Update app.py

app.py

@@ -16,31 +16,31 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
     Market Intelligence Assistant for Indian Businesses powered by Anki-2.5
     Provides insights on Indian market trends, business strategies, and more
     """
-    # Construct conversation with system message
-    conversation = [{"role": "system", "content": system_message}]
+    # Build prompt manually
+    prompt = system_message + "\n\n"
     
     # Add history
     for msg in history:
         if isinstance(msg, dict):
-            conversation.append(msg)
+            role = msg.get("role", "")
+            content = msg.get("content", "")
+            if role == "user":
+                prompt += f"User: {content}\n"
+            elif role == "assistant":
+                prompt += f"Assistant: {content}\n"
         elif len(msg) == 2:
-            conversation.append({"role": "user", "content": msg[0]})
-            conversation.append({"role": "assistant", "content": msg[1]})
+            prompt += f"User: {msg[0]}\n"
+            prompt += f"Assistant: {msg[1]}\n"
     
     # Add current message
-    conversation.append({"role": "user", "content": message})
+    prompt += f"User: {message}\nAssistant:"
     
     # Generate response
-    inputs = tokenizer.apply_chat_template(
-        conversation,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_tensors="pt"
-    ).to(model.device)
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
     
     with torch.no_grad():
         outputs = model.generate(
-            inputs,
+            inputs["input_ids"],
             max_new_tokens=max_tokens,
             temperature=temperature,
             top_p=top_p,
@@ -48,7 +48,7 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
             pad_token_id=tokenizer.eos_token_id
         )
     
-    response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
+    response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
     return response
 
 # Create ChatInterface focused on business intelligence