app.py

import gradio as gr
from inference import evo_chat_predict
import subprocess

# Global chat history buffer
chat_history = []

# 🧠 Main chat handler
def chat_fn(user_input, option1, option2):
    global chat_history

    # Validate input
    if not user_input or not option1 or not option2:
        return "Please enter a message and both options.", chat_history

    options = [option1.strip(), option2.strip()]
    result = evo_chat_predict(chat_history, user_input, options)

    # Format Evo reply
    evo_response = f"**Answer:** {result['answer']}  \n**Reasoning:** {result['reasoning']}"
    chat_history.append(f"User: {user_input}")
    chat_history.append(f"Evo: {evo_response}")

    return evo_response, chat_history

# 🔁 Reset chat history
def clear_fn():
    global chat_history
    chat_history = []
    return "", "", "", []

# 🔁 Trigger Evo retraining
def retrain_model():
    try:
        subprocess.run(["python", "retrain_from_feedback.py"], check=True)
        return "✅ Evo retrained successfully."
    except Exception as e:
        return f"❌ Retraining failed: {str(e)}"

# 🧠 Gradio UI layout
with gr.Blocks(title="EvoRAG – Real-Time Adaptive Reasoning AI") as demo:
    gr.Markdown("## 🧬 EvoRAG – The Evolving Reasoning AI")
    gr.Markdown("Ask a question, give two options, and Evo will decide with confidence. Then, retrain it live.")

    with gr.Row():
        with gr.Column(scale=4):
            user_input = gr.Textbox(label="Your Question", lines=2)
            option1 = gr.Textbox(label="Option 1")
            option2 = gr.Textbox(label="Option 2")
            submit = gr.Button("🧠 Ask Evo")
            clear = gr.Button("🔁 Clear")
            retrain = gr.Button("📈 Retrain Evo from Feedback")

        with gr.Column(scale=6):
            evo_reply = gr.Markdown()
            chat_display = gr.HighlightedText(label="Conversation History")

    submit.click(fn=chat_fn, inputs=[user_input, option1, option2],
                 outputs=[evo_reply, chat_display])
    clear.click(fn=clear_fn, inputs=[], outputs=[user_input, option1, option2, chat_display])
    retrain.click(fn=retrain_model, inputs=[], outputs=evo_reply)

demo.launch()