app.py

import gradio as gr
from inference import evo_rag_response, get_gpt_response
from retriever import build_index_from_file
from logger import log_feedback

def advisor_interface(query, file, feedback_choice):
    # Build FAISS index from uploaded file
    if file is not None:
        build_index_from_file(file.name)

    # Get Evo + GPT responses
    evo_output = evo_rag_response(query)
    gpt_output = get_gpt_response(query, "")  # optional context

    # Log feedback
    if feedback_choice != "No feedback":
        log_feedback(query, "[RAG+WEB context]", evo_output, feedback_choice)

    return evo_output, gpt_output

# Manual retrain trigger
def retrain_evo():
    import retrain
    retrain.fine_tune_on_feedback()
    return "✅ Evo retrained on feedback."

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

    with gr.Row():
        query = gr.Textbox(label="📝 Ask a financial question", placeholder="e.g. Option 1: Reduce exposure to Fund A. Option 2: Maintain allocation.")
        file = gr.File(label="📂 Upload memo or policy (.pdf or .txt)", file_types=[".pdf", ".txt"])

    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")
        gpt_out = gr.Textbox(label="🤖 GPT-3.5 Suggestion")

    submit_btn = gr.Button("Run Advisors")
    submit_btn.click(fn=advisor_interface, inputs=[query, file, feedback], outputs=[evo_out, gpt_out])

    gr.Markdown("---")

    retrain_btn = gr.Button("🔁 Retrain Evo from Feedback")
    retrain_status = gr.Textbox(label="Retraining Status")
    retrain_btn.click(fn=retrain_evo, outputs=retrain_status)

demo.launch()