app.py

# # app.py
# import os
# import logging
# from fastapi import FastAPI, HTTPException
# from fastapi.responses import JSONResponse
# from pydantic import BaseModel
# from huggingface_hub import InferenceClient
# from typing import Optional

# # Set up logging
# logging.basicConfig(level=logging.INFO)
# logger = logging.getLogger(__name__)

# # Initialize FastAPI app
# app = FastAPI(
#     title="LLM Chat API",
#     description="API for getting chat responses from Llama model",
#     version="1.0.0"
# )

# class ChatRequest(BaseModel):
#     text: str

# class ChatResponse(BaseModel):
#     response: str
#     status: str

# def llm_chat_response(text: str) -> str:
#     try:
#         HF_TOKEN = os.getenv("HF_TOKEN")
#         logger.info("Checking HF_TOKEN...")
#         if not HF_TOKEN:
#             logger.error("HF_TOKEN not found in environment variables")
#             raise HTTPException(status_code=500, detail="HF_TOKEN not configured")
        
#         logger.info("Initializing InferenceClient...")
#         client = InferenceClient(
#             provider="sambanova",
#             api_key=HF_TOKEN
#         )
        
#         messages = [
#             {
#                 "role": "user",
#                 "content": [
#                     {
#                         "type": "text",
#                         "text": text + " describe in one line only"
#                     }
#                 ]
#             }
#         ]
        
#         logger.info("Sending request to model...")
#         completion = client.chat.completions.create(
#             model="meta-llama/Llama-3.2-11B-Vision-Instruct",
#             messages=messages,
#             max_tokens=500
#         )
#         return completion.choices[0].message['content']

#     except Exception as e:
#         logger.error(f"Error in llm_chat_response: {str(e)}")
#         raise HTTPException(status_code=500, detail=str(e))

# @app.post("/chat", response_model=ChatResponse)
# async def chat(request: ChatRequest):
#     try:
#         logger.info(f"Received chat request with text: {request.text}")
#         response = llm_chat_response(request.text)
#         return ChatResponse(response=response, status="success")
#     except HTTPException as he:
#         logger.error(f"HTTP Exception in chat endpoint: {str(he)}")
#         raise he
#     except Exception as e:
#         logger.error(f"Unexpected error in chat endpoint: {str(e)}")
#         raise HTTPException(status_code=500, detail=str(e))

# @app.get("/")
# async def root():
#     return {"message": "Welcome to the LLM Chat API. Use POST /chat endpoint to get responses."}

# @app.exception_handler(404)
# async def not_found_handler(request, exc):
#     return JSONResponse(
#         status_code=404,
#         content={"error": "Endpoint not found. Please use POST /chat for queries."}
#     )

# @app.exception_handler(405)
# async def method_not_allowed_handler(request, exc):
#     return JSONResponse(
#         status_code=405,
#         content={"error": "Method not allowed. Please check the API documentation."}
#     )

# app.py
import os
import logging
from typing import Optional
from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from huggingface_hub import InferenceClient

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Initialize FastAPI app
app = FastAPI(
    title="LLM Chat API",
    description="API for getting chat responses from Llama model (supports text and image input)",
    version="1.0.0"
)

class ChatRequest(BaseModel):
    text: str
    image_url: Optional[str] = None

class ChatResponse(BaseModel):
    response: str
    status: str

def llm_chat_response(text: str, image_url: Optional[str] = None) -> str:
    try:
        HF_TOKEN = os.getenv("HF_TOKEN")
        logger.info("Checking HF_TOKEN...")
        if not HF_TOKEN:
            logger.error("HF_TOKEN not found in environment variables")
            raise HTTPException(status_code=500, detail="HF_TOKEN not configured")
        
        logger.info("Initializing InferenceClient...")
        client = InferenceClient(
            provider="sambanova",
            api_key=HF_TOKEN
        )
        
        # Build the messages payload dynamically.
        # If only text is provided, we append a default instruction.
        message_content = [{
            "type": "text",
            "text": text + ("" if image_url else " describe in one line only")
        }]
        
        if image_url:
            message_content.append({
                "type": "image_url",
                "image_url": {"url": image_url}
            })
        
        messages = [{
            "role": "user",
            "content": message_content
        }]
        
        logger.info("Sending request to model...")
        completion = client.chat.completions.create(
            model="meta-llama/Llama-3.2-11B-Vision-Instruct",
            messages=messages,
            max_tokens=500
        )
        
        # Debug log the raw response for troubleshooting.
        logger.info(f"Raw model response: {completion}")
        
        # Ensure we have a valid response.
        if not completion.choices or len(completion.choices) == 0:
            logger.error("No choices returned from model.")
            raise HTTPException(status_code=500, detail="Model returned no choices.")
        
        # Extract the message; use dict get to avoid NoneType errors.
        response_message = None
        # Some responses may be dicts or objects; try both approaches.
        choice = completion.choices[0]
        if hasattr(choice, "message"):
            response_message = choice.message
        elif isinstance(choice, dict):
            response_message = choice.get("message")
        
        if not response_message:
            logger.error(f"Response message is empty: {choice}")
            raise HTTPException(status_code=500, detail="Model response did not include a message.")
        
        # Extract the content from the message.
        content = None
        if isinstance(response_message, dict):
            content = response_message.get("content")
        # If for some reason it's not a dict, try attribute access.
        if content is None and hasattr(response_message, "content"):
            content = response_message.content
        
        if not content:
            logger.error(f"Message content is missing: {response_message}")
            raise HTTPException(status_code=500, detail="Model message did not include content.")
        
        return content

    except Exception as e:
        logger.error(f"Error in llm_chat_response: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/chat", response_model=ChatResponse)
async def chat(request: ChatRequest):
    try:
        logger.info(f"Received chat request with text: {request.text}")
        if request.image_url:
            logger.info(f"Image URL provided: {request.image_url}")
        response = llm_chat_response(request.text, request.image_url)
        return ChatResponse(response=response, status="success")
    except HTTPException as he:
        logger.error(f"HTTP Exception in chat endpoint: {str(he)}")
        raise he
    except Exception as e:
        logger.error(f"Unexpected error in chat endpoint: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/")
async def root():
    return {"message": "Welcome to the LLM Chat API. Use POST /chat endpoint with 'text' and optionally 'image_url' for queries."}

@app.exception_handler(404)
async def not_found_handler(request, exc):
    return JSONResponse(
        status_code=404,
        content={"error": "Endpoint not found. Please use POST /chat for queries."}
    )

@app.exception_handler(405)
async def method_not_allowed_handler(request, exc):
    return JSONResponse(
        status_code=405,
        content={"error": "Method not allowed. Please check the API documentation."}
    )