solve the problem

app.py

@@ -1,153 +1,86 @@
 import gradio as gr
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from fastapi import FastAPI, Request
 
-# -------------------------------------------------
-# 1. Load model (same as your old code)
-# -------------------------------------------------
-print("Initializing DialoGPT-medium model...")
-model_name = "microsoft/DialoGPT-medium"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
-
-print("DialoGPT-medium loaded!")
-
-# -------------------------------------------------
-# 2. Generation helper (your old logic, cleaned up)
-# -------------------------------------------------
-def generate_response(message: str, chat_history: list):
-    if not message.strip():
-        return "Please enter a message."
-
-    # Build conversation string
-    conv = ""
-    for user, bot in chat_history:
-        conv += f"User: {user}\nBot: {bot}\n"
-    conv += f"User: {message}\nBot:"
-
-    # Encode
-    inputs = tokenizer.encode(conv, return_tensors="pt", max_length=1024, truncation=True)
-
-    # Generate
+# Initialize model and tokenizer
+model = None
+tokenizer = None
+
+print("🚀 Initializing DialoGPT-medium model...")
+
+try:
+    print("📥 Loading DialoGPT-medium model...")
+    model_name = "microsoft/DialoGPT-medium"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    print("✅ DialoGPT-medium model loaded successfully!")
+    
+    # Add padding token if it doesn't exist
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    
+except Exception as e:
+    print(f"❌ Failed to load DialoGPT-medium model: {e}")
+    model = None
+    tokenizer = None
+
+def respond(message, chat_history):
+    """Respond to user message using DialoGPT"""
+    if model is None or tokenizer is None:
+        return "Model not loaded. Please try again later."
+    
+    # Build conversation history
+    conversation = ""
+    for turn in chat_history:
+        conversation += f"User: {turn[0]}\nBot: {turn[1]}\n"
+    
+    conversation += f"User: {message}\nBot:"
+    
+    # Encode and generate
+    inputs = tokenizer.encode(conversation, return_tensors='pt', max_length=1024, truncation=True)
+    
     with torch.no_grad():
         outputs = model.generate(
             inputs,
-            max_length=inputs.shape[1] + 128,
+            max_length=len(inputs[0]) + 128,
             pad_token_id=tokenizer.eos_token_id,
             do_sample=True,
             temperature=0.7,
             top_k=50,
             top_p=0.95,
-            repetition_penalty=1.2,
+            repetition_penalty=1.2
         )
-
+    
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
     response = response.split("Bot:")[-1].strip()
+    
+    # Clean response
     if "\nUser:" in response:
         response = response.split("\nUser:")[0]
+    
+    chat_history.append((message, response))
+    return "", chat_history
+
+# Create the chat interface
+demo = gr.ChatInterface(
+    fn=respond,
+    title="💬 GihonTech AI Conversation Assistant",
+    description="Chat with an AI powered by Microsoft's DialoGPT-medium model",
+    examples=[
+        "Hello! How are you today?",
+        "What can you help me with?",
+        "Tell me about artificial intelligence",
+        "What's your favorite programming language?",
+    ],
+    cache_examples=False,
+    retry_btn=None,
+    undo_btn="↩️ Undo",
+    clear_btn="🗑️ Clear"
+)
 
-    return response
-
-# -------------------------------------------------
-# 3. Gradio chat function (used by /run/predict)
-# -------------------------------------------------
-def chat_fn(message: str, history: list):
-    response = generate_response(message, history or [])
-    history.append((message, response))
-    return "", history   # clear textbox, update chat
-
-# -------------------------------------------------
-# 4. Build the UI (your Blocks layout)
-# -------------------------------------------------
-example_questions = [
-    "Hello! How are you today?",
-    "What can you help me with?",
-    "Tell me about artificial intelligence",
-    "What's your favorite programming language?",
-    "Can you explain machine learning?",
-    "How does a neural network work?"
-]
-
-with gr.Blocks(
-    theme=gr.themes.Soft(primary_hue="blue", secondary_hue="green"),
-    title="GihonTech - AI Conversation Assistant"
-) as demo:
-
-    gr.Markdown("# GihonTech AI Conversation Assistant")
-    gr.Markdown("Chat with an AI powered by **DialoGPT-medium**")
-
-    with gr.Row():
-        with gr.Column(scale=3):
-            chatbot = gr.Chatbot(label="Conversation", height=500)
-
-            with gr.Row():
-                msg = gr.Textbox(
-                    label="Your Message",
-                    placeholder="Type your message here...",
-                    lines=2,
-                    scale=4,
-                )
-                send = gr.Button("Send", variant="primary", scale=1)
-
-            clear = gr.Button("Clear Chat", variant="secondary")
-
-        with gr.Column(scale=1):
-            gr.Markdown("### Example Questions")
-            for q in example_questions:
-                gr.Button(q[:40] + ("..." if len(q) > 40 else ""), size="sm").click(
-                    lambda x=q: x, outputs=msg
-                )
-            gr.Markdown("---")
-            gr.Markdown("### Model Info")
-            gr.Textbox(
-                value="DialoGPT-medium: Loaded",
-                label="Model Status",
-                interactive=False,
-            )
-            gr.Markdown(
-                """
-                **Features**  
-                - Context-aware replies  
-                - Conversation memory  
-
-                **Tips**  
-                - Ask clear questions  
-                - Use *Clear Chat* to start over
-                """
-            )
-
-    # Event wiring
-    send.click(chat_fn, inputs=[msg, chatbot], outputs=[msg, chatbot])
-    msg.submit(chat_fn, inputs=[msg, chatbot], outputs=[msg, chatbot])
-    clear.click(lambda: ([], ""), outputs=[chatbot, msg])
-
-# -------------------------------------------------
-# 5. OPTIONAL: expose /lambda (same JSON format)
-# -------------------------------------------------
-fastapi_app = FastAPI()
-
-@fastapi_app.post("/lambda")
-async def lambda_endpoint(req: Request):
-    payload = await req.json()
-    # Gradio sends {"data": [...]} ; we accept anything
-    user_msg = payload.get("data", [""])[0]
-    # Use the same generation logic (no history for this endpoint)
-    resp = generate_response(user_msg, [])
-    return {"data": [resp]}
-
-demo.mount_app(fastapi_app)   # makes /lambda reachable
-
-# -------------------------------------------------
-# 6. Launch with queue (critical for API!)
-# -------------------------------------------------
 if __name__ == "__main__":
-    demo.queue().launch(
+    demo.launch(
         server_name="0.0.0.0",
         server_port=7860,
-        share=False,
-        show_error=True,
+        share=False
     )