Update app.py

app.py

@@ -1,22 +1,29 @@
 import gradio as gr
-from transformers import pipeline
 
-# Use a simple, fast Hugging Face model
-chatbot_pipeline = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha")
+# Define the chatbot function
+def chat_response(history, user_input):
+    """Handles chatbot interactions."""
+    if not user_input.strip():
+        return history + [("User", user_input), ("Bot", "Please enter a valid message.")]
 
-# Function to generate a chatbot response
-def chat_response(user_input):
-    response = chatbot_pipeline(user_input, max_length=100)[0]["generated_text"]
-    return response
+    response = f"You said: {user_input}"  # Replace this with actual chatbot logic (e.g., OpenAI API)
+    history.append({"role": "user", "content": user_input})
+    history.append({"role": "assistant", "content": response})
 
-# Gradio UI
-demo = gr.Interface(
-    fn=chat_response,
-    inputs="text",
-    outputs="text",
-    title="🤖 Bandar AI Model"
-)
+    return history, ""
 
-# Run on Hugging Face Spaces
+# Initialize Gradio chatbot with the correct type
+with gr.Blocks() as demo:
+    gr.Markdown("## 🚀 AI Bandar - Powered by Gradio")
+
+    chatbot = gr.Chatbot(type="messages")  # ✅ Fix: Use 'messages' format
+    user_input = gr.Textbox(label="Your Message")
+    submit_button = gr.Button("Send")
+
+    # Button click or Enter key submits the message
+    submit_button.click(chat_response, inputs=[chatbot, user_input], outputs=[chatbot, user_input])
+    user_input.submit(chat_response, inputs=[chatbot, user_input], outputs=[chatbot, user_input])
+
+# Run the Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)  # 🚀 Allows sharing via public Gradio link