app.py

import gradio as gr
from transformers import pipeline

# ✅ Use a lightweight, always-free model
jarvis = pipeline("text2text-generation", model="google/flan-t5-small")

# Function to handle messages (compatible with new Gradio)
def chat(message, history):
    # Reconstruct a text-based chat history
    context = ""
    for h in history:
        context += f"User: {h['content']}\nJarvis: {h.get('response', '')}\n"
    context += f"User: {message}\nJarvis:"
    
    # Generate response
    response = jarvis(context, max_new_tokens=128, temperature=0.7, do_sample=True)
    reply = response[0]["generated_text"]

    # Return message in new Gradio "messages" format
    history.append({"role": "user", "content": message})
    history.append({"role": "assistant", "content": reply})
    return "", history

# Gradio Chat Interface (new style)
gr.ChatInterface(
    fn=chat,
    title="Jarvis AI V2",
    description="Your personal AI assistant — accessible anywhere in the world.",
    theme="soft",
    type="messages",  # 👈 important to avoid tuple errors
).launch(share=True)