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 RAG index if a file is uploaded
    if file is not None:
        build_index_from_file(file.name)

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

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

    return evo_output, gpt_output

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. Should we reduce exposure to Fund A?")
        file = gr.File(label="📂 Upload policy or memo (.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])

demo.launch()