refactor(fastapi): progress

servers/fastapi/api/lifespan.py

@@ -0,0 +1,75 @@
+from contextlib import asynccontextmanager
+import os
+
+from fastapi import FastAPI
+from sqlmodel import SQLModel
+
+from constants.supported_ollama_models import SUPPORTED_OLLAMA_MODELS
+from enums.llm_provider import LLMProvider
+from services import SQL_ENGINE
+from utils.custom_llm_provider import list_available_custom_models
+from utils.llm_provider import (
+    get_llm_provider,
+    is_custom_llm_selected,
+    is_ollama_selected,
+)
+from utils.ollama import pull_ollama_model
+
+
+can_change_keys = os.getenv("CAN_CHANGE_KEYS") != "false"
+
+
+async def check_llm_model_availability():
+    if not can_change_keys:
+        if get_llm_provider() == LLMProvider.OPENAI:
+            openai_api_key = os.getenv("OPENAI_API_KEY")
+            if not openai_api_key:
+                raise Exception("OPENAI_API_KEY must be provided")
+
+        elif get_llm_provider() == LLMProvider.GOOGLE:
+            google_api_key = os.getenv("GOOGLE_API_KEY")
+            if not google_api_key:
+                raise Exception("GOOGLE_API_KEY must be provided")
+
+        elif is_ollama_selected():
+            ollama_model = os.getenv("OLLAMA_MODEL")
+            if not ollama_model:
+                raise Exception("OLLAMA_MODEL must be provided")
+
+            if ollama_model not in SUPPORTED_OLLAMA_MODELS:
+                raise Exception(f"Model {ollama_model} is not supported")
+
+            print("-" * 50)
+            print("Pulling model: ", ollama_model)
+            async for event in pull_ollama_model(ollama_model):
+                print(event)
+            print("Pulled model: ", ollama_model)
+            print("-" * 50)
+
+        elif is_custom_llm_selected():
+            custom_model = os.getenv("CUSTOM_MODEL")
+            custom_llm_url = os.getenv("CUSTOM_LLM_URL")
+            custom_llm_api_key = os.getenv("CUSTOM_LLM_API_KEY")
+            if not custom_model:
+                raise Exception("CUSTOM_MODEL must be provided")
+            if not custom_llm_url:
+                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(
+                custom_llm_url, custom_llm_api_key
+            )
+            print("Available models: ", models)
+            print("-" * 50)
+            if custom_model not in models:
+                raise Exception(f"Model {custom_model} is not available")
+
+
+@asynccontextmanager
+async def app_lifespan(_: FastAPI):
+    os.makedirs(os.getenv("APP_DATA_DIRECTORY"), exist_ok=True)
+    SQLModel.metadata.create_all(SQL_ENGINE)
+    await check_llm_model_availability()
+    yield

servers/fastapi/api/main.py

@@ -1,98 +1,8 @@
-import asyncio
-import os
-from fastapi import FastAPI, Request
-from fastapi.middleware.cors import CORSMiddleware
-from sqlmodel import SQLModel
-from contextlib import asynccontextmanager
-
-from api.models import SelectedLLMProvider
-from api.routers.presentation.router import presentation_router
-from api.services.database import sql_engine
-from api.utils.supported_ollama_models import SUPPORTED_OLLAMA_MODELS
-from api.utils.utils import update_env_with_user_config
-from api.utils.model_utils import (
-    get_selected_llm_provider,
-    is_custom_llm_selected,
-    is_ollama_selected,
-    list_available_custom_models,
-    pull_ollama_model,
-)
-
-can_change_keys = os.getenv("CAN_CHANGE_KEYS") != "false"
+from fastapi import FastAPI
+from api.v1.ppt.router import API_V1_PPT_ROUTER
+from api.lifespan import app_lifespan
 
 
-async def check_llm_model_availability():
-    if not can_change_keys:
-        if get_selected_llm_provider() == SelectedLLMProvider.OPENAI:
-            openai_api_key = os.getenv("OPENAI_API_KEY")
-            if not openai_api_key:
-                raise Exception("OPENAI_API_KEY must be provided")
+APP = FastAPI(lifespan=app_lifespan)
 
-        elif get_selected_llm_provider() == SelectedLLMProvider.GOOGLE:
-            google_api_key = os.getenv("GOOGLE_API_KEY")
-            if not google_api_key:
-                raise Exception("GOOGLE_API_KEY must be provided")
-
-        elif is_ollama_selected():
-            ollama_model = os.getenv("OLLAMA_MODEL")
-            if not ollama_model:
-                raise Exception("OLLAMA_MODEL must be provided")
-
-            if ollama_model not in SUPPORTED_OLLAMA_MODELS:
-                raise Exception(f"Model {ollama_model} is not supported")
-
-            print("-" * 50)
-            print("Pulling model: ", ollama_model)
-            async for event in pull_ollama_model(ollama_model):
-                print(event)
-            print("Pulled model: ", ollama_model)
-            print("-" * 50)
-
-        elif is_custom_llm_selected():
-            custom_model = os.getenv("CUSTOM_MODEL")
-            custom_llm_url = os.getenv("CUSTOM_LLM_URL")
-            custom_llm_api_key = os.getenv("CUSTOM_LLM_API_KEY")
-            if not custom_model:
-                raise Exception("CUSTOM_MODEL must be provided")
-            if not custom_llm_url:
-                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(
-                custom_llm_url, custom_llm_api_key
-            )
-            print("Available models: ", models)
-            print("-" * 50)
-            if custom_model not in models:
-                raise Exception(f"Model {custom_model} is not available")
-
-
-@asynccontextmanager
-async def lifespan(_: FastAPI):
-    os.makedirs(os.getenv("APP_DATA_DIRECTORY"), exist_ok=True)
-    SQLModel.metadata.create_all(sql_engine)
-    await check_llm_model_availability()
-    yield
-
-
-app = FastAPI(lifespan=lifespan)
-origins = ["*"]
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=origins,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-@app.middleware("http")
-async def update_env_middleware(request: Request, call_next):
-    if can_change_keys:
-        update_env_with_user_config()
-    return await call_next(request)
-
-
-app.include_router(presentation_router)
+APP.include_router(API_V1_PPT_ROUTER)

servers/fastapi/api/middlewares.py

@@ -0,0 +1,23 @@
+from fastapi import Request
+from api.main import APP
+from fastapi.middleware.cors import CORSMiddleware
+
+from utils.get_env import get_can_change_keys_env
+from utils.user_config import update_env_with_user_config
+
+
+origins = ["*"]
+APP.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@APP.middleware("http")
+async def update_env_middleware(request: Request, call_next):
+    if get_can_change_keys_env() != "false":
+        update_env_with_user_config()
+    return await call_next(request)

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

@@ -0,0 +1,34 @@
+from http.client import HTTPException
+from typing import List, Optional
+import uuid
+from fastapi import UploadFile
+
+from api.v1.ppt.router import API_V1_PPT_ROUTER
+from constants.documents import UPLOAD_ACCEPTED_DOCUMENTS, UPLOAD_ACCEPTED_IMAGES
+from services import TEMP_FILE_SERVICE
+from utils.validators import validate_files
+
+
+@API_V1_PPT_ROUTER.post("/files/upload")
+async def upload_files(files: Optional[List[UploadFile]]):
+    if not files:
+        raise HTTPException(400, "Files are required")
+
+    temp_dir = TEMP_FILE_SERVICE.create_temp_dir(str(uuid.uuid4()))
+
+    validate_files(files, True, True, 50, UPLOAD_ACCEPTED_DOCUMENTS)
+    validate_files(files, True, True, 10, UPLOAD_ACCEPTED_IMAGES)
+
+    temp_files: List[str] = []
+    if files:
+        for each_file in files:
+            temp_path = TEMP_FILE_SERVICE.create_temp_file_path(
+                each_file.filename, temp_dir
+            )
+            with open(temp_path, "wb") as f:
+                content = await each_file.read()
+                f.write(content)
+
+            temp_files.append(temp_path)
+
+    return temp_files

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

@@ -0,0 +1,5 @@
+from fastapi import APIRouter
+
+
+API_V1_PPT = "/api/v1/ppt"
+API_V1_PPT_ROUTER = APIRouter(prefix=API_V1_PPT)

servers/fastapi/constants/documents.py

@@ -0,0 +1,21 @@
+PDF_MIME_TYPES = ["application/pdf"]
+TEXT_MIME_TYPES = ["text/plain"]
+POWERPOINT_TYPES = [
+    "application/vnd.openxmlformats-officedocument.presentationml.presentation"
+]
+WORD_TYPES = [
+    "application/msword",
+    "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+]
+SPREADSHEET_TYPES = ["text/csv", "application/csv"]
+
+
+PNG_MIME_TYPES = ["image/png"]
+JPEG_MIME_TYPES = ["image/jpeg"]
+WEBP_MIME_TYPES = ["image/webp"]
+
+
+UPLOAD_ACCEPTED_DOCUMENTS = (
+    PDF_MIME_TYPES + TEXT_MIME_TYPES + POWERPOINT_TYPES + WORD_TYPES
+)
+UPLOAD_ACCEPTED_IMAGES = PNG_MIME_TYPES + JPEG_MIME_TYPES + WEBP_MIME_TYPES

servers/fastapi/constants/supported_ollama_models.py

@@ -0,0 +1,253 @@
+from models.ollama_model_metadata import OllamaModelMetadata
+
+
+SUPPORTED_LLAMA_MODELS = {
+    "llama3:8b": OllamaModelMetadata(
+        label="Llama 3:8b",
+        value="llama3:8b",
+        description="❌ Graphs not supported.",
+        size="4.7GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/meta.png",
+    ),
+    "llama3:70b": OllamaModelMetadata(
+        label="Llama 3:70b",
+        value="llama3:70b",
+        description="✅ Graphs supported.",
+        size="40GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/meta.png",
+    ),
+    "llama3.1:8b": OllamaModelMetadata(
+        label="Llama 3.1:8b",
+        value="llama3.1:8b",
+        description="❌ Graphs not supported.",
+        size="4.9GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/meta.png",
+    ),
+    "llama3.1:70b": OllamaModelMetadata(
+        label="Llama 3.1:70b",
+        value="llama3.1:70b",
+        description="✅ Graphs supported.",
+        size="43GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/meta.png",
+    ),
+    "llama3.1:405b": OllamaModelMetadata(
+        label="Llama 3.1:405b",
+        value="llama3.1:405b",
+        description="✅ Graphs supported.",
+        size="243GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/meta.png",
+    ),
+    "llama3.2:1b": OllamaModelMetadata(
+        label="Llama 3.2:1b",
+        value="llama3.2:1b",
+        description="❌ Graphs not supported.",
+        size="1.3GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/meta.png",
+    ),
+    "llama3.2:3b": OllamaModelMetadata(
+        label="Llama 3.2:3b",
+        value="llama3.2:3b",
+        description="❌ Graphs not supported.",
+        size="2GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/meta.png",
+    ),
+    "llama3.3:70b": OllamaModelMetadata(
+        label="Llama 3.3:70b",
+        value="llama3.3:70b",
+        description="✅ Graphs supported.",
+        size="43GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/meta.png",
+    ),
+    "llama4:16x17b": OllamaModelMetadata(
+        label="Llama 4:16x17b",
+        value="llama4:16x17b",
+        description="✅ Graphs supported.",
+        size="67GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/meta.png",
+    ),
+    "llama4:128x17b": OllamaModelMetadata(
+        label="Llama 4:128x17b",
+        value="llama4:128x17b",
+        description="✅ Graphs supported.",
+        size="245GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/meta.png",
+    ),
+}
+
+SUPPORTED_GEMMA_MODELS = {
+    "gemma3:1b": OllamaModelMetadata(
+        label="Gemma 3:1b",
+        value="gemma3:1b",
+        description="❌ Graphs not supported.",
+        size="815MB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/gemma.png",
+    ),
+    "gemma3:4b": OllamaModelMetadata(
+        label="Gemma 3:4b",
+        value="gemma3:4b",
+        description="❌ Graphs not supported.",
+        size="3.3GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/gemma.png",
+    ),
+    "gemma3:12b": OllamaModelMetadata(
+        label="Gemma 3:12b",
+        value="gemma3:12b",
+        description="❌ Graphs not supported.",
+        size="8.1GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/gemma.png",
+    ),
+    "gemma3:27b": OllamaModelMetadata(
+        label="Gemma 3:27b",
+        value="gemma3:27b",
+        description="✅ Graphs supported.",
+        size="17GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/gemma.png",
+    ),
+}
+
+SUPPORTED_DEEPSEEK_MODELS = {
+    "deepseek-r1:1.5b": OllamaModelMetadata(
+        label="DeepSeek R1:1.5b",
+        value="deepseek-r1:1.5b",
+        description="❌ Graphs not supported.",
+        size="1.1GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/deepseek.png",
+    ),
+    "deepseek-r1:7b": OllamaModelMetadata(
+        label="DeepSeek R1:7b",
+        value="deepseek-r1:7b",
+        description="❌ Graphs not supported.",
+        size="4.7GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/deepseek.png",
+    ),
+    "deepseek-r1:8b": OllamaModelMetadata(
+        label="DeepSeek R1:8b",
+        value="deepseek-r1:8b",
+        description="❌ Graphs not supported.",
+        size="5.2GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/deepseek.png",
+    ),
+    "deepseek-r1:14b": OllamaModelMetadata(
+        label="DeepSeek R1:14b",
+        value="deepseek-r1:14b",
+        description="❌ Graphs not supported.",
+        size="9GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/deepseek.png",
+    ),
+    "deepseek-r1:32b": OllamaModelMetadata(
+        label="DeepSeek R1:32b",
+        value="deepseek-r1:32b",
+        description="✅ Graphs supported.",
+        size="20GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/deepseek.png",
+    ),
+    "deepseek-r1:70b": OllamaModelMetadata(
+        label="DeepSeek R1:70b",
+        value="deepseek-r1:70b",
+        description="✅ Graphs supported.",
+        size="43GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/deepseek.png",
+    ),
+    "deepseek-r1:671b": OllamaModelMetadata(
+        label="DeepSeek R1:671b",
+        value="deepseek-r1:671b",
+        description="✅ Graphs supported.",
+        size="404GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/deepseek.png",
+    ),
+}
+
+SUPPORTED_QWEN_MODELS = {
+    "qwen3:0.6b": OllamaModelMetadata(
+        label="Qwen 3:0.6b",
+        value="qwen3:0.6b",
+        description="❌ Graphs not supported.",
+        size="523MB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/qwen.png",
+    ),
+    "qwen3:1.7b": OllamaModelMetadata(
+        label="Qwen 3:1.7b",
+        value="qwen3:1.7b",
+        description="❌ Graphs not supported.",
+        size="1.4GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/qwen.png",
+    ),
+    "qwen3:4b": OllamaModelMetadata(
+        label="Qwen 3:4b",
+        value="qwen3:4b",
+        description="❌ Graphs not supported.",
+        size="2.6GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/qwen.png",
+    ),
+    "qwen3:8b": OllamaModelMetadata(
+        label="Qwen 3:8b",
+        value="qwen3:8b",
+        description="❌ Graphs not supported.",
+        size="5.2GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/qwen.png",
+    ),
+    "qwen3:14b": OllamaModelMetadata(
+        label="Qwen 3:14b",
+        value="qwen3:14b",
+        description="❌ Graphs not supported.",
+        size="9.3GB",
+        supports_graph=False,
+        icon="/static/servers/fastapi/assets/icons/qwen.png",
+    ),
+    "qwen3:30b": OllamaModelMetadata(
+        label="Qwen 3:30b",
+        value="qwen3:30b",
+        description="✅ Graphs supported.",
+        size="19GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/qwen.png",
+    ),
+    "qwen3:32b": OllamaModelMetadata(
+        label="Qwen 3:32b",
+        value="qwen3:32b",
+        description="✅ Graphs supported.",
+        size="20GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/qwen.png",
+    ),
+    "qwen3:235b": OllamaModelMetadata(
+        label="Qwen 3:235b",
+        value="qwen3:235b",
+        description="✅ Graphs supported.",
+        size="142GB",
+        supports_graph=True,
+        icon="/static/servers/fastapi/assets/icons/qwen.png",
+    ),
+}
+
+SUPPORTED_OLLAMA_MODELS = {
+    **SUPPORTED_LLAMA_MODELS,
+    **SUPPORTED_GEMMA_MODELS,
+    **SUPPORTED_DEEPSEEK_MODELS,
+    **SUPPORTED_QWEN_MODELS,
+}

servers/fastapi/enums/llm_provider.py

@@ -0,0 +1,8 @@
+from enum import Enum
+
+
+class LLMProvider(Enum):
+    OLLAMA = "ollama"
+    OPENAI = "openai"
+    GOOGLE = "google"
+    CUSTOM = "custom"

servers/fastapi/graph_processor/models.py

@@ -1,116 +0,0 @@
-from enum import Enum
-from typing import List, Optional
-from pydantic import BaseModel, Field, model_validator
-
-
-class PointModel(BaseModel):
-    x: float
-    y: float
-
-    def to_list(self) -> List[float]:
-        return [self.x, self.y]
-
-
-class PointWithRadius(PointModel):
-    radius: Optional[float] = None
-
-
-class BarSeriesModel(BaseModel):
-    name: str
-    data: List[float] = Field(
-        description="Only numbers should be given out in data. Don't include text/string in data."
-    )
-
-
-class ScatterSeriesModel(BaseModel):
-    name: str
-    points: List[PointModel]
-
-
-class BubbleSeriesModel(BaseModel):
-    name: str
-    points: List[PointWithRadius]
-
-
-class LineSeriesModel(BaseModel):
-    name: str
-    data: List[float] = Field(
-        description="Only numbers should be given out in data. Don't include text/string in data."
-    )
-
-
-class PieChartSeriesModel(BaseModel):
-    data: List[float]
-
-
-class BarGraphDataModel(BaseModel):
-    categories: List[str]
-    series: List[BarSeriesModel] = Field(
-        description="There should be no more than 3 series"
-    )
-
-
-class ScatterChartDataModel(BaseModel):
-    series: List[ScatterSeriesModel]
-
-
-class BubbleChartDataModel(BaseModel):
-    series: List[BubbleSeriesModel]
-
-
-class LineChartDataModel(BaseModel):
-    categories: List[str]
-    series: List[LineSeriesModel] = Field(
-        description="There should be no more than 3 series"
-    )
-
-
-class PieChartDataModel(BaseModel):
-    categories: List[str]
-    series: List[PieChartSeriesModel] = Field(
-        description="One series model with list of data",
-        min_length=1,
-    )
-
-    @model_validator(mode="after")
-    def limit_series(self):
-        self.series = self.series[:1]
-        return self
-
-
-class GraphTypeEnum(Enum):
-    pie = "pie"
-    bar = "bar"
-    line = "line"
-
-
-class LLMGraphModel(BaseModel):
-    name: str
-    type: GraphTypeEnum
-    unit: Optional[str] = Field(
-        description="Unit of the data in the graph. Example: %, kg, million USD, tonnes, etc."
-    )
-    data: PieChartDataModel | LineChartDataModel | BarGraphDataModel
-
-
-class GraphModel(LLMGraphModel):
-    style: Optional[dict] = {}
-
-    @classmethod
-    def from_llm_graph_model(
-        cls, llm_graph_model: LLMGraphModel, style: Optional[dict] = {}
-    ):
-        return cls(
-            name=llm_graph_model.name,
-            type=llm_graph_model.type,
-            unit=llm_graph_model.unit,
-            data=llm_graph_model.data,
-            style=style,
-        )
-
-
-GRAPH_TYPE_MAPPING = {
-    GraphTypeEnum.pie: PieChartDataModel,
-    GraphTypeEnum.bar: BarGraphDataModel,
-    GraphTypeEnum.line: LineChartDataModel,
-}

servers/fastapi/models/ollama_model_metadata.py

@@ -0,0 +1,10 @@
+from pydantic import BaseModel
+
+
+class OllamaModelMetadata(BaseModel):
+    label: str
+    value: str
+    description: str
+    icon: str
+    size: str
+    supports_graph: bool

servers/fastapi/models/ollama_model_status_response.py

@@ -0,0 +1,10 @@
+from typing import Optional
+from pydantic import BaseModel
+
+
+class OllamaModelStatusResponse(BaseModel):
+    name: str
+    size: Optional[int] = None
+    downloaded: Optional[int] = None
+    status: str
+    done: bool

servers/fastapi/models/user_config.py

@@ -0,0 +1,14 @@
+from typing import Optional
+from pydantic import BaseModel
+
+
+class UserConfig(BaseModel):
+    LLM: Optional[str] = None
+    OPENAI_API_KEY: Optional[str] = None
+    GOOGLE_API_KEY: Optional[str] = None
+    OLLAMA_URL: Optional[str] = None
+    OLLAMA_MODEL: Optional[str] = None
+    CUSTOM_LLM_URL: Optional[str] = None
+    CUSTOM_LLM_API_KEY: Optional[str] = None
+    CUSTOM_MODEL: Optional[str] = None
+    PEXELS_API_KEY: Optional[str] = None

servers/fastapi/ppt_generator/models/content_type_models.py

@@ -1,232 +0,0 @@
-from enum import Enum
-from typing import List, Mapping, Union
-from pydantic import BaseModel
-
-from graph_processor.models import GraphModel, LLMGraphModel
-from ppt_generator.models.other_models import (
-    TYPE1,
-    TYPE2,
-    TYPE3,
-    TYPE4,
-    TYPE5,
-    TYPE6,
-    TYPE7,
-    TYPE8,
-    TYPE9,
-)
-
-
-class TableType(Enum):
-    TABLE = "table"
-    BAR = "bar"
-    LINE = "line"
-    PIE = "pie"
-
-
-class TableDataModel(BaseModel):
-    x_labels: List[str]
-    y_labels: List[str]
-    data: List[List[float]]
-
-
-class TableModel(BaseModel):
-    name: str
-    type: TableType
-    data: TableDataModel
-
-
-class HeadingModel(BaseModel):
-    heading: str
-    description: str
-
-    def to_llm_content(self, image_prompt: str = None, icon_query: str = None):
-        from ppt_generator.models.llm_models import (
-            LLMHeadingModel,
-            LLMHeadingModelWithImagePrompt,
-            LLMHeadingModelWithIconQuery,
-        )
-
-        if image_prompt:
-            return LLMHeadingModelWithImagePrompt(
-                heading=self.heading,
-                description=self.description,
-                image_prompt=image_prompt,
-            )
-        elif icon_query:
-            return LLMHeadingModelWithIconQuery(
-                heading=self.heading,
-                description=self.description,
-                icon_query=icon_query,
-            )
-        return LLMHeadingModel(
-            heading=self.heading,
-            description=self.description,
-        )
-
-
-class SlideContentModel(BaseModel):
-    title: str
-
-
-class Type1Content(SlideContentModel):
-    body: str
-    image_prompts: List[str]
-
-    def to_llm_content(self):
-        from ppt_generator.models.llm_models import LLMType1Content
-
-        return LLMType1Content(
-            title=self.title,
-            body=self.body,
-            image_prompt=self.image_prompts[0] if self.image_prompts else "",
-        )
-
-
-class Type2Content(SlideContentModel):
-    body: List[HeadingModel]
-
-    def to_llm_content(self):
-        from ppt_generator.models.llm_models import LLMType2Content
-
-        return LLMType2Content(
-            title=self.title,
-            body=[item.to_llm_content() for item in self.body],
-        )
-
-
-class Type3Content(SlideContentModel):
-    body: List[HeadingModel]
-    image_prompts: List[str]
-
-    def to_llm_content(self):
-        from ppt_generator.models.llm_models import LLMType3Content
-
-        return LLMType3Content(
-            title=self.title,
-            body=[item.to_llm_content() for item in self.body],
-            image_prompt=self.image_prompts[0] if self.image_prompts else "",
-        )
-
-
-class Type4Content(SlideContentModel):
-    body: List[HeadingModel]
-    image_prompts: List[str]
-
-    def to_llm_content(self):
-        from ppt_generator.models.llm_models import LLMType4Content
-
-        llm_body = []
-        for i, item in enumerate(self.body):
-            image_prompt = self.image_prompts[i] if i < len(self.image_prompts) else ""
-            llm_body.append(item.to_llm_content(image_prompt=image_prompt))
-        return LLMType4Content(
-            title=self.title,
-            body=llm_body,
-        )
-
-
-class Type5Content(SlideContentModel):
-    body: str
-    # table: TableModel
-    graph: GraphModel
-
-    def to_llm_content(self):
-        from ppt_generator.models.llm_models import LLMType5Content
-
-        return LLMType5Content(
-            title=self.title,
-            body=self.body,
-            # table=self.table,
-            graph=self.graph,
-        )
-
-
-class Type6Content(SlideContentModel):
-    description: str
-    body: List[HeadingModel]
-
-    def to_llm_content(self):
-        from ppt_generator.models.llm_models import LLMType6Content
-
-        return LLMType6Content(
-            title=self.title,
-            description=self.description,
-            body=[item.to_llm_content() for item in self.body],
-        )
-
-
-class Type7Content(SlideContentModel):
-    body: List[HeadingModel]
-    icon_queries: List[str]
-
-    def to_llm_content(self):
-        from ppt_generator.models.llm_models import LLMType7Content
-
-        llm_body = []
-        for i, item in enumerate(self.body):
-            icon_query = self.icon_queries[i] if i < len(self.icon_queries) else ""
-            llm_body.append(item.to_llm_content(icon_query=icon_query))
-        return LLMType7Content(
-            title=self.title,
-            body=llm_body,
-        )
-
-
-class Type8Content(SlideContentModel):
-    description: str
-    body: List[HeadingModel]
-    icon_queries: List[str]
-
-    def to_llm_content(self):
-        from ppt_generator.models.llm_models import LLMType8Content
-
-        llm_body = []
-        for i, item in enumerate(self.body):
-            icon_query = self.icon_queries[i] if i < len(self.icon_queries) else ""
-            llm_body.append(item.to_llm_content(icon_query=icon_query))
-        return LLMType8Content(
-            title=self.title,
-            description=self.description,
-            body=llm_body,
-        )
-
-
-class Type9Content(SlideContentModel):
-    body: List[HeadingModel]
-    # table: TableModel
-    graph: GraphModel
-
-    def to_llm_content(self):
-        from ppt_generator.models.llm_models import LLMType9Content
-
-        return LLMType9Content(
-            title=self.title,
-            body=[item.to_llm_content() for item in self.body],
-            # table=self.table,
-            graph=self.graph,
-        )
-
-
-ContentUnion = Union[
-    Type1Content,
-    Type2Content,
-    Type3Content,
-    Type4Content,
-    Type5Content,
-    Type6Content,
-    Type7Content,
-    Type8Content,
-    Type9Content,
-]
-
-CONTENT_TYPE_MAPPING: Mapping[int, ContentUnion] = {
-    TYPE1: Type1Content,
-    TYPE2: Type2Content,
-    TYPE3: Type3Content,
-    TYPE4: Type4Content,
-    TYPE5: Type5Content,
-    TYPE6: Type6Content,
-    TYPE7: Type7Content,
-    TYPE8: Type8Content,
-    TYPE9: Type9Content,
-}

servers/fastapi/ppt_generator/models/llm_models.py

@@ -1,220 +0,0 @@
-from typing import List, Mapping, Union
-from pydantic import BaseModel
-
-from graph_processor.models import GraphModel, LLMGraphModel
-from ppt_generator.models.content_type_models import (
-    HeadingModel,
-    TableDataModel,
-    TableModel,
-    TableType,
-    Type1Content,
-    Type2Content,
-    Type3Content,
-    Type4Content,
-    Type5Content,
-    Type6Content,
-    Type7Content,
-    Type8Content,
-    Type9Content,
-)
-from ppt_generator.models.other_models import (
-    TYPE1,
-    TYPE2,
-    TYPE3,
-    TYPE4,
-    TYPE5,
-    TYPE6,
-    TYPE7,
-    TYPE8,
-    TYPE9,
-)
-
-
-class LLMTableDataModel(TableDataModel):
-    x_labels: List[str]
-    y_labels: List[str]
-    data: List[List[float]]
-
-
-class LLMTableModel(TableModel):
-    name: str
-    type: TableType
-    data: LLMTableDataModel
-
-
-class LLMHeadingModel(BaseModel):
-    heading: str
-    description: str
-
-    def to_content(self) -> HeadingModel:
-        return HeadingModel(
-            heading=self.heading,
-            description=self.description,
-        )
-
-
-class LLMHeadingModelWithImagePrompt(LLMHeadingModel):
-    image_prompt: str
-
-    def to_content(self) -> HeadingModel:
-        return HeadingModel(
-            heading=self.heading,
-            description=self.description,
-        )
-
-
-class LLMHeadingModelWithIconQuery(LLMHeadingModel):
-    icon_query: str
-
-    def to_content(self) -> HeadingModel:
-        return HeadingModel(
-            heading=self.heading,
-            description=self.description,
-        )
-
-
-class LLMSlideContentModel(BaseModel):
-    title: str
-
-
-class LLMType1Content(LLMSlideContentModel):
-    body: str
-    image_prompt: str
-
-    def to_content(self) -> Type1Content:
-        return Type1Content(
-            title=self.title,
-            body=self.body,
-            image_prompts=[self.image_prompt],
-        )
-
-
-class LLMType2Content(LLMSlideContentModel):
-    body: List[LLMHeadingModel]
-
-    def to_content(self) -> Type2Content:
-        return Type2Content(
-            title=self.title,
-            body=[each.to_content() for each in self.body],
-        )
-
-
-class LLMType3Content(LLMSlideContentModel):
-    body: List[LLMHeadingModel]
-    image_prompt: str
-
-    def to_content(self) -> Type3Content:
-        return Type3Content(
-            title=self.title,
-            body=[each.to_content() for each in self.body],
-            image_prompts=[self.image_prompt],
-        )
-
-
-class LLMType4Content(LLMSlideContentModel):
-    body: List[LLMHeadingModelWithImagePrompt]
-
-    def to_content(self) -> Type4Content:
-        return Type4Content(
-            title=self.title,
-            body=[each.to_content() for each in self.body],
-            image_prompts=[each.image_prompt for each in self.body],
-        )
-
-
-class LLMType5Content(LLMSlideContentModel):
-    body: str
-    # table: LLMTableModel
-    graph: LLMGraphModel
-
-    def to_content(self) -> Type5Content:
-        return Type5Content(
-            title=self.title,
-            body=self.body,
-            # table=self.table,
-            graph=GraphModel.from_llm_graph_model(self.graph),
-        )
-
-
-class LLMType6Content(LLMSlideContentModel):
-    description: str
-    body: List[LLMHeadingModel]
-
-    def to_content(self) -> Type6Content:
-        return Type6Content(
-            title=self.title,
-            description=self.description,
-            body=[each.to_content() for each in self.body],
-        )
-
-
-class LLMType7Content(LLMSlideContentModel):
-    body: List[LLMHeadingModelWithIconQuery]
-
-    def to_content(self) -> Type7Content:
-        return Type7Content(
-            title=self.title,
-            body=[each.to_content() for each in self.body],
-            icon_queries=[each.icon_query for each in self.body],
-        )
-
-
-class LLMType8Content(LLMSlideContentModel):
-    description: str
-    body: List[LLMHeadingModelWithImagePrompt]
-
-    def to_content(self) -> Type8Content:
-        return Type8Content(
-            title=self.title,
-            description=self.description,
-            body=[each.to_content() for each in self.body],
-            icon_queries=[each.image_prompt for each in self.body],
-        )
-
-
-class LLMType9Content(LLMSlideContentModel):
-    body: List[LLMHeadingModel]
-    # table: LLMTableModel
-    graph: LLMGraphModel
-
-    def to_content(self) -> Type9Content:
-        return Type9Content(
-            title=self.title,
-            body=[each.to_content() for each in self.body],
-            # table=self.table,
-            graph=GraphModel.from_llm_graph_model(self.graph),
-        )
-
-
-LLMContentUnion = Union[
-    LLMType1Content,
-    LLMType2Content,
-    LLMType3Content,
-    LLMType4Content,
-    LLMType5Content,
-    LLMType6Content,
-    LLMType7Content,
-    LLMType8Content,
-    LLMType9Content,
-]
-
-LLM_CONTENT_TYPE_MAPPING: Mapping[int, LLMContentUnion] = {
-    TYPE1: LLMType1Content,
-    TYPE2: LLMType2Content,
-    TYPE3: LLMType3Content,
-    TYPE4: LLMType4Content,
-    TYPE5: LLMType5Content,
-    TYPE6: LLMType6Content,
-    TYPE7: LLMType7Content,
-    TYPE8: LLMType8Content,
-    TYPE9: LLMType9Content,
-}
-
-
-class LLMSlideModel(BaseModel):
-    type: int
-    content: LLMContentUnion
-
-
-class LLMPresentationModel(BaseModel):
-    slides: List[LLMSlideModel]

servers/fastapi/ppt_generator/models/llm_models_with_validations.py

@@ -1,232 +0,0 @@
-from typing import List, Mapping, Union
-from pydantic import Field
-
-from graph_processor.models import LLMGraphModel
-from ppt_generator.models.content_type_models import TableType
-from ppt_generator.models.other_models import (
-    TYPE1,
-    TYPE2,
-    TYPE3,
-    TYPE4,
-    TYPE5,
-    TYPE6,
-    TYPE7,
-    TYPE8,
-    TYPE9,
-)
-from ppt_generator.models.llm_models import (
-    LLMTableDataModel,
-    LLMTableModel,
-    LLMHeadingModel,
-    LLMHeadingModelWithImagePrompt,
-    LLMHeadingModelWithIconQuery,
-    LLMSlideContentModel,
-    LLMType1Content,
-    LLMType2Content,
-    LLMType3Content,
-    LLMType4Content,
-    LLMType5Content,
-    LLMType6Content,
-    LLMType7Content,
-    LLMType8Content,
-    LLMType9Content,
-    LLMSlideModel,
-    LLMPresentationModel,
-)
-
-
-class LLMTableDataModelWithValidation(LLMTableDataModel):
-    x_labels: List[str] = Field(
-        description="X labels of the table",
-        min_length=1,
-        max_length=5,
-    )
-    y_labels: List[str] = Field(
-        description="Y labels of the table",
-        min_length=1,
-        max_length=3,
-    )
-    data: List[List[float]] = Field(
-        description="Data of the table",
-        min_length=1,
-        max_length=5,
-    )
-
-
-class LLMTableModelWithValidation(LLMTableModel):
-    name: str = Field(
-        description="Name of the table in about 8 words",
-        min_length=10,
-        max_length=50,
-    )
-    type: TableType = Field(description="Type of the table")
-    data: LLMTableDataModelWithValidation
-
-
-class LLMHeadingModelWithValidation(LLMHeadingModel):
-    heading: str = Field(
-        description="Item heading in about 6 words",
-        min_length=10,
-        max_length=40,
-    )
-    description: str = Field(
-        description="Item description in about 12 words.",
-        min_length=50,
-        max_length=120,
-    )
-
-
-class LLMHeadingModelWithImagePromptWithValidation(LLMHeadingModelWithImagePrompt):
-    image_prompt: str = Field(
-        description="Item image prompt in about 10 words",
-        min_length=10,
-        max_length=100,
-    )
-
-
-class LLMHeadingModelWithIconQueryWithValidation(LLMHeadingModelWithIconQuery):
-    icon_query: str = Field(
-        description="Item icon query in about 4 words",
-        min_length=10,
-        max_length=40,
-    )
-
-
-class LLMSlideContentModelWithValidation(LLMSlideContentModel):
-    title: str = Field(
-        description="Slide title in about 8 words",
-        min_length=10,
-        max_length=80,
-    )
-
-
-class LLMType1ContentWithValidation(LLMType1Content):
-    body: str = Field(
-        description="Slide content summary in about 30 words.",
-        min_length=50,
-        max_length=300,
-    )
-    image_prompt: str = Field(
-        description="Slide image prompt in about 5 words",
-        min_length=10,
-        max_length=30,
-    )
-
-
-class LLMType2ContentWithValidation(LLMType2Content):
-    body: List[LLMHeadingModelWithValidation] = Field(
-        description="Items to show in slide",
-        min_length=1,
-        max_length=4,
-    )
-
-
-class LLMType3ContentWithValidation(LLMType3Content):
-    body: List[LLMHeadingModelWithValidation] = Field(
-        description="Items to show in slide",
-        min_length=3,
-        max_length=3,
-    )
-    image_prompt: str = Field(
-        description="Slide image prompt in about 5 words",
-        min_length=10,
-        max_length=30,
-    )
-
-
-class LLMType4ContentWithValidation(LLMType4Content):
-    body: List[LLMHeadingModelWithImagePromptWithValidation] = Field(
-        description="Items to show in slide",
-        min_length=1,
-        max_length=3,
-    )
-
-
-class LLMType5ContentWithValidation(LLMType5Content):
-    body: str = Field(
-        description="Slide content summary in about 30 words.",
-        min_length=50,
-        max_length=300,
-    )
-    # table: LLMTableModelWithValidation = Field(description="Table to show in slide")
-    graph: LLMGraphModel = Field(description="Graph to show in slide")
-
-
-class LLMType6ContentWithValidation(LLMType6Content):
-    description: str = Field(
-        description="Slide content summary in about 20 words.",
-        min_length=50,
-        max_length=300,
-    )
-    body: List[LLMHeadingModelWithValidation] = Field(
-        description="Items to show in slide",
-        min_length=1,
-        max_length=3,
-    )
-
-
-class LLMType7ContentWithValidation(LLMType7Content):
-    body: List[LLMHeadingModelWithIconQueryWithValidation] = Field(
-        description="Items to show in slide",
-        min_length=1,
-        max_length=4,
-    )
-
-
-class LLMType8ContentWithValidation(LLMType8Content):
-    description: str = Field(
-        description="Slide content summary in about 20 words.",
-        min_length=50,
-        max_length=300,
-    )
-    body: List[LLMHeadingModelWithImagePromptWithValidation] = Field(
-        description="Items to show in slide",
-        min_length=1,
-        max_length=3,
-    )
-
-
-class LLMType9ContentWithValidation(LLMType9Content):
-    body: List[LLMHeadingModelWithValidation] = Field(
-        description="Items to show in slide",
-        min_length=1,
-        max_length=3,
-    )
-    # table: LLMTableModelWithValidation = Field(description="Table to show in slide")
-    graph: LLMGraphModel = Field(description="Graph to show in slide")
-
-
-LLMContentUnionWithValidation = Union[
-    LLMType1ContentWithValidation,
-    LLMType2ContentWithValidation,
-    LLMType3ContentWithValidation,
-    LLMType4ContentWithValidation,
-    LLMType5ContentWithValidation,
-    LLMType6ContentWithValidation,
-    LLMType7ContentWithValidation,
-    LLMType8ContentWithValidation,
-    LLMType9ContentWithValidation,
-]
-
-LLM_CONTENT_TYPE_MAPPING_WITH_VALIDATION: Mapping[
-    int, LLMContentUnionWithValidation
-] = {
-    TYPE1: LLMType1ContentWithValidation,
-    TYPE2: LLMType2ContentWithValidation,
-    TYPE3: LLMType3ContentWithValidation,
-    TYPE4: LLMType4ContentWithValidation,
-    TYPE5: LLMType5ContentWithValidation,
-    TYPE6: LLMType6ContentWithValidation,
-    TYPE7: LLMType7ContentWithValidation,
-    TYPE8: LLMType8ContentWithValidation,
-    TYPE9: LLMType9ContentWithValidation,
-}
-
-
-class LLMSlideModelWithValidation(LLMSlideModel):
-    type: int
-    content: LLMContentUnionWithValidation
-
-
-class LLMPresentationModelWithValidation(LLMPresentationModel):
-    slides: List[LLMSlideModelWithValidation]

servers/fastapi/ppt_generator/models/other_models.py

@@ -1,24 +0,0 @@
-from pydantic import BaseModel, Field
-
-# 1. contains title, description and an image.
-TYPE1 = 1
-# 2. contains title and list of items.
-TYPE2 = 2
-# 3. contains title, list of items and an image.
-TYPE3 = 3
-# 4. contains title and list of items and multiple images.
-TYPE4 = 4
-# 5. contains title, description and a graph.
-TYPE5 = 5
-# 6. contains title, description and list of items.
-TYPE6 = 6
-# 7. contains title, list of items and icons.
-TYPE7 = 7
-# 8. contains title, description, list of items and icons.
-TYPE8 = 8
-# 9. contains title, list of items and a graph.
-TYPE9 = 9
-
-
-class SlideTypeModel(BaseModel):
-    slide_type: int = Field(gte=1, lte=9, description="Slide type from 1 to 9")

servers/fastapi/ppt_generator/models/slide_model.py

@@ -1,66 +0,0 @@
-import uuid
-from typing import List, Optional
-from pydantic import BaseModel
-
-from ppt_generator.models.content_type_models import (
-    CONTENT_TYPE_MAPPING,
-    Type1Content,
-    Type2Content,
-    Type3Content,
-    Type4Content,
-    Type5Content,
-    Type6Content,
-    Type7Content,
-    Type8Content,
-    Type9Content,
-)
-
-
-class SlideModel(BaseModel):
-    id: Optional[str] = None
-    index: int
-    type: int
-    design_index: Optional[int] = None
-    images: Optional[List[str]] = None
-    icons: Optional[List[str]] = None
-    presentation: str
-    content: (
-        Type1Content
-        | Type2Content
-        | Type3Content
-        | Type4Content
-        | Type5Content
-        | Type6Content
-        | Type7Content
-        | Type8Content
-        | Type9Content
-    )
-    properties: Optional[dict] = None
-
-    @classmethod
-    def from_dict(cls, data):
-        slide_model = cls(**data)
-        content = data["content"]
-        slide_model.content = CONTENT_TYPE_MAPPING[slide_model.type](**content)
-        return slide_model
-
-    def to_create_dict(self, auto_id: bool = False):
-        temp = self.model_dump(mode="json")
-        if not self.id:
-            if auto_id:
-                temp["id"] = str(uuid.uuid4())
-            else:
-                temp.pop("id")
-        return temp
-
-    @property
-    def images_count(self):
-        if not hasattr(self.content, "image_prompts"):
-            return 0
-        return len(self.content.image_prompts or [])
-
-    @property
-    def icons_count(self):
-        if not hasattr(self.content, "icon_queries"):
-            return 0
-        return len(self.content.icon_queries or [])

servers/fastapi/server.py

@@ -4,12 +4,19 @@ import argparse
 
 
 if __name__ == "__main__":
-    os.makedirs("debug", exist_ok=True)
-
     parser = argparse.ArgumentParser(description="Run the FastAPI server")
     parser.add_argument(
         "--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"
+    )
     args = parser.parse_args()
 
-    uvicorn.run("api.main:app", host="0.0.0.0", port=args.port, log_level="info")
+    uvicorn.run(
+        "api.main:APP",
+        host="0.0.0.0",
+        port=args.port,
+        log_level="info",
+        reload=args.reload,
+    )

servers/fastapi/services/__init__.py

@@ -0,0 +1,6 @@
+from services.temp_file_service import TempFileService
+from services.database import sql_engine
+
+
+TEMP_FILE_SERVICE = TempFileService()
+SQL_ENGINE = sql_engine

servers/fastapi/services/database.py

@@ -0,0 +1,25 @@
+from contextlib import contextmanager
+import os
+from sqlalchemy import create_engine
+from sqlmodel import Session
+
+from utils.get_env import get_app_data_directory_env, get_database_url_env
+
+
+database_url = get_database_url_env() or "sqlite:///" + os.path.join(
+    get_app_data_directory_env(), "fastapi.db"
+)
+connect_args = {}
+if "sqlite" in database_url:
+    connect_args["check_same_thread"] = False
+
+sql_engine = create_engine(database_url, connect_args=connect_args)
+
+
+@contextmanager
+def get_sql_session():
+    session = Session(sql_engine)
+    try:
+        yield session
+    finally:
+        session.close()

servers/fastapi/services/documents_loader.py

@@ -0,0 +1,120 @@
+from http.client import HTTPException
+import mimetypes
+import os, pdfplumber, asyncio
+from typing import List, Tuple
+from docx import Document
+from pptx import Presentation
+
+from constants.documents import (
+    PDF_MIME_TYPES,
+    POWERPOINT_TYPES,
+    TEXT_MIME_TYPES,
+    WORD_TYPES,
+)
+
+
+class DocumentsLoader:
+
+    def __init__(self, documents: List[str]):
+        self._document_paths = documents
+
+        self._documents: List[str] = []
+        self._images: List[List[str]] = []
+
+    @property
+    def documents(self):
+        return self._documents
+
+    @property
+    def images(self):
+        return self._images
+
+    async def load_documents(
+        self,
+        temp_dir: str,
+        load_text: bool = True,
+        load_images: bool = False,
+    ):
+        documents: List[str] = []
+        images: List[str] = []
+
+        for file_path in self._document_paths:
+            if not os.path.exists(file_path):
+                raise HTTPException(
+                    status_code=404, detail=f"File {file_path} not found"
+                )
+
+            document = ""
+            imgs = []
+
+            mime_type = mimetypes.guess_type(file_path)[0]
+            if mime_type in PDF_MIME_TYPES:
+                document, imgs = await self.load_pdf(
+                    file_path, load_text, load_images, temp_dir
+                )
+            elif mime_type in TEXT_MIME_TYPES:
+                document = await self.load_text(file_path)
+            elif mime_type in POWERPOINT_TYPES:
+                document = self.load_powerpoint(file_path)
+            elif mime_type in WORD_TYPES:
+                document = self.load_msword(file_path)
+
+            documents.append(document)
+            images.append(imgs)
+
+        self._documents = documents
+        self._images = images
+
+    async def load_pdf(
+        self,
+        file_path: str,
+        load_text: bool,
+        load_images: bool,
+        temp_dir: str,
+    ) -> Tuple[str, List[str]]:
+        image_paths = []
+        document: str = ""
+
+        if load_text:
+            with pdfplumber.open(file_path) as pdf:
+                for page in pdf.pages:
+                    document += await asyncio.to_thread(page.extract_text)
+
+        if load_images:
+            image_paths = await self.get_page_images_from_pdf_async(file_path, temp_dir)
+
+        return document, image_paths
+
+    async def load_text(self, file_path: str) -> str:
+        with open(file_path, "r") as file:
+            return await asyncio.to_thread(file.read)
+
+    def load_msword(self, file_path: str) -> str:
+        document = Document(file_path)
+        text = "\n".join([paragraph.text for paragraph in document.paragraphs])
+        return text
+
+    def load_powerpoint(self, file_path: str) -> str:
+        presentation = Presentation(file_path)
+
+        extracted_text = ""
+        for index, slide in enumerate(presentation.slides):
+            extracted_text += f"# Slide {index + 1}\n"
+            for shape in slide.shapes:
+                if shape.has_text_frame:
+                    for paragraph in shape.text_frame.paragraphs:
+                        extracted_text += f"{paragraph.text}\n"
+                    extracted_text += "\n"
+            extracted_text += "\n\n"
+        return extracted_text
+
+    def get_page_images_from_pdf(self, file_path: str, temp_dir: str):
+        with pdfplumber.open(file_path) as pdf:
+            for page in pdf.pages:
+                img = page.to_image(resolution=300)
+                img.save(os.path.join(temp_dir, f"page_{page.page_number}.png"))
+
+    async def get_page_images_from_pdf_async(self, file_path: str, temp_dir: str):
+        return await asyncio.to_thread(
+            self.get_page_images_from_pdf, file_path, temp_dir
+        )

servers/fastapi/services/temp_file_service.py

@@ -0,0 +1,65 @@
+import os
+import uuid
+from typing import Optional, Union
+
+
+class TempFileService:
+
+    def __init__(self):
+        self.base_dir = os.getenv("TEMP_DIRECTORY")
+        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:
+        temp_dir = os.path.join(base_dir, dir_name if dir_name else str(uuid.uuid4()))
+        os.makedirs(temp_dir, exist_ok=True)
+        return temp_dir
+
+    def create_temp_dir(self, dir_name: Optional[str] = None) -> str:
+        return self.create_dir_in_dir(self.base_dir, dir_name)
+
+    def create_temp_file_path(
+        self, file_path: str, dir_path: Optional[str] = None
+    ) -> str:
+        if dir_path is None:
+            dir_path = self.base_dir
+
+        full_path = os.path.join(dir_path, file_path)
+
+        os.makedirs(os.path.dirname(full_path), exist_ok=True)
+        return full_path
+
+    def create_temp_file(
+        self, file_path: str, content: Union[bytes, str], dir_path: Optional[str] = None
+    ) -> str:
+        file_path = self.create_temp_file_path(file_path, dir_path)
+        mode = "wb" if isinstance(content, bytes) else "w"
+        with open(file_path, mode) as f:
+            f.write(content)
+
+        return file_path
+
+    def read_temp_file(self, file_path: str, binary: bool = True) -> Union[bytes, str]:
+        mode = "rb" if binary else "r"
+        with open(file_path, mode) as f:
+            return f.read()
+
+    def cleanup_temp_file(self, file_path: str):
+        if os.path.exists(file_path):
+            os.remove(file_path)
+
+    def delete_dir_files(self, dir_path: str):
+        if os.path.exists(dir_path):
+            for root, dirs, files in os.walk(dir_path, topdown=False):
+                for name in files:
+                    os.remove(os.path.join(root, name))
+                for name in dirs:
+                    os.rmdir(os.path.join(root, name))
+
+    def cleanup_temp_dir(self, dir_path: str):
+        if os.path.exists(dir_path):
+            self.delete_dir_files(dir_path)
+            os.rmdir(dir_path)
+
+    def cleanup_base_dir(self):
+        self.cleanup_temp_dir(self.base_dir)

servers/fastapi/utils/custom_llm_provider.py

@@ -0,0 +1,18 @@
+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/get_env.py

@@ -0,0 +1,57 @@
+import os
+
+
+def get_can_change_keys_env():
+    return os.getenv("CAN_CHANGE_KEYS")
+
+
+def get_database_url_env():
+    return os.getenv("DATABASE_URL")
+
+
+def get_app_data_directory_env():
+    return os.getenv("APP_DATA_DIRECTORY")
+
+
+def get_temp_directory_env():
+    return os.getenv("TEMP_DIRECTORY")
+
+
+def get_user_config_path_env():
+    return os.getenv("USER_CONFIG_PATH")
+
+
+def get_llm_provider_env():
+    return os.getenv("LLM")
+
+
+def get_ollama_url_env():
+    return os.getenv("OLLAMA_URL")
+
+
+def get_custom_llm_url_env():
+    return os.getenv("CUSTOM_URL")
+
+
+def get_openai_api_key_env():
+    return os.getenv("OPENAI_API_KEY")
+
+
+def get_google_api_key_env():
+    return os.getenv("GOOGLE_API_KEY")
+
+
+def get_custom_llm_api_key_env():
+    return os.getenv("CUSTOM_LLM_API_KEY")
+
+
+def get_ollama_model_env():
+    return os.getenv("OLLAMA_MODEL")
+
+
+def get_custom_model_env():
+    return os.getenv("CUSTOM_MODEL")
+
+
+def get_pexels_api_key_env():
+    return os.getenv("PEXELS_API_KEY")

servers/fastapi/utils/llm_provider.py

@@ -0,0 +1,66 @@
+from http.client import HTTPException
+import os
+from openai import AsyncOpenAI
+
+from enums.llm_provider import LLMProvider
+from utils.get_env import (
+    get_custom_llm_api_key_env,
+    get_custom_llm_url_env,
+    get_google_api_key_env,
+    get_llm_provider_env,
+    get_ollama_url_env,
+    get_openai_api_key_env,
+)
+
+
+def get_llm_provider():
+    return LLMProvider(get_llm_provider_env())
+
+
+def get_ollama_url():
+    return get_ollama_url_env() or "http://localhost:11434"
+
+
+def is_ollama_selected():
+    return get_llm_provider() == LLMProvider.OLLAMA
+
+
+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():
+    selected_llm = get_llm_provider()
+    if selected_llm == LLMProvider.OPENAI:
+        return get_openai_api_key_env()
+    elif selected_llm == LLMProvider.GOOGLE:
+        return get_google_api_key_env()
+    elif selected_llm == LLMProvider.OLLAMA:
+        return "ollama"
+    elif selected_llm == LLMProvider.CUSTOM:
+        return get_custom_llm_api_key_env() or "none"
+    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(),
+    )
+    return client

servers/fastapi/utils/ollama.py

@@ -0,0 +1,60 @@
+from http.client import HTTPException
+import json
+from typing import AsyncGenerator
+import aiohttp
+
+from models.ollama_model_status_response import OllamaModelStatusResponse
+from utils.get_env import get_ollama_url_env
+
+
+async def pull_ollama_model(model: str) -> AsyncGenerator[dict, None]:
+    async with aiohttp.ClientSession() as session:
+        async with session.post(
+            f"{get_ollama_url_env()}/api/pull",
+            json={"model": model},
+        ) as response:
+            if response.status != 200:
+                raise HTTPException(
+                    status_code=response.status,
+                    detail=f"Failed to pull model: {await response.text()}",
+                )
+
+            async for line in response.content:
+                if not line.strip():
+                    continue
+
+                try:
+                    event = json.loads(line.decode("utf-8"))
+                except json.JSONDecodeError:
+                    continue
+
+                yield event
+
+
+async def list_pulled_ollama_models() -> list[OllamaModelStatusResponse]:
+    async with aiohttp.ClientSession() as session:
+        async with session.get(
+            f"{get_ollama_url_env()}/api/tags",
+        ) as response:
+            if response.status == 200:
+                pulled_models = await response.json()
+                return [
+                    OllamaModelStatusResponse(
+                        name=m["model"],
+                        size=m["size"],
+                        status="pulled",
+                        downloaded=m["size"],
+                        done=True,
+                    )
+                    for m in pulled_models["models"]
+                ]
+            elif response.status == 403:
+                raise HTTPException(
+                    status_code=403,
+                    detail="Forbidden: Please check your Ollama Configuration",
+                )
+            else:
+                raise HTTPException(
+                    status_code=response.status,
+                    detail=f"Failed to list Ollama models: {response.status}",
+                )

servers/fastapi/utils/set_env.py

@@ -0,0 +1,45 @@
+import os
+
+
+def set_temp_directory_env(value):
+    os.environ["TEMP_DIRECTORY"] = value
+
+
+def set_user_config_path_env(value):
+    os.environ["USER_CONFIG_PATH"] = value
+
+
+def set_llm_provider_env(value):
+    os.environ["LLM"] = value
+
+
+def set_ollama_url_env(value):
+    os.environ["OLLAMA_URL"] = value
+
+
+def set_custom_llm_url_env(value):
+    os.environ["CUSTOM_URL"] = value
+
+
+def set_openai_api_key_env(value):
+    os.environ["OPENAI_API_KEY"] = value
+
+
+def set_google_api_key_env(value):
+    os.environ["GOOGLE_API_KEY"] = value
+
+
+def set_custom_llm_api_key_env(value):
+    os.environ["CUSTOM_LLM_API_KEY"] = value
+
+
+def set_ollama_model_env(value):
+    os.environ["OLLAMA_MODEL"] = value
+
+
+def set_custom_model_env(value):
+    os.environ["CUSTOM_MODEL"] = value
+
+
+def set_pexels_api_key_env(value):
+    os.environ["PEXELS_API_KEY"] = value

servers/fastapi/utils/user_config.py

@@ -0,0 +1,75 @@
+import os
+import json
+
+from models.user_config import UserConfig
+from utils.get_env import (
+    get_custom_llm_api_key_env,
+    get_custom_llm_url_env,
+    get_custom_model_env,
+    get_google_api_key_env,
+    get_llm_provider_env,
+    get_ollama_model_env,
+    get_ollama_url_env,
+    get_openai_api_key_env,
+    get_pexels_api_key_env,
+    get_user_config_path_env,
+)
+from utils.set_env import (
+    set_custom_llm_api_key_env,
+    set_custom_llm_url_env,
+    set_custom_model_env,
+    set_google_api_key_env,
+    set_llm_provider_env,
+    set_ollama_model_env,
+    set_ollama_url_env,
+    set_openai_api_key_env,
+    set_pexels_api_key_env,
+)
+
+
+def get_user_config():
+    user_config_path = get_user_config_path_env()
+
+    existing_config = UserConfig()
+    try:
+        if os.path.exists(user_config_path):
+            with open(user_config_path, "r") as f:
+                existing_config = UserConfig(**json.load(f))
+    except Exception as e:
+        print("Error while loading user config")
+        pass
+
+    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(),
+        GOOGLE_API_KEY=existing_config.GOOGLE_API_KEY or get_google_api_key_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(),
+        CUSTOM_LLM_API_KEY=existing_config.CUSTOM_LLM_API_KEY
+        or get_custom_llm_api_key_env(),
+        CUSTOM_MODEL=existing_config.CUSTOM_MODEL or get_custom_model_env(),
+        PEXELS_API_KEY=existing_config.PEXELS_API_KEY or get_pexels_api_key_env(),
+    )
+
+
+def update_env_with_user_config():
+    user_config = get_user_config()
+    if user_config.LLM:
+        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.GOOGLE_API_KEY:
+        set_google_api_key_env(user_config.GOOGLE_API_KEY)
+    if user_config.OLLAMA_URL:
+        set_ollama_url_env(user_config.OLLAMA_URL)
+    if user_config.OLLAMA_MODEL:
+        set_ollama_model_env(user_config.OLLAMA_MODEL)
+    if user_config.CUSTOM_LLM_URL:
+        set_custom_llm_url_env(user_config.CUSTOM_LLM_URL)
+    if user_config.CUSTOM_LLM_API_KEY:
+        set_custom_llm_api_key_env(user_config.CUSTOM_LLM_API_KEY)
+    if user_config.CUSTOM_MODEL:
+        set_custom_model_env(user_config.CUSTOM_MODEL)
+    if user_config.PEXELS_API_KEY:
+        set_pexels_api_key_env(user_config.PEXELS_API_KEY)

servers/fastapi/utils/validators.py

@@ -0,0 +1,27 @@
+from http.client import HTTPException
+from typing import List
+
+from fastapi import UploadFile
+
+
+def validate_files(
+    field,
+    nullable: bool,
+    multiple: bool,
+    max_size: int,
+    accepted_types: List[str],
+):
+
+    if field:
+        files: List[UploadFile] = field if multiple else [field]
+        for each_file in files:
+            if (max_size * 1024 * 1024) < each_file.size:
+                raise HTTPException(
+                    400,
+                    f"File '{each_file.filename}' exceeded max upload size of {max_size} MB",
+                )
+            elif each_file.content_type not in accepted_types:
+                raise HTTPException(400, f"File '{each_file.filename}' not accepted.")
+
+    elif not (field or nullable):
+        raise HTTPException(400, "File must be provided.")

servers/fastapi_old/api/main.py

@@ -0,0 +1,97 @@
+import os
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from sqlmodel import SQLModel
+from contextlib import asynccontextmanager
+
+from api.models import SelectedLLMProvider
+from api.routers.presentation.router import presentation_router
+from api.services.database import sql_engine
+from api.utils.supported_ollama_models import SUPPORTED_OLLAMA_MODELS
+from api.utils.utils import update_env_with_user_config
+from api.utils.model_utils import (
+    get_selected_llm_provider,
+    is_custom_llm_selected,
+    is_ollama_selected,
+    list_available_custom_models,
+    pull_ollama_model,
+)
+
+can_change_keys = os.getenv("CAN_CHANGE_KEYS") != "false"
+
+
+async def check_llm_model_availability():
+    if not can_change_keys:
+        if get_selected_llm_provider() == SelectedLLMProvider.OPENAI:
+            openai_api_key = os.getenv("OPENAI_API_KEY")
+            if not openai_api_key:
+                raise Exception("OPENAI_API_KEY must be provided")
+
+        elif get_selected_llm_provider() == SelectedLLMProvider.GOOGLE:
+            google_api_key = os.getenv("GOOGLE_API_KEY")
+            if not google_api_key:
+                raise Exception("GOOGLE_API_KEY must be provided")
+
+        elif is_ollama_selected():
+            ollama_model = os.getenv("OLLAMA_MODEL")
+            if not ollama_model:
+                raise Exception("OLLAMA_MODEL must be provided")
+
+            if ollama_model not in SUPPORTED_OLLAMA_MODELS:
+                raise Exception(f"Model {ollama_model} is not supported")
+
+            print("-" * 50)
+            print("Pulling model: ", ollama_model)
+            async for event in pull_ollama_model(ollama_model):
+                print(event)
+            print("Pulled model: ", ollama_model)
+            print("-" * 50)
+
+        elif is_custom_llm_selected():
+            custom_model = os.getenv("CUSTOM_MODEL")
+            custom_llm_url = os.getenv("CUSTOM_LLM_URL")
+            custom_llm_api_key = os.getenv("CUSTOM_LLM_API_KEY")
+            if not custom_model:
+                raise Exception("CUSTOM_MODEL must be provided")
+            if not custom_llm_url:
+                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(
+                custom_llm_url, custom_llm_api_key
+            )
+            print("Available models: ", models)
+            print("-" * 50)
+            if custom_model not in models:
+                raise Exception(f"Model {custom_model} is not available")
+
+
+@asynccontextmanager
+async def lifespan(_: FastAPI):
+    os.makedirs(os.getenv("APP_DATA_DIRECTORY"), exist_ok=True)
+    SQLModel.metadata.create_all(sql_engine)
+    await check_llm_model_availability()
+    yield
+
+
+app = FastAPI(lifespan=lifespan)
+origins = ["*"]
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.middleware("http")
+async def update_env_middleware(request: Request, call_next):
+    if can_change_keys:
+        update_env_with_user_config()
+    return await call_next(request)
+
+
+app.include_router(presentation_router)

servers/fastapi_old/api/routers/presentation/handlers/edit.py

@@ -23,7 +23,6 @@ from ppt_generator.models.query_and_prompt_models import (
     IconQueryCollectionWithData,
     ImagePromptWithThemeAndAspectRatio,
 )
-from ppt_generator.models.slide_model import SlideModel
 from ppt_generator.slide_generator import (
     get_edited_slide_content_model,
     get_slide_type_from_prompt,
@@ -64,7 +63,7 @@ class PresentationEditHandler:
                 )
             ).first()
 
-        slide_to_edit = SlideModel.from_dict(slide_to_edit_sql.model_dump(mode="json"))
+        slide_to_edit = SlideSqlModel(**slide_to_edit_sql.model_dump(mode="json"))
         new_slide_type = await get_slide_type_from_prompt(self.prompt, slide_to_edit)
         new_slide_type = new_slide_type.slide_type
 
@@ -87,7 +86,7 @@ class PresentationEditHandler:
             presentation.language,
         )
 
-        new_slide_model = SlideModel(
+        new_slide_model = SlideSqlModel(
             id=slide_to_edit.id,
             index=slide_to_edit.index,
             type=new_slide_type,

servers/fastapi_old/api/routers/presentation/handlers/list_presentation_layouts.py

@@ -0,0 +1,16 @@
+from sqlmodel import select
+from api.models import LogMetadata
+from api.services.database import get_sql_session
+from api.services.logging import LoggingService
+from api.sql_models import PresentationLayoutSqlModel
+
+
+class ListPresentationLayoutsHandler:
+
+    def __init__(self):
+        pass
+
+    async def get(self, logging_service: LoggingService, log_metadata: LogMetadata):
+        with get_sql_session() as sql_session:
+            layouts = sql_session.exec(select(PresentationLayoutSqlModel)).all()
+        return layouts

servers/fastapi_old/api/routers/presentation/handlers/list_slide_layouts.py

@@ -0,0 +1,13 @@
+from sqlmodel import select
+from api.models import LogMetadata
+from api.services.database import get_sql_session
+from api.services.logging import LoggingService
+from api.sql_models import SlideLayoutSqlModel
+
+
+class ListSlideLayoutsHandler:
+
+    async def get(self, logging_service: LoggingService, log_metadata: LogMetadata):
+        with get_sql_session() as sql_session:
+            layouts = sql_session.exec(select(SlideLayoutSqlModel)).all()
+        return layouts

servers/fastapi_old/api/routers/presentation/handlers/save_presentation_layouts_handler.py

@@ -0,0 +1,19 @@
+from api.models import LogMetadata
+from api.routers.presentation.models import SavePresentationLayoutsRequest
+from api.services.database import get_sql_session
+from api.services.logging import LoggingService
+
+
+class SavePresentationLayoutsHandler:
+
+    def __init__(self, data: SavePresentationLayoutsRequest):
+        self.data = data
+
+    async def post(self, logging_service: LoggingService, log_metadata: LogMetadata):
+
+        with get_sql_session() as sql_session:
+            for layout in self.data.layouts:
+                sql_session.merge(layout)
+            sql_session.commit()
+
+        return self.data

servers/fastapi_old/api/routers/presentation/handlers/save_slide_layouts_handler.py

@@ -0,0 +1,19 @@
+from api.models import LogMetadata
+from api.routers.presentation.models import SaveSlideLayoutsRequest
+from api.services.database import get_sql_session
+from api.services.logging import LoggingService
+
+
+class SaveSlideLayoutsHandler:
+
+    def __init__(self, data: SaveSlideLayoutsRequest):
+        self.data = data
+
+    async def post(self, logging_service: LoggingService, log_metadata: LogMetadata):
+
+        with get_sql_session() as sql_session:
+            for layout in self.data.layouts:
+                sql_session.merge(layout)
+            sql_session.commit()
+
+        return self.data

servers/fastapi_old/api/routers/presentation/models.py

@@ -11,8 +11,12 @@ from ppt_generator.models.query_and_prompt_models import (
     IconCategoryEnum,
     ImagePromptWithThemeAndAspectRatio,
 )
-from ppt_generator.models.slide_model import SlideModel
-from api.sql_models import PresentationSqlModel, SlideSqlModel
+from api.sql_models import (
+    PresentationLayoutSqlModel,
+    PresentationSqlModel,
+    SlideLayoutSqlModel,
+    SlideSqlModel,
+)
 from ollama._types import ModelDetails
 
 
@@ -126,7 +130,7 @@ class PresentationAndSlides(BaseModel):
 
 class PresentationUpdateRequest(BaseModel):
     presentation_id: str
-    slides: List[SlideModel]
+    slides: List[SlideSqlModel]
 
 
 class PresentationAndUrl(BaseModel):
@@ -197,3 +201,11 @@ class PresentationWithOneSlide(BaseModel):
             title=presentation.title,
             slide=slide,
         )
+
+
+class SaveSlideLayoutsRequest(BaseModel):
+    layouts: List[SlideLayoutSqlModel]
+
+
+class SavePresentationLayoutsRequest(BaseModel):
+    layouts: List[PresentationLayoutSqlModel]

servers/fastapi_old/api/routers/presentation/router.py

@@ -40,10 +40,20 @@ from api.routers.presentation.handlers.list_available_custom_models import (
 from api.routers.presentation.handlers.list_ollama_pulled_models import (
     ListPulledOllamaModelsHandler,
 )
+from api.routers.presentation.handlers.list_presentation_layouts import (
+    ListPresentationLayoutsHandler,
+)
+from api.routers.presentation.handlers.list_slide_layouts import ListSlideLayoutsHandler
 from api.routers.presentation.handlers.list_supported_ollama_models import (
     ListSupportedOllamaModelsHandler,
 )
 from api.routers.presentation.handlers.pull_ollama_model import PullOllamaModelHandler
+from api.routers.presentation.handlers.save_presentation_layouts_handler import (
+    SavePresentationLayoutsHandler,
+)
+from api.routers.presentation.handlers.save_slide_layouts_handler import (
+    SaveSlideLayoutsHandler,
+)
 from api.routers.presentation.handlers.search_icon import SearchIconHandler
 from api.routers.presentation.handlers.search_image import SearchImageHandler
 from api.routers.presentation.handlers.update_parsed_document import (
@@ -78,15 +88,21 @@ from api.routers.presentation.models import (
     PresentationAndUrls,
     PresentationGenerateRequest,
     PresentationPathAndEditPath,
+    SavePresentationLayoutsRequest,
+    SaveSlideLayoutsRequest,
     SearchIconRequest,
     SearchImageRequest,
     UpdatePresentationThemeRequest,
     PresentationUpdateRequest,
     PresentationWithOneSlide,
 )
-from api.sql_models import PresentationSqlModel
+from api.sql_models import (
+    PresentationLayoutSqlModel,
+    PresentationSqlModel,
+    SlideLayoutSqlModel,
+    SlideSqlModel,
+)
 from api.utils.utils import handle_errors
-from ppt_generator.models.slide_model import SlideModel
 
 route_prefix = "/api/v1/ppt"
 presentation_router = APIRouter(prefix=route_prefix)
@@ -248,7 +264,7 @@ async def update_presentation(
     )
 
 
-@presentation_router.post("/edit", response_model=SlideModel)
+@presentation_router.post("/edit", response_model=SlideSqlModel)
 async def update_presentation(
     data: EditPresentationSlideRequest,
 ):
@@ -404,3 +420,51 @@ async def list_custom_models(
         logging_service,
         log_metadata,
     )
+
+
+@presentation_router.get(
+    "/layout/slides/list", response_model=List[SlideLayoutSqlModel]
+)
+async def list_slide_layouts():
+    request_utils = RequestUtils(f"{route_prefix}/layout/slides/list")
+    logging_service, log_metadata = await request_utils.initialize_logger()
+    return await handle_errors(
+        ListSlideLayoutsHandler().get,
+        logging_service,
+        log_metadata,
+    )
+
+
+@presentation_router.post("/layout/slides/save")
+async def save_slide_layouts(data: SaveSlideLayoutsRequest):
+    request_utils = RequestUtils(f"{route_prefix}/layout/slides/save")
+    logging_service, log_metadata = await request_utils.initialize_logger()
+    return await handle_errors(
+        SaveSlideLayoutsHandler(data).post,
+        logging_service,
+        log_metadata,
+    )
+
+
+@presentation_router.get(
+    "/layout/presentations/list", response_model=List[PresentationLayoutSqlModel]
+)
+async def list_presentation_layouts():
+    request_utils = RequestUtils(f"{route_prefix}/layout/presentations/list")
+    logging_service, log_metadata = await request_utils.initialize_logger()
+    return await handle_errors(
+        ListPresentationLayoutsHandler().get,
+        logging_service,
+        log_metadata,
+    )
+
+
+@presentation_router.post("/layout/presentations/save")
+async def save_presentation_layouts(data: SavePresentationLayoutsRequest):
+    request_utils = RequestUtils(f"{route_prefix}/layout/presentations/save")
+    logging_service, log_metadata = await request_utils.initialize_logger()
+    return await handle_errors(
+        SavePresentationLayoutsHandler(data).post,
+        logging_service,
+        log_metadata,
+    )

servers/fastapi_old/api/sql_models.py

@@ -8,13 +8,25 @@ def get_random_uuid() -> str:
     return str(uuid.uuid4())
 
 
+class SlideLayoutSqlModel(SQLModel, table=True):
+    id: str = Field(primary_key=True)
+    description: Optional[str] = None
+    json_schema: dict = Field(sa_column=Column(JSON, nullable=False))
+
+
+class PresentationLayoutSqlModel(SQLModel, table=True):
+    id: str = Field(default_factory=get_random_uuid, primary_key=True)
+    name: str
+    description: Optional[str] = None
+    slide_layouts: List[str] = Field(sa_column=Column(JSON, nullable=False))
+
+
 class PresentationSqlModel(SQLModel, table=True):
     id: str = Field(default_factory=get_random_uuid, primary_key=True)
     created_at: datetime = Field(default=datetime.now())
     prompt: Optional[str] = None
     n_slides: int
     theme: Optional[dict] = Field(sa_column=Column(JSON, nullable=True), default=None)
-    file: Optional[str] = None
     title: Optional[str] = None
     structure: Optional[dict] = Field(
         sa_column=Column(JSON, nullable=True), default=None
@@ -27,21 +39,13 @@ class PresentationSqlModel(SQLModel, table=True):
     )
     language: Optional[str] = None
     summary: Optional[str] = None
-    thumbnail: Optional[str] = None
     data: Optional[dict] = Field(sa_column=Column(JSON, nullable=True), default=None)
 
 
 class SlideSqlModel(SQLModel, table=True):
     id: str = Field(default_factory=get_random_uuid, primary_key=True)
     index: int = Field(index=True)
-    type: int
-    design_index: Optional[int] = None
-    images: Optional[List[str]] = Field(
-        sa_column=Column(JSON, nullable=True), default=None
-    )
-    icons: Optional[List[str]] = Field(
-        sa_column=Column(JSON, nullable=True), default=None
-    )
+    layout: str
     presentation: str
     content: dict = Field(sa_column=Column(JSON, nullable=False), default=None)
     properties: Optional[dict] = Field(

servers/fastapi_old/api/utils/utils.py

@@ -160,8 +160,8 @@ async def handle_errors(
         )
         raise e
     except Exception as e:
-        print(traceback.print_stack())
-        print(traceback.print_exc())
+        traceback.print_stack()
+        traceback.print_exc()
 
         log_metadata.status_code = 400
         logging_service.logger.critical(