app.py

import gradio as gr
import requests
import json

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    access_token,
    model_endpoint,
    max_tokens,
    temperature,
    top_p,
):
    # Build conversation history
    messages = [{"role": "system", "content": system_message}]
    
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})
    
    messages.append({"role": "user", "content": message})
    
    # Vertex AI API request
    headers = {
        "Authorization": f"Bearer {access_token}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "instances": [{
            "messages": messages,
            "max_tokens": max_tokens,
            "temperature": temperature,
            "top_p": top_p
        }]
    }
    
    try:
        response = requests.post(model_endpoint, headers=headers, json=payload)
        response.raise_for_status()
        
        result = response.json()
        # Extract response text from Vertex AI response format
        generated_text = result["predictions"][0]["content"]
        yield generated_text
        
    except Exception as e:
        yield f"Error: {str(e)}"


demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Textbox(value="", label="Google Cloud Access Token", type="password"),
        gr.Textbox(value="", label="Vertex AI Model Endpoint URL", placeholder="https://us-central1-aiplatform.googleapis.com/v1/projects/YOUR_PROJECT/locations/us-central1/endpoints/YOUR_ENDPOINT:predict"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()