93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
import asyncio
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import json
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from google.genai.types import GenerateContentConfig
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from models.presentation_layout import SlideLayoutModel
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from models.presentation_outline_model import SlideOutlineModel
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from utils.llm_provider import (
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get_google_llm_client,
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get_llm_client,
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get_nano_model,
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is_google_selected,
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)
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from utils.schema_utils import remove_fields_from_schema
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system_prompt = """
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Generate structured slide based on provided title and outline, follow mentioned steps and notes and provide structured output.
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# Steps
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1. Analyze the outline and title.
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2. Generate structured slide based on the outline and title.
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# Notes
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- Slide body should not use words like "This slide", "This presentation".
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- Rephrase the slide body to make it flow naturally.
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- Provide prompt to generate image on "__image_prompt__" property.
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- Provide query to search icon on "__icon_query__" property.
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- Do not use markdown formatting in slide body.
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- **Strictly follow the max and min character limit for every property in the slide.**
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"""
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def get_user_prompt(title: str, outline: str):
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return f"""
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## Slide Title
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{title}
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## Slide Outline
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{outline}
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"""
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def get_prompt_to_generate_slide_content(title: str, outline: str):
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return [
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{
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"role": "system",
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"content": system_prompt,
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},
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{
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"role": "user",
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"content": get_user_prompt(title, outline),
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},
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]
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async def get_slide_content_from_type_and_outline(
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slide_layout: SlideLayoutModel, outline: SlideOutlineModel
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):
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model = get_nano_model()
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response_schema = remove_fields_from_schema(
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slide_layout.json_schema, ["__image_url__", "__icon_url__"]
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)
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if not is_google_selected():
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client = get_llm_client()
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response = await client.beta.chat.completions.parse(
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model=model,
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messages=get_prompt_to_generate_slide_content(
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outline.title,
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outline.body,
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),
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response_format={
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"type": "json_schema",
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"json_schema": {
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"name": "SlideContent",
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"schema": response_schema,
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},
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},
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)
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return json.loads(response.choices[0].message.content)
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else:
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client = get_google_llm_client()
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response = await asyncio.to_thread(
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client.models.generate_content,
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model=model,
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contents=[get_user_prompt(outline.title, outline.body)],
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config=GenerateContentConfig(
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system_instruction=system_prompt,
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response_mime_type="application/json",
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response_json_schema=response_schema,
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),
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)
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return json.loads(response.text)
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