Update app.py

app.py

@@ -1,34 +1,21 @@
-import os
-os.system("pip install transformers torch accelerate gradio")  # Install dependencies
-
 import gradio as gr
 from transformers import pipeline
 
-# Load the Hugging Face model
-chatbot_pipeline = pipeline("text-generation", model="mistralai/Mistral-7B-v0.1", max_new_tokens=100)
-
-# Chatbot function
-def chat_response(history, user_input):
-    if not user_input.strip():
-        return history + [{"role": "user", "content": user_input}, {"role": "assistant", "content": "Please enter a valid message."}], ""
-
-    bot_reply = chatbot_pipeline(user_input)[0]["generated_text"]
+# Use a simple, fast Hugging Face model
+chatbot_pipeline = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha")
 
-    history.append({"role": "user", "content": user_input})
-    history.append({"role": "assistant", "content": bot_reply})
-
-    return history, ""
+# Function to generate a chatbot response
+def chat_response(user_input):
+    response = chatbot_pipeline(user_input, max_length=100)[0]["generated_text"]
+    return response
 
 # Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("## 🤖 AI Bandar - Powered by Hugging Face")
-
-    chatbot = gr.Chatbot(type="messages")
-    user_input = gr.Textbox(label="Your Message")
-    submit_button = gr.Button("Send")
-
-    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])
+demo = gr.Interface(
+    fn=chat_response,
+    inputs="text",
+    outputs="text",
+    title="🤖 Bandar AI Model"
+)
 
 # Run on Hugging Face Spaces
 if __name__ == "__main__":