Deploy Gradio app with multiple files

app.py

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+import gradio as gr
+from models import chat_with_model
+from config import MODEL_NAME
+
+def chat_response(message, history):
+    # Format history for the model
+    conversation = []
+    for user_msg, assistant_msg in history:
+        conversation.append({"role": "user", "content": user_msg})
+        if assistant_msg:
+            conversation.append({"role": "assistant", "content": assistant_msg})
+    conversation.append({"role": "user", "content": message})
+    
+    response = chat_with_model(conversation)
+    return response
+
+with gr.Blocks(title="LLM Chatbot") as demo:
+    gr.Markdown("# 🤖 LLM Chatbot")
+    gr.Markdown("*Powered by a Hugging Face model*")
+    gr.Markdown("[Built with anycoder](https://huggingface.co/spaces/akhaliq/anycoder)")
+    
+    chatbot = gr.Chatbot(type="messages", height=400)
+    msg = gr.Textbox(placeholder="Type your message here...", label="Message")
+    
+    with gr.Row():
+        submit_btn = gr.Button("Send")
+        clear_btn = gr.ClearButton([msg, chatbot])
+    
+    def respond(message, chat_history):
+        if not message.strip():
+            return "", chat_history
+        
+        response = chat_response(message, chat_history)
+        chat_history.append({"role": "user", "content": message})
+        chat_history.append({"role": "assistant", "content": response})
+        return "", chat_history
+    
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    submit_btn.click(respond, [msg, chatbot], [msg, chatbot])
+
+if __name__ == "__main__":
+    demo.launch()

config.py

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+# Configuration for the chatbot
+MODEL_NAME = "microsoft/DialoGPT-medium"  # Change to your preferred Hugging Face chat model

models.py

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+from transformers import pipeline
+from utils import format_conversation
+from config import MODEL_NAME
+
+# Load the model pipeline once
+chat_pipeline = None
+
+def get_chat_pipeline():
+    global chat_pipeline
+    if chat_pipeline is None:
+        chat_pipeline = pipeline("conversational", model=MODEL_NAME)
+    return chat_pipeline
+
+def chat_with_model(conversation):
+    """Send conversation to the Hugging Face model and get response."""
+    try:
+        pipe = get_chat_pipeline()
+        formatted_input = format_conversation(conversation)
+        response = pipe(formatted_input)
+        return response[0]['generated_text']
+    except Exception as e:
+        return f"Error: {str(e)}"

requirements.txt

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+gradio>=4.0.0
+transformers>=4.0.0
+torch>=2.0.0
+accelerate>=0.20.0

utils.py

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+def format_conversation(conversation):
+    """Format conversation for the model."""
+    return "\n".join([f"{msg['role']}: {msg['content']}" for msg in conversation])