LLM === "openai" && !llmConfig.OPENAI_MODEL) ||
+      (llmConfig.LLM === "google" && !llmConfig.GOOGLE_MODEL) ||
       (llmConfig.LLM === "ollama" && !llmConfig.OLLAMA_MODEL) ||
-      (llmConfig.LLM === "custom" && !llmConfig.CUSTOM_MODEL);
+      (llmConfig.LLM === "custom" && !llmConfig.CUSTOM_MODEL) ||
+      (llmConfig.LLM === "anthropic" && !llmConfig.ANTHROPIC_MODEL);
 
     const needsApiKey =
       ((llmConfig.IMAGE_PROVIDER === "dall-e-3" || llmConfig.LLM === "openai") && !llmConfig.OPENAI_API_KEY) ||
       ((llmConfig.IMAGE_PROVIDER === "gemini_flash" || llmConfig.LLM === "google") && !llmConfig.GOOGLE_API_KEY) ||
+      (llmConfig.LLM === "anthropic" && !llmConfig.ANTHROPIC_API_KEY) ||
       (llmConfig.IMAGE_PROVIDER === "pexels" && !llmConfig.PEXELS_API_KEY) ||
       (llmConfig.IMAGE_PROVIDER === "pixabay" && !llmConfig.PIXABAY_API_KEY);
 
@@ -86,7 +88,7 @@ export default function LLMProviderSelection({
 
   }, [llmConfig]);
 
-  const input_field_changed = (new_value: string, field: string) => {
+  const input_field_changed = (new_value: string | boolean, field: string) => {
     const updatedConfig = updateLLMConfig(llmConfig, field, new_value);
     setLlmConfig(updatedConfig);
   };
@@ -94,68 +96,8 @@ export default function LLMProviderSelection({
   const handleProviderChange = (provider: string) => {
     const newConfig = changeProviderUtil(llmConfig, provider);
     setLlmConfig(newConfig);
-    if (provider === "ollama") {
-      fetchOllamaModels();
-    }
   };
 
-  const fetchOllamaModels = async () => {
-    try {
-      setOllamaModelsLoading(true);
-      const result = await fetchOllamaModelsWithConfig(llmConfig);
-      setOllamaModels(result.models);
-      if (result.updatedConfig) {
-        setLlmConfig(result.updatedConfig);
-      }
-    } catch (error) {
-      console.error("Error fetching Ollama models:", error);
-      setOllamaModels([]);
-    } finally {
-      setOllamaModelsLoading(false);
-    }
-  };
-
-  const fetchCustomModelsHandler = async () => {
-    try {
-      setCustomModelsLoading(true);
-      const models = await fetchCustomModels(
-        llmConfig.CUSTOM_LLM_URL || "",
-        llmConfig.CUSTOM_LLM_API_KEY || ""
-      );
-      setCustomModels(models);
-      setCustomModelsChecked(true);
-    } catch (error) {
-      console.error("Error fetching custom models:", error);
-      setCustomModels([]);
-    } finally {
-      setCustomModelsLoading(false);
-    }
-  };
-
-  const setOllamaConfigHandler = () => {
-    const updatedConfig = setOllamaConfig(llmConfig, useCustomOllamaUrl);
-    setLlmConfig(updatedConfig);
-  };
-
-  useEffect(() => {
-    if (llmConfig.LLM === "ollama") {
-      setOllamaConfigHandler();
-      fetchOllamaModels();
-    }
-  }, [llmConfig.LLM]);
-
-  useEffect(() => {
-    setOllamaConfigHandler();
-  }, [useCustomOllamaUrl]);
-
-  useEffect(() => {
-    if (llmConfig.LLM === "custom") {
-      setCustomModels([]);
-      setCustomModelsChecked(false);
-      setLlmConfig({ ...llmConfig, CUSTOM_MODEL: "" });
-    }
-  }, [llmConfig.CUSTOM_LLM_URL, llmConfig.CUSTOM_LLM_API_KEY]);
-
   useEffect(() => {
     if (!llmConfig.IMAGE_PROVIDER) {
       if (llmConfig.LLM === "openai") {
@@ -177,9 +119,10 @@ export default function LLMProviderSelection({
           onValueChange={handleProviderChange}
           className="w-full"
         >
-          <TabsList className="grid w-full grid-cols-4 bg-transparent h-10">
+          <TabsList className="grid w-full grid-cols-5 bg-transparent h-10">
             <TabsTrigger value="openai">OpenAI</TabsTrigger>
             <TabsTrigger value="google">Google</TabsTrigger>
+            <TabsTrigger value="anthropic">Anthropic</TabsTrigger>
             <TabsTrigger value="ollama">Ollama</TabsTrigger>
             <TabsTrigger value="custom">Custom</TabsTrigger>
           </TabsList>
@@ -198,6 +141,7 @@ export default function LLMProviderSelection({
           <TabsContent value="openai" className="mt-6">
             <OpenAIConfig
               openaiApiKey={llmConfig.OPENAI_API_KEY || ""}
+              openaiModel={llmConfig.OPENAI_MODEL || ""}
               onInputChange={input_field_changed}
             />
           </TabsContent>
@@ -206,6 +150,17 @@ export default function LLMProviderSelection({
           <TabsContent value="google" className="mt-6">
             <GoogleConfig
               googleApiKey={llmConfig.GOOGLE_API_KEY || ""}
+              googleModel={llmConfig.GOOGLE_MODEL || ""}
+              onInputChange={input_field_changed}
+            />
+          </TabsContent>
+
+          {/* Anthropic Content */}
+          <TabsContent value="anthropic" className="mt-6">
+            <AnthropicConfig
+              anthropicApiKey={llmConfig.ANTHROPIC_API_KEY || ""}
+              anthropicModel={llmConfig.ANTHROPIC_MODEL || ""}
+              extendedReasoning={llmConfig.EXTENDED_REASONING || false}
               onInputChange={input_field_changed}
             />
           </TabsContent>
@@ -215,16 +170,8 @@ export default function LLMProviderSelection({
             <OllamaConfig
               ollamaModel={llmConfig.OLLAMA_MODEL || ""}
               ollamaUrl={llmConfig.OLLAMA_URL || ""}
-              useCustomUrl={useCustomOllamaUrl}
-              ollamaModels={ollamaModels}
-              ollamaModelsLoading={ollamaModelsLoading}
+              useCustomUrl={llmConfig.USE_CUSTOM_URL || false}
               onInputChange={input_field_changed}
-              onUseCustomUrlChange={setUseCustomOllamaUrl}
-              openModelSelect={openModelSelect}
-              onOpenModelSelectChange={setOpenModelSelect}
-              onModelSelect={(modelName: string) => {
-                input_field_changed(modelName, "ollama_model");
-              }}
             />
           </TabsContent>
 
@@ -234,19 +181,13 @@ export default function LLMProviderSelection({
               customLlmUrl={llmConfig.CUSTOM_LLM_URL || ""}
               customLlmApiKey={llmConfig.CUSTOM_LLM_API_KEY || ""}
               customModel={llmConfig.CUSTOM_MODEL || ""}
-              customModels={customModels}
-              customModelsLoading={customModelsLoading}
-              customModelsChecked={customModelsChecked}
-              openModelSelect={openModelSelect}
               onInputChange={input_field_changed}
-              onOpenModelSelectChange={setOpenModelSelect}
-              onFetchCustomModels={fetchCustomModelsHandler}
             />
           </TabsContent>
         </Tabs>
 
         {/* Image Provider Selection */}
-        <div className="mb-8">
+        <div className="my-8">
           <label className="block text-sm font-medium text-gray-700 mb-3">
             Select Image Provider
           </label>
@@ -388,7 +329,13 @@ export default function LLMProviderSelection({
                   ? llmConfig.OLLAMA_MODEL ?? "xxxxx"
                   : llmConfig.LLM === "custom"
                     ? llmConfig.CUSTOM_MODEL ?? "xxxxx"
-                    : LLM_PROVIDERS[llmConfig.LLM!]?.model_label || "xxxxx"}{" "}
+                    : llmConfig.LLM === "anthropic"
+                      ? llmConfig.ANTHROPIC_MODEL ?? "xxxxx"
+                      : llmConfig.LLM === "google"
+                        ? llmConfig.GOOGLE_MODEL ?? "xxxxx"
+                        : llmConfig.LLM === "openai"
+                          ? llmConfig.OPENAI_MODEL ?? "xxxxx"
+                          : "xxxxx"}{" "}
                 for text generation and{" "}
                 {llmConfig.IMAGE_PROVIDER &&
                   IMAGE_PROVIDERS[llmConfig.IMAGE_PROVIDER]

servers/nextjs/components/OllamaConfig.tsx

@@ -1,5 +1,5 @@
 "use client";
-import { useState } from "react";
+import { useState, useEffect } from "react";
 import { Check, ChevronsUpDown, Loader2 } from "lucide-react";
 import { Button } from "./ui/button";
 import {
@@ -13,6 +13,7 @@ import {
 import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover";
 import { cn } from "@/lib/utils";
 import { Switch } from "./ui/switch";
+import { toast } from "sonner";
 
 interface OllamaModel {
   label: string;
@@ -26,30 +27,84 @@ interface OllamaConfigProps {
   ollamaModel: string;
   ollamaUrl: string;
   useCustomUrl: boolean;
-  ollamaModels: OllamaModel[];
-  ollamaModelsLoading?: boolean;
-  onInputChange: (value: string, field: string) => void;
-  onUseCustomUrlChange: (checked: boolean) => void;
-  openModelSelect: boolean;
-  onOpenModelSelectChange: (open: boolean) => void;
-  onModelSelect?: (modelName: string) => void;
+  onInputChange: (value: string | boolean, field: string) => void;
 }
 
 export default function OllamaConfig({
   ollamaModel,
   ollamaUrl,
   useCustomUrl,
-  ollamaModels,
-  ollamaModelsLoading = false,
   onInputChange,
-  onUseCustomUrlChange,
-  openModelSelect,
-  onOpenModelSelectChange,
-  onModelSelect,
 }: OllamaConfigProps) {
+  const [ollamaModels, setOllamaModels] = useState<OllamaModel[]>([]);
+  const [ollamaModelsLoading, setOllamaModelsLoading] = useState(false);
+  const [openModelSelect, setOpenModelSelect] = useState(false);
+
+  const fetchOllamaModels = async () => {
+    try {
+      setOllamaModelsLoading(true);
+      const response = await fetch('/api/v1/ppt/ollama/models/supported');
+
+      if (response.ok) {
+        const data = await response.json();
+        console.log(data);
+        setOllamaModels(data);
+      } else {
+        console.error('Failed to fetch Ollama models');
+        setOllamaModels([]);
+        toast.error('Failed to fetch Ollama models');
+      }
+    } catch (error) {
+      console.error('Error fetching Ollama models:', error);
+      toast.error('Error fetching Ollama models');
+      setOllamaModels([]);
+    } finally {
+      setOllamaModelsLoading(false);
+    }
+  };
+
+  useEffect(() => {
+    fetchOllamaModels();
+  }, []);
+
   return (
-    <>
-      <div className="mb-8">
+    <div className="space-y-6">
+      {/* URL Configuration */}
+      <div>
+        <div className="flex items-center justify-between mb-4 bg-green-50 p-2 rounded-sm">
+          <label className="text-sm font-medium text-gray-700">
+            Use custom Ollama URL
+          </label>
+          <Switch
+            checked={useCustomUrl}
+            onCheckedChange={(checked) => onInputChange(checked, "use_custom_url")}
+          />
+        </div>
+        {useCustomUrl && (
+          <div className="mb-4">
+            <label className="block text-sm font-medium text-gray-700 mb-2">
+              Ollama URL
+            </label>
+            <div className="relative">
+              <input
+                type="text"
+                required
+                placeholder="Enter your Ollama URL"
+                className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
+                value={ollamaUrl}
+                onChange={(e) => onInputChange(e.target.value, "ollama_url")}
+              />
+            </div>
+            <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
+              <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
+              Change this if you are using a custom Ollama instance
+            </p>
+          </div>
+        )}
+      </div>
+
+      {/* Model Selection */}
+      <div className="mb-4">
         <label className="block text-sm font-medium text-gray-700 mb-3">
           Choose a supported model
         </label>
@@ -64,7 +119,7 @@ export default function OllamaConfig({
           ) : ollamaModels && ollamaModels.length > 0 ? (
             <Popover
               open={openModelSelect}
-              onOpenChange={onOpenModelSelectChange}
+              onOpenChange={setOpenModelSelect}
             >
               <PopoverTrigger asChild>
                 <Button
@@ -122,12 +177,8 @@ export default function OllamaConfig({
                           key={index}
                           value={model.value}
                           onSelect={(value) => {
-                            if (onModelSelect) {
-                              onModelSelect(value);
-                            } else {
-                              onInputChange(value, "ollama_model");
-                            }
-                            onOpenModelSelectChange(false);
+                            onInputChange(value, "ollama_model");
+                            setOpenModelSelect(false);
                           }}
                         >
                           <Check
@@ -185,42 +236,6 @@ export default function OllamaConfig({
           </p>
         )}
       </div>
-      <div className="mb-8">
-        <div className="flex items-center justify-between mb-4 bg-green-50 p-2 rounded-sm">
-          <label className="text-sm font-medium text-gray-700">
-            Use custom Ollama URL
-          </label>
-          <Switch
-            checked={useCustomUrl}
-            onCheckedChange={onUseCustomUrlChange}
-          />
-        </div>
-        {useCustomUrl && (
-          <>
-            <div className="mb-4">
-              <label className="block text-sm font-medium text-gray-700 mb-2">
-                Ollama URL
-              </label>
-              <div className="relative">
-                <input
-                  type="text"
-                  required
-                  placeholder="Enter your Ollama URL"
-                  className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
-                  value={ollamaUrl}
-                  onChange={(e) =>
-                    onInputChange(e.target.value, "ollama_url")
-                  }
-                />
-              </div>
-              <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
-                <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
-                Change this if you are using a custom Ollama instance
-              </p>
-            </div>
-          </>
-        )}
-      </div>
-    </>
+    </div>
   );
 } 

servers/nextjs/components/OpenAIConfig.tsx

@@ -1,27 +1,214 @@
+"use client";
+import { useEffect, useState } from "react";
+import { Check, ChevronsUpDown, Loader2 } from "lucide-react";
+import { Button } from "./ui/button";
+import {
+  Command,
+  CommandEmpty,
+  CommandGroup,
+  CommandInput,
+  CommandItem,
+  CommandList,
+} from "./ui/command";
+import { Popover, PopoverContent, PopoverTrigger } from "./ui/popover";
+import { cn } from "@/lib/utils";
+import { toast } from "sonner";
+
 interface OpenAIConfigProps {
   openaiApiKey: string;
+  openaiModel: string;
   onInputChange: (value: string, field: string) => void;
 }
 
-export default function OpenAIConfig({ openaiApiKey, onInputChange }: OpenAIConfigProps) {
+export default function OpenAIConfig({
+  openaiApiKey,
+  openaiModel,
+  onInputChange
+}: OpenAIConfigProps) {
+  const [openModelSelect, setOpenModelSelect] = useState(false);
+  const [availableModels, setAvailableModels] = useState<string[]>([]);
+  const [modelsLoading, setModelsLoading] = useState(false);
+  const [modelsChecked, setModelsChecked] = useState(false);
+  const [apiKey, setApiKey] = useState(openaiApiKey);
+
+  const openaiUrl = "https://api.openai.com/v1";
+
+  useEffect(() => {
+    setAvailableModels([]);
+    setModelsChecked(false);
+    onInputChange("", "openai_model");
+  }, [apiKey]);
+
+  const onApiKeyChange = (value: string) => {
+    setApiKey(value);
+    onInputChange(value, "openai_api_key");
+  };
+
+  const fetchAvailableModels = async () => {
+    if (!openaiApiKey) return;
+
+    setModelsLoading(true);
+    try {
+      const response = await fetch('/api/v1/ppt/openai/models/available', {
+        method: 'POST',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({
+          url: openaiUrl,
+          api_key: openaiApiKey
+        }),
+      });
+
+      if (response.ok) {
+        const data = await response.json();
+        setAvailableModels(data);
+        setModelsChecked(true);
+      } else {
+        console.error('Failed to fetch models');
+        setAvailableModels([]);
+        setModelsChecked(true);
+      }
+    } catch (error) {
+      console.error('Error fetching models:', error);
+      toast.error('Error fetching models');
+      setAvailableModels([]);
+      setModelsChecked(true);
+    } finally {
+      setModelsLoading(false);
+    }
+  };
+
   return (
-    <div className="mb-8">
-      <label className="block text-sm font-medium text-gray-700 mb-2">
-        OpenAI API Key
-      </label>
-      <div className="relative">
-        <input
-          type="text"
-          value={openaiApiKey}
-          onChange={(e) => onInputChange(e.target.value, "openai_api_key")}
-          className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
-          placeholder="Enter your API key"
-        />
+    <div className="space-y-6">
+      {/* API Key Input */}
+      <div className="mb-4">
+        <label className="block text-sm font-medium text-gray-700 mb-2">
+          OpenAI API Key
+        </label>
+        <div className="relative">
+          <input
+            type="text"
+            value={openaiApiKey}
+            onChange={(e) => onApiKeyChange(e.target.value)}
+            className="w-full px-4 py-2.5 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors"
+            placeholder="Enter your API key"
+          />
+        </div>
+        <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
+          <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
+          Your API key will be stored locally and never shared
+        </p>
       </div>
-      <p className="mt-2 text-sm text-gray-500 flex items-center gap-2">
-        <span className="block w-1 h-1 rounded-full bg-gray-400"></span>
-        Your API key will be stored locally and never shared
-      </p>
+
+
+
+      {/* Check for available models button - show when no models checked or no models found */}
+      {(!modelsChecked || (modelsChecked && availableModels.length === 0)) && (
+        <div className="mb-4">
+          <button
+            onClick={fetchAvailableModels}
+            disabled={modelsLoading || !openaiApiKey}
+            className={`w-full py-2.5 px-4 rounded-lg transition-all duration-200 border-2 ${modelsLoading || !openaiApiKey
+              ? "bg-gray-100 border-gray-300 cursor-not-allowed text-gray-500"
+              : "bg-white border-blue-600 text-blue-600 hover:bg-blue-50 focus:ring-2 focus:ring-blue-500/20"
+              }`}
+          >
+            {modelsLoading ? (
+              <div className="flex items-center justify-center gap-2">
+                <Loader2 className="w-4 h-4 animate-spin" />
+                Checking for models...
+              </div>
+            ) : (
+              "Check for available models"
+            )}
+          </button>
+        </div>
+      )}
+
+      {/* Show message if no models found */}
+      {modelsChecked && availableModels.length === 0 && (
+        <div className="mb-4 p-3 bg-yellow-50 border border-yellow-200 rounded-lg">
+          <p className="text-sm text-yellow-800">
+            No models found. Please make sure your API key is valid and has access to OpenAI models.
+          </p>
+        </div>
+      )}
+
+      {/* Model Selection - only show if models are available */}
+      {modelsChecked && availableModels.length > 0 ? (
+        <div>
+          <label className="block text-sm font-medium text-gray-700 mb-3">
+            Select OpenAI Model
+          </label>
+          <div className="w-full">
+            <Popover
+              open={openModelSelect}
+              onOpenChange={setOpenModelSelect}
+            >
+              <PopoverTrigger asChild>
+                <Button
+                  variant="outline"
+                  role="combobox"
+                  aria-expanded={openModelSelect}
+                  className="w-full h-12 px-4 py-4 outline-none border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500/20 focus:border-blue-500 transition-colors hover:border-gray-400 justify-between"
+                >
+                  <div className="flex gap-3 items-center">
+                    <span className="text-sm font-medium text-gray-900">
+                      {openaiModel
+                        ? availableModels.find(model => model === openaiModel) || openaiModel
+                        : "Select a model"}
+                    </span>
+                  </div>
+                  <ChevronsUpDown className="w-4 h-4 text-gray-500" />
+                </Button>
+              </PopoverTrigger>
+              <PopoverContent
+                className="p-0"
+                align="start"
+                style={{ width: "var(--radix-popover-trigger-width)" }}
+              >
+                <Command>
+                  <CommandInput placeholder="Search models..." />
+                  <CommandList>
+                    <CommandEmpty>No model found.</CommandEmpty>
+                    <CommandGroup>
+                      {availableModels.map((model, index) => (
+                        <CommandItem
+                          key={index}
+                          value={model}
+                          onSelect={(value) => {
+                            onInputChange(value, "openai_model");
+                            setOpenModelSelect(false);
+                          }}
+                        >
+                          <Check
+                            className={cn(
+                              "mr-2 h-4 w-4",
+                              openaiModel === model
+                                ? "opacity-100"
+                                : "opacity-0"
+                            )}
+                          />
+                          <div className="flex gap-3 items-center">
+                            <div className="flex flex-col space-y-1 flex-1">
+                              <div className="flex items-center justify-between gap-2">
+                                <span className="text-sm font-medium text-gray-900">
+                                  {model}
+                                </span>
+                              </div>
+                            </div>
+                          </div>
+                        </CommandItem>
+                      ))}
+                    </CommandGroup>
+                  </CommandList>
+                </Command>
+              </PopoverContent>
+            </Popover>
+          </div>
+        </div>
+      ) : null}
     </div>
   );
-} 
+}

servers/nextjs/types/global.d.ts

@@ -15,17 +15,36 @@ interface TextFrameProps {
 
 interface LLMConfig {
   LLM?: string;
+
+  // OpenAI
   OPENAI_API_KEY?: string;
+  OPENAI_MODEL?: string;
+
+  // Google
   GOOGLE_API_KEY?: string;
+  GOOGLE_MODEL?: string;
+
+  // Anthropic
+  ANTHROPIC_API_KEY?: string;
+  ANTHROPIC_MODEL?: string;
+
+  // Ollama
   OLLAMA_URL?: string;
   OLLAMA_MODEL?: string;
+
+  // Custom LLM
   CUSTOM_LLM_URL?: string;
   CUSTOM_LLM_API_KEY?: string;
   CUSTOM_MODEL?: string;
+
+  // Image providers
   IMAGE_PROVIDER?: string;
   PIXABAY_API_KEY?: string;
   PEXELS_API_KEY?: string;
 
+  // Extended reasoning
+  EXTENDED_REASONING?: boolean;
+
   // Only used in UI settings
   USE_CUSTOM_URL?: boolean;
 }

servers/nextjs/utils/providerConstants.ts

@@ -67,16 +67,17 @@ export const LLM_PROVIDERS: Record<string, LLMProviderOption> = {
   openai: {
     value: "openai",
     label: "OpenAI",
-    description: "OpenAI's latest image generation model",
-    model_value: "gpt-4.1",
-    model_label: "GPT-4.1"
+    description: "OpenAI's latest text generation model",
   },
   google: {
     value: "google",
     label: "Google",
-    description: "Google's primary image generation model",
-    model_value: "gemini-2.0-flash",
-    model_label: "Gemini 2.0 Flash"
+    description: "Google's primary text generation model",
+  },
+  anthropic: {
+    value: "anthropic",
+    label: "Anthropic",
+    description: "Anthropic's Claude models",
   },
   ollama: {
     value: "ollama",

servers/nextjs/utils/providerUtils.ts

@@ -1,5 +1,3 @@
-import { toast } from "sonner";
-
 export interface OllamaModel {
   label: string;
   value: string;
@@ -18,16 +16,37 @@ export interface DownloadingModel {
 
 export interface LLMConfig {
   LLM?: string;
+
+  // OpenAI
   OPENAI_API_KEY?: string;
+  OPENAI_MODEL?: string;
+
+  // Google
   GOOGLE_API_KEY?: string;
+  GOOGLE_MODEL?: string;
+
+  // Anthropic
+  ANTHROPIC_API_KEY?: string;
+  ANTHROPIC_MODEL?: string;
+
+  // Ollama
   OLLAMA_URL?: string;
   OLLAMA_MODEL?: string;
+
+  // Custom LLM
   CUSTOM_LLM_URL?: string;
   CUSTOM_LLM_API_KEY?: string;
   CUSTOM_MODEL?: string;
+
+  // Image providers
+  IMAGE_PROVIDER?: string;
   PEXELS_API_KEY?: string;
   PIXABAY_API_KEY?: string;
-  IMAGE_PROVIDER?: string;
+
+  // Extended reasoning
+  EXTENDED_REASONING?: boolean;
+
+  // Only used in UI settings
   USE_CUSTOM_URL?: boolean;
 }
 
@@ -42,11 +61,15 @@ export interface OllamaModelsResult {
 export const updateLLMConfig = (
   currentConfig: LLMConfig,
   field: string,
-  value: string
+  value: string | boolean
 ): LLMConfig => {
   const fieldMappings: Record<string, keyof LLMConfig> = {
     openai_api_key: "OPENAI_API_KEY",
+    openai_model: "OPENAI_MODEL",
     google_api_key: "GOOGLE_API_KEY",
+    google_model: "GOOGLE_MODEL",
+    anthropic_api_key: "ANTHROPIC_API_KEY",
+    anthropic_model: "ANTHROPIC_MODEL",
     ollama_url: "OLLAMA_URL",
     ollama_model: "OLLAMA_MODEL",
     custom_llm_url: "CUSTOM_LLM_URL",
@@ -55,6 +78,8 @@ export const updateLLMConfig = (
     pexels_api_key: "PEXELS_API_KEY",
     pixabay_api_key: "PIXABAY_API_KEY",
     image_provider: "IMAGE_PROVIDER",
+    extended_reasoning: "EXTENDED_REASONING",
+    use_custom_url: "USE_CUSTOM_URL",
   };
 
   const configKey = fieldMappings[field];
@@ -86,53 +111,6 @@ export const changeProvider = (
   return newConfig;
 };
 
-/**
- * Fetches supported Ollama models
- */
-export const fetchOllamaModels = async (): Promise<OllamaModel[]> => {
-  try {
-    const response = await fetch("/api/v1/ppt/ollama/models/supported");
-    const models = await response.json();
-    return models || [];
-  } catch (error) {
-    console.error("Error fetching ollama models:", error);
-    return []; // Ensure we always return an empty array on error
-  }
-};
-
-/**
- * Fetches Ollama models and validates current selection
- * Returns models and updated config if needed
- */
-export const fetchOllamaModelsWithConfig = async (
-  config: LLMConfig
-): Promise<OllamaModelsResult> => {
-  try {
-    const models = await fetchOllamaModels();
-
-    // Check if currently selected model is still available
-    let updatedConfig: LLMConfig | undefined;
-    if (config.OLLAMA_MODEL && models && models.length > 0) {
-      const isModelAvailable = models.some(
-        (model: OllamaModel) => model.value === config.OLLAMA_MODEL
-      );
-      if (!isModelAvailable) {
-        updatedConfig = { ...config, OLLAMA_MODEL: "" };
-      }
-    }
-
-    return {
-      models,
-      updatedConfig
-    };
-  } catch (error) {
-    console.error("Error fetching ollama models:", error);
-    return {
-      models: [],
-      updatedConfig: { ...config, OLLAMA_MODEL: "" }
-    };
-  }
-};
 
 export const checkIfSelectedOllamaModelIsPulled = async (ollamaModel: string) => {
   try {
@@ -146,37 +124,6 @@ export const checkIfSelectedOllamaModelIsPulled = async (ollamaModel: string) =>
   }
 }
 
-/**
- * Fetches available custom models
- */
-export const fetchCustomModels = async (
-  url: string,
-  apiKey: string
-): Promise<string[]> => {
-  try {
-    const response = await fetch("/api/v1/ppt/custom_llm/models/available", {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-      },
-      body: JSON.stringify({
-        url: url || "",
-        api_key: apiKey || "",
-      }),
-    });
-
-    if (!response.ok) {
-      throw new Error(`HTTP error! status: ${response.status}`);
-    }
-
-    const data = await response.json();
-    return data;
-  } catch (error) {
-    toast.info("Could not fetch custom models");
-    console.error("Error fetching custom models:", error);
-    throw error;
-  }
-};
 
 /**
  * Resets downloading model state
@@ -234,51 +181,4 @@ export const pullOllamaModel = async (
       }
     }, 1000);
   });
-};
-
-/**
- * Sets Ollama configuration based on custom URL preference
- */
-export const setOllamaConfig = (
-  currentConfig: LLMConfig,
-  useCustomUrl: boolean
-): LLMConfig => {
-  let customUrl = "http://localhost:11434";
-  if (!useCustomUrl) {
-    return {
-      ...currentConfig,
-      OLLAMA_URL: customUrl,
-      USE_CUSTOM_URL: false,
-    };
-  } else {
-    return { ...currentConfig, USE_CUSTOM_URL: true, OLLAMA_URL: customUrl };
-  }
-};
-
-/**
- * Handles saving configuration with error handling
- */
-export const handleSaveConfiguration = async (
-  llmConfig: LLMConfig,
-  handleSaveLLMConfig: (config: LLMConfig) => Promise<void>,
-  pullOllamaModels?: () => Promise<void>
-): Promise<void> => {
-  try {
-    await handleSaveLLMConfig(llmConfig);
-    if (llmConfig.LLM === "ollama" && pullOllamaModels) {
-      await pullOllamaModels();
-    }
-    toast.success("Configuration saved successfully");
-  } catch (error) {
-    console.error("Error:", error);
-    toast.error(
-      error instanceof Error
-        ? error.message
-        : "Failed to save configuration",
-      {
-        description: "Failed to save configuration",
-      }
-    );
-    throw error;
-  }
-}; 
+};

servers/nextjs/utils/storeHelpers.ts

@@ -16,8 +16,30 @@ export const handleSaveLLMConfig = async (llmConfig: LLMConfig) => {
 export const hasValidLLMConfig = (llmConfig: LLMConfig) => {
   if (!llmConfig.LLM) return false;
   if (!llmConfig.IMAGE_PROVIDER) return false;
-  const OPENAI_API_KEY = llmConfig.OPENAI_API_KEY;
-  const GOOGLE_API_KEY = llmConfig.GOOGLE_API_KEY;
+
+  const isOpenAIConfigValid =
+    llmConfig.OPENAI_MODEL !== "" &&
+    llmConfig.OPENAI_MODEL !== null &&
+    llmConfig.OPENAI_MODEL !== undefined &&
+    llmConfig.OPENAI_API_KEY !== "" &&
+    llmConfig.OPENAI_API_KEY !== null &&
+    llmConfig.OPENAI_API_KEY !== undefined;
+
+  const isGoogleConfigValid =
+    llmConfig.GOOGLE_MODEL !== "" &&
+    llmConfig.GOOGLE_MODEL !== null &&
+    llmConfig.GOOGLE_MODEL !== undefined &&
+    llmConfig.GOOGLE_API_KEY !== "" &&
+    llmConfig.GOOGLE_API_KEY !== null &&
+    llmConfig.GOOGLE_API_KEY !== undefined;
+
+  const isAnthropicConfigValid =
+    llmConfig.ANTHROPIC_MODEL !== "" &&
+    llmConfig.ANTHROPIC_MODEL !== null &&
+    llmConfig.ANTHROPIC_MODEL !== undefined &&
+    llmConfig.ANTHROPIC_API_KEY !== "" &&
+    llmConfig.ANTHROPIC_API_KEY !== null &&
+    llmConfig.ANTHROPIC_API_KEY !== undefined;
 
   const isOllamaConfigValid =
     llmConfig.OLLAMA_MODEL !== "" &&
@@ -42,9 +64,9 @@ export const hasValidLLMConfig = (llmConfig: LLMConfig) => {
       case "pixabay":
         return llmConfig.PIXABAY_API_KEY && llmConfig.PIXABAY_API_KEY !== "";
       case "dall-e-3":
-        return OPENAI_API_KEY && OPENAI_API_KEY !== "";
+        return llmConfig.OPENAI_API_KEY && llmConfig.OPENAI_API_KEY !== "";
       case "gemini_flash":
-        return GOOGLE_API_KEY && GOOGLE_API_KEY !== "";
+        return llmConfig.GOOGLE_API_KEY && llmConfig.GOOGLE_API_KEY !== "";
       default:
         return false;
     }
@@ -52,18 +74,16 @@ export const hasValidLLMConfig = (llmConfig: LLMConfig) => {
 
   const isLLMConfigValid =
     llmConfig.LLM === "openai"
-      ? OPENAI_API_KEY !== "" &&
-      OPENAI_API_KEY !== null &&
-      OPENAI_API_KEY !== undefined
+      ? isOpenAIConfigValid
       : llmConfig.LLM === "google"
-        ? GOOGLE_API_KEY !== "" &&
-        GOOGLE_API_KEY !== null &&
-        GOOGLE_API_KEY !== undefined
-        : llmConfig.LLM === "ollama"
-          ? isOllamaConfigValid
-          : llmConfig.LLM === "custom"
-            ? isCustomConfigValid
-            : false;
+        ? isGoogleConfigValid
+        : llmConfig.LLM === "anthropic"
+          ? isAnthropicConfigValid
+          : llmConfig.LLM === "ollama"
+            ? isOllamaConfigValid
+            : llmConfig.LLM === "custom"
+              ? isCustomConfigValid
+              : false;
 
   return isLLMConfigValid && isImageConfigValid();
 };

start.js

@@ -38,15 +38,20 @@ const setupUserConfigFromEnv = () => {
   const userConfig = {
     LLM: process.env.LLM || existingConfig.LLM,
     OPENAI_API_KEY: process.env.OPENAI_API_KEY || existingConfig.OPENAI_API_KEY,
+    OPENAI_MODEL: process.env.OPENAI_MODEL || existingConfig.OPENAI_MODEL,
     GOOGLE_API_KEY: process.env.GOOGLE_API_KEY || existingConfig.GOOGLE_API_KEY,
+    GOOGLE_MODEL: process.env.GOOGLE_MODEL || existingConfig.GOOGLE_MODEL,
     OLLAMA_URL: process.env.OLLAMA_URL || existingConfig.OLLAMA_URL,
     OLLAMA_MODEL: process.env.OLLAMA_MODEL || existingConfig.OLLAMA_MODEL,
+    ANTHROPIC_API_KEY: process.env.ANTHROPIC_API_KEY || existingConfig.ANTHROPIC_API_KEY,
+    ANTHROPIC_MODEL: process.env.ANTHROPIC_MODEL || existingConfig.ANTHROPIC_MODEL,
     CUSTOM_LLM_URL: process.env.CUSTOM_LLM_URL || existingConfig.CUSTOM_LLM_URL,
     CUSTOM_LLM_API_KEY: process.env.CUSTOM_LLM_API_KEY || existingConfig.CUSTOM_LLM_API_KEY,
     CUSTOM_MODEL: process.env.CUSTOM_MODEL || existingConfig.CUSTOM_MODEL,
     PEXELS_API_KEY: process.env.PEXELS_API_KEY || existingConfig.PEXELS_API_KEY,
     PIXABAY_API_KEY: process.env.PIXABAY_API_KEY || existingConfig.PIXABAY_API_KEY,
     IMAGE_PROVIDER: process.env.IMAGE_PROVIDER || existingConfig.IMAGE_PROVIDER,
+    EXTENDED_REASONING: process.env.EXTENDED_REASONING || existingConfig.EXTENDED_REASONING,
     USE_CUSTOM_URL: process.env.USE_CUSTOM_URL || existingConfig.USE_CUSTOM_URL,
   };