app.py

import gradio as gr
from inference import get_evo_response, get_gpt_response
from logger import log_feedback
import retrain
import pandas as pd
import os

# 🔍 Advisor core logic
def advisor_interface(query, context, feedback_choice):
    evo_output, evo_reasoning = get_evo_response(query, context, enable_search=True)
    gpt_output = get_gpt_response(query, context)

    if feedback_choice != "No feedback":
        label = 1 if feedback_choice == "👍 Helpful" else 0
        log_feedback(query, context, evo_output, label)

    return evo_reasoning, gpt_output, load_history()

# 🔁 Retrain logic
def retrain_evo():
    retrain.fine_tune_on_feedback()
    return "✅ Evo retrained on feedback.", load_history()

# 📜 Feedback log viewer
def load_history():
    if os.path.exists("feedback_log.csv"):
        df = pd.read_csv("feedback_log.csv")
        return df.tail(10).to_markdown(index=False)
    return "No history available yet."

# 🧠 UI
with gr.Blocks() as demo:
    gr.Markdown("## 🧠 EvoRAG – Retrieval-Augmented Adaptive AI for Finance")

    with gr.Accordion("📌 Ask a Financial Question", open=True):
        with gr.Row():
            query = gr.Textbox(label="📝 Question", placeholder="e.g. Should we reduce exposure to Fund A?")
        with gr.Row():
            context = gr.Textbox(label="📂 Memo, Policy, or News Context", placeholder="e.g. Tech Fund A underperformed by 3.2%...")

    with gr.Row():
        feedback = gr.Radio(["👍 Helpful", "👎 Not Helpful", "No feedback"], label="📊 Was Evo’s answer useful?", value="No feedback")

    with gr.Row():
        evo_out = gr.Textbox(label="🔬 EvoRAG Suggestion (with reasoning)", lines=4)
        gpt_out = gr.Textbox(label="🤖 GPT-3.5 Suggestion", lines=4)

    run_button = gr.Button("🚀 Run Advisors")
    run_button.click(fn=advisor_interface, inputs=[query, context, feedback], outputs=[evo_out, gpt_out, gr.Textbox(label="📜 Recent Feedback Log")])

    gr.Markdown("---")

    with gr.Accordion("🔁 Evo Live Retraining", open=False):
        retrain_button = gr.Button("📚 Retrain Evo Now from Feedback")
        retrain_output = gr.Textbox(label="🛠️ Retrain Status", interactive=False)
        history_output = gr.Textbox(label="📜 Recent Feedback Log", interactive=False)
        retrain_button.click(fn=retrain_evo, inputs=[], outputs=[retrain_output, history_output])

demo.launch()