Update app.py

app.py

@@ -1,3 +1,101 @@
+# # 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
@@ -14,43 +112,57 @@ 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"
+    description="API for getting chat responses from Llama model with image support",
+    version="1.1.0"
 )
 
 class ChatRequest(BaseModel):
     text: str
+    image_url: Optional[str] = None
 
 class ChatResponse(BaseModel):
     response: str
     status: str
 
-def llm_chat_response(text: str) -> 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
         )
-        
+
+        # Prepare content list for the message
+        content = [
+            {
+                "type": "text",
+                "text": text + " describe in one line only"
+            }
+        ]
+
+        # Add image to content if provided
+        if image_url:
+            logger.info(f"Adding image URL to request: {image_url}")
+            content.append({
+                "type": "image_url",
+                "image_url": {
+                    "url": image_url
+                }
+            })
+
         messages = [
             {
                 "role": "user",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": text + " describe in one line only"
-                    }
-                ]
+                "content": content
             }
         ]
-        
+
         logger.info("Sending request to model...")
         completion = client.chat.completions.create(
             model="meta-llama/Llama-3.2-11B-Vision-Instruct",
@@ -58,7 +170,6 @@ def llm_chat_response(text: str) -> str:
             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))
@@ -67,7 +178,10 @@ def llm_chat_response(text: str) -> str:
 async def chat(request: ChatRequest):
     try:
         logger.info(f"Received chat request with text: {request.text}")
-        response = llm_chat_response(request.text)
+        if request.image_url:
+            logger.info(f"Image URL included: {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)}")
@@ -78,7 +192,7 @@ async def chat(request: ChatRequest):
 
 @app.get("/")
 async def root():
-    return {"message": "Welcome to the LLM Chat API. Use POST /chat endpoint to get responses."}
+    return {"message": "Welcome to the LLM Chat API with image support. Use POST /chat endpoint to get responses."}
 
 @app.exception_handler(404)
 async def not_found_handler(request, exc):