Update app.py

app.py

@@ -1,25 +1,94 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
+import os
 
+# Only import transformers when we need it
 app = FastAPI(title="Quiz Guru Chatbot", version="1.0.0")
 
+# Global variables
+model = None
+tokenizer = None
+device = None
+model_loaded = False
+
 class PromptRequest(BaseModel):
     prompt: str
 
+def load_model():
+    global model, tokenizer, device, model_loaded
+    try:
+        print("🔄 Starting model loading...")
+        
+        # Set cache directory
+        os.environ["HF_HOME"] = "/tmp"
+        
+        # Import here to avoid startup issues
+        from transformers import T5ForConditionalGeneration, T5Tokenizer
+        import torch
+        
+        print("📦 Loading tokenizer...")
+        tokenizer = T5Tokenizer.from_pretrained("chalana2001/quiz_guru_chatbot")
+        
+        print("🤖 Loading model...")
+        model = T5ForConditionalGeneration.from_pretrained(
+            "chalana2001/quiz_guru_chatbot", 
+            trust_remote_code=True
+        )
+        
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model.to(device)
+        model_loaded = True
+        
+        print(f"✅ Model loaded successfully on {device}")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Error loading model: {e}")
+        return False
+
+@app.on_event("startup")
+async def startup_event():
+    print("🚀 Starting up...")
+    # Don't block startup if model fails to load
+    load_model()
+
 @app.get("/")
 def read_root():
-    return {"message": "Quiz Guru Chatbot API", "status": "running"}
+    return {
+        "message": "Quiz Guru Chatbot API", 
+        "status": "running",
+        "model_loaded": model_loaded
+    }
 
-@app.get("/test")
-def test():
-    return {"message": "Test endpoint working!"}
+@app.get("/health")
+def health():
+    return {
+        "status": "healthy",
+        "model_loaded": model_loaded,
+        "device": str(device) if device else "unknown"
+    }
 
 @app.post("/predict")
 def predict(request: PromptRequest):
-    # For now, just echo back - we'll add the model next
-    return {"result": f"Echo: {request.prompt}", "status": "working"}
+    if not model_loaded:
+        return {"error": "Model not loaded. Please check /health endpoint."}
+    
+    try:
+        # Import torch here
+        import torch
+        
+        inputs = tokenizer(request.prompt, return_tensors="pt", padding=True).to(device)
+        
+        with torch.no_grad():
+            output = model.generate(**inputs, max_length=256, num_beams=4, early_stopping=True)
+        
+        decoded = tokenizer.decode(output[0], skip_special_tokens=True)
+        
+        return {"result": decoded, "status": "success"}
+        
+    except Exception as e:
+        return {"error": str(e), "status": "error"}
 
-# This will run when you use: python app.py
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=7860)