Update app.py

app.py

@@ -1,54 +1,3 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-# ✅ Connect to Hugging Face API
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-# ✅ Custom System Prompt
-SYSTEM_PROMPT = (
-    "You are an AI assistant named Bandar AI. "
-    "If you do not understand the question, say: 'I don't understand.'"
-)
-
-# ✅ Chatbot Response Function
-def chatbot_response(user_input):
-    messages = [
-        {"role": "system", "content": SYSTEM_PROMPT},
-        {"role": "user", "content": user_input}
-    ]
-
-    response = client.chat_completion(messages, max_tokens=150, temperature=0.7)
-
-    # Extract the response text
-    try:
-        reply = response.choices[0].message["content"]
-    except (KeyError, IndexError):
-        reply = "I don't understand."  # Fallback response
-
-    return reply
-
-# ✅ Gradio UI with Modern Design
-with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo:
-    gr.Markdown(
-        """
-        # 🤖 Bandar AI - Smart Chatbot  
-        ### 💬 Ask me anything, and I will include your name in my responses!
-        """, 
-        elem_id="title"
-    )
-
-    chatbot = gr.Chatbot(label="Bandar AI", bubble_full_width=True)
-
-    with gr.Row():
-        user_input = gr.Textbox(placeholder="Type your message here...", label="Your Message", scale=8)
-        send_button = gr.Button("Send", variant="primary", scale=2)
-
-    with gr.Row():
-        clear_button = gr.Button("Clear Chat", variant="secondary")
-
-    # Event Handlers
-    send_button.click(chatbot_response, inputs=user_input, outputs=chatbot)
-    user_input.submit(chatbot_response, inputs=user_input, outputs=chatbot)
-    clear_button.click(lambda: "", outputs=chatbot)  # Clears the chat
-
 
+gr.load("models/HuggingFaceH4/zephyr-7b-beta").launch()