app.py

import gradio as gr
from inference import get_evo_response, get_gpt_response
from logger import log_feedback
from utils import extract_text_from_file

def advisor_interface(question, file, feedback_choice):
    context = extract_text_from_file(file) if file else ""
    
    evo_answer = get_evo_response(question, context)
    gpt_answer = get_gpt_response(question, context)

    if feedback_choice != "No feedback":
        log_feedback(question, context, evo_answer, feedback_choice)

    return evo_answer, gpt_answer

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

    with gr.Row():
        question = gr.Textbox(label="📝 Ask anything", placeholder="e.g. Should we diversify the portfolio?")

    with gr.Row():
        file = gr.File(label="📂 Upload memo (.pdf or .txt)", file_types=[".pdf", ".txt"], type="binary")

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

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

demo.launch()