app.py

import gradio as gr
import os
from inference import evo_infer, gpt_infer
from retrain_from_feedback import train_evo
from logger import log_feedback

question = gr.Textbox(label="🧠 Your Question", placeholder="e.g. What should you do if there's a fire?", lines=1)
option1 = gr.Textbox(label="🅰️ Option 1", placeholder="Enter the first option")
option2 = gr.Textbox(label="🅱️ Option 2", placeholder="Enter the second option")
choice = gr.Radio(["Evo", "GPT"], label="🗳️ Who was better?", info="Optional – fuels evolution", type="value")

evo_out = gr.Textbox(label="🤖 Evo", interactive=False)
gpt_out = gr.Textbox(label="🧠 GPT-3.5", interactive=False)
history = gr.Textbox(label="📜 Conversation History", interactive=False)
evo_stats = gr.Textbox(label="📊 Evo Stats", value="Layers: 6 | Heads: 8 | FFN: 1024 | Memory: ✅ | Accuracy: ~64.5% | Phase: v2.2", interactive=False)

def run_inference(q, o1, o2, winner):
    evo_answer, evo_reasoning = evo_infer(q, o1, o2)
    gpt_answer = gpt_infer(q, o1, o2)
    context = f"Question: {q}\nOptions: {o1}, {o2}"
    log_feedback(q, o1, o2, context, evo_answer, gpt_answer, evo_reasoning, winner)
    conv_log = f"👤 {q}\n🅰️ {o1} | 🅱️ {o2}\n🤖 Evo: {evo_answer} ({evo_reasoning})\n🧠 GPT: {gpt_answer}"
    return evo_answer, gpt_answer, conv_log

def clear():
    return "", "", "", None, "", "", ""

def export_csv():
    return gr.File("feedback_log.csv")

def retrain():
    train_evo()
    return "🔁 Evo model reloaded."

with gr.Blocks(theme=gr.themes.Soft(), css="""
body { background-color: #f3f6fb; font-family: 'Segoe UI', sans-serif; }
.gradio-container { max-width: 1024px; margin: auto; }
.gr-box { box-shadow: 0 4px 16px rgba(0,0,0,0.1); border-radius: 12px; padding: 16px; transition: all 0.3s ease-in-out; }
.gr-button { border-radius: 8px; font-weight: 600; transition: all 0.2s ease-in-out; }
.gr-button:hover { transform: scale(1.03); background-color: #e6f2ff; }
.gr-textbox, .gr-radio { border-radius: 8px; }
""") as demo:

    gr.Markdown("""
        <h1 style="font-size: 2.2em;">🧠 EvoRAG – Real-Time Reasoning AI</h1>
        <p><b>Built Different. Learns Live. Evolves from You.</b></p>
        <div style="margin-top: 10px; font-size: 0.9em;">
        <ul>
          <li>🚀 <b>Why Evo?</b></li>
          <li>✔️ Learns from your input — evolves in real time</li>
          <li>✔️ Adaptive architecture (changes #layers, memory, etc.)</li>
          <li>✔️ Tiny model (~13M–28M params) vs GPT-3.5 (175B)</li>
          <li>✔️ Runs on CPU or low-end GPUs</li>
          <li>✔️ Transparent architecture: shows how it thinks</li>
          <li>✔️ Can be deployed, fine-tuned, and evolved per user/domain</li>
        </ul>
        </div>
    """)

    with gr.Row():
        with gr.Column():
            question.render()
            option1.render()
            option2.render()
            choice.render()

        with gr.Column():
            ask_btn = gr.Button("💡 Ask Evo", variant="primary")
            retrain_btn = gr.Button("🔁 Retrain Evo")
            clear_btn = gr.Button("🧹 Clear")
            export_btn = gr.Button("⬇️ Export Feedback CSV")
            evo_stats.render()

    with gr.Row():
        evo_out.render()
        gpt_out.render()

    with gr.Row():
        retrain_status = gr.Textbox(label="🔁 Status", interactive=False)

    with gr.Accordion("📚 Conversation History", open=True):
        history.render()

    ask_btn.click(fn=run_inference, inputs=[question, option1, option2, choice], outputs=[evo_out, gpt_out, history])
    clear_btn.click(fn=clear, outputs=[question, option1, option2, choice, evo_out, gpt_out, history])
    export_btn.click(fn=export_csv, outputs=[])
    retrain_btn.click(fn=retrain, outputs=[retrain_status])

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))