Update app.py

app.py

@@ -1,41 +1,134 @@
-from fastapi import FastAPI, Request
+from fastapi import FastAPI, Request, HTTPException
 from fastapi.responses import JSONResponse
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
+import os
+import logging
 
-app = FastAPI()
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Get Hugging Face Space configuration
+HF_SPACE = os.getenv("SPACE_ID", "")
+BASE_PATH = f"/spaces/{HF_SPACE}" if HF_SPACE else ""
+
+# Initialize FastAPI with correct base path
+app = FastAPI(
+    title="DialoGPT API",
+    description="Chatbot API using Microsoft's DialoGPT-medium model",
+    version="1.0",
+    root_path=BASE_PATH,
+    docs_url="/docs" if not BASE_PATH else f"{BASE_PATH}/docs",
+    redoc_url=None
+)
 
 # Load model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+try:
+    logger.info("Loading tokenizer and model...")
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+    model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+    logger.info("Model loaded successfully!")
+except Exception as e:
+    logger.error(f"Model loading failed: {str(e)}")
+    raise RuntimeError("Model initialization failed") from e
 
-# In-memory history per user
+# In-memory chat history storage
 chat_history = {}
 
-@app.get("/")
+@app.get("/", include_in_schema=False)
 async def root():
     return {"message": "🟢 API is running. Use /ai?query=Hello&user_id=yourname"}
 
 @app.get("/ai")
 async def chat(request: Request):
-    query_params = dict(request.query_params)
-    user_input = query_params.get("query", "")
-    user_id = query_params.get("user_id", "default")
-
-    if not user_input:
-        return JSONResponse({"error": "Missing 'query' parameter"}, status_code=400)
+    try:
+        # Get query parameters
+        user_input = request.query_params.get("query", "").strip()
+        user_id = request.query_params.get("user_id", "default").strip()
+        
+        # Validate input
+        if not user_input:
+            raise HTTPException(
+                status_code=400,
+                detail="Missing 'query' parameter. Usage: /ai?query=Hello&user_id=yourname"
+            )
+        if len(user_input) > 200:
+            raise HTTPException(
+                status_code=400,
+                detail="Query too long (max 200 characters)"
+            )
 
-    new_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
-    user_history = chat_history.get(user_id, [])
-    bot_input_ids = torch.cat(user_history + [new_input_ids], dim=-1) if user_history else new_input_ids
+        # Process the query
+        new_input_ids = tokenizer.encode(
+            user_input + tokenizer.eos_token,
+            return_tensors='pt'
+        )
+        
+        # Retrieve user history
+        user_history = chat_history.get(user_id, [])
+        
+        # Generate bot response
+        bot_input_ids = torch.cat(user_history + [new_input_ids], dim=-1) if user_history else new_input_ids
+        output_ids = model.generate(
+            bot_input_ids,
+            max_new_tokens=100,
+            pad_token_id=tokenizer.eos_token_id,
+            do_sample=True,
+            top_k=50,
+            top_p=0.95
+        )
+        
+        # Decode and clean response
+        response = tokenizer.decode(
+            output_ids[:, bot_input_ids.shape[-1]:][0],
+            skip_special_tokens=True
+        ).strip()
+        
+        # Update chat history
+        chat_history[user_id] = [bot_input_ids, output_ids]
+        
+        return {"reply": response}
+    
+    except torch.cuda.OutOfMemoryError:
+        logger.error("CUDA out of memory error")
+        # Clear history to free memory
+        if user_id in chat_history:
+            del chat_history[user_id]
+        raise HTTPException(
+            status_code=500,
+            detail="Memory error. Conversation history cleared. Please try again."
+        )
+    
+    except Exception as e:
+        logger.error(f"Processing error: {str(e)}")
+        raise HTTPException(
+            status_code=500,
+            detail=f"Processing error: {str(e)}"
+        ) from e
 
-    output_ids = model.generate(bot_input_ids, max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
-    response = tokenizer.decode(output_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+@app.get("/health")
+async def health_check():
+    return {
+        "status": "healthy",
+        "model": "microsoft/DialoGPT-medium",
+        "users": len(chat_history),
+        "space_id": HF_SPACE
+    }
 
-    chat_history[user_id] = [bot_input_ids, output_ids]
-    return JSONResponse({"reply": response})
+@app.get("/reset")
+async def reset_history(user_id: str = "default"):
+    if user_id in chat_history:
+        del chat_history[user_id]
+    return {"status": "success", "message": f"History cleared for user {user_id}"}
 
-# Only needed if running locally, not in Hugging Face Space
+# Only run with uvicorn when executing locally
 if __name__ == "__main__":
     import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(
+        app,
+        host="0.0.0.0",
+        port=7860,
+        log_level="info",
+        timeout_keep_alive=30
+    )