Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+'''import gradio as gr
 import os
 from inference import get_evo_response, get_gpt_response
 from logger import log_feedback
@@ -105,4 +105,117 @@ with gr.Blocks(title="EvoRAG") as demo:
     )
     retrain_button.click(fn=trigger_retrain, inputs=[], outputs=[retrain_status])
 
-demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
+demo.launch(server_name="0.0.0.0", server_port=7860, share=True)'''
+
+import gradio as gr
+import torch
+import time
+import os
+from inference import load_model_and_tokenizer, infer
+from logger import log_feedback
+from retrain_from_feedback import train_evo
+from datetime import datetime
+
+# Globals
+model, tokenizer = load_model_and_tokenizer("trained_model/evo_retrained.pt")
+
+# Helper to reload model
+def reload_model():
+    global model, tokenizer
+    model, tokenizer = load_model_and_tokenizer("trained_model/evo_retrained.pt")
+
+# Get last update time
+def get_last_update():
+    if os.path.exists("last_updated.txt"):
+        with open("last_updated.txt") as f:
+            return f.read().strip()
+    return "Never"
+
+# Handle inference
+def compare(question, option1, option2, context):
+    evo_ans, evo_score, evo_reason, evo_ctx = infer(model, tokenizer, question, option1, option2, context)
+    gpt_ans = "Coming soon via API"
+    return (
+        f"Answer: {evo_ans} (Confidence: {evo_score:.2f})\n\nReasoning: {evo_reason}\n\nContext used: {evo_ctx}",
+        gpt_ans
+    )
+
+# Handle feedback
+def handle_feedback(feedback_text, question, option1, option2, context, evo_output):
+    evo_was_correct = feedback_text.strip().lower() == "👍 evo was correct. retrain from this."
+    log_feedback(question, option1, option2, context, evo_output, evo_was_correct)
+    return "✅ Feedback logged and Evo will improve."
+
+# Manual retrain
+def manual_retrain():
+    try:
+        train_evo()
+        reload_model()
+        ts = datetime.utcnow().strftime("%Y-%m-%d %H:%M GMT")
+        with open("last_updated.txt", "w") as f:
+            f.write(ts)
+        return f"✅ Evo successfully evolved! Reloaded at {ts}"
+    except Exception as e:
+        return f"❌ Retraining failed: {str(e)}"
+
+with gr.Blocks(title="EvoRAG – Adaptive Reasoning AI", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # EvoRAG – Adaptive Reasoning AI
+    **What is Evo?**  
+    EvoTransformer is a lightweight, evolving neural network with ~28M parameters.  
+    It learns from feedback, adapts over time, and reasons using both web and context data.
+
+    **Why Evo?**  
+    ✅ Evolves from human input  
+    ✅ Architecturally updatable  
+    ✅ Transparent and fine-tunable  
+    ✅ Efficient on modest hardware  
+
+    **Hardware:** Trained on Google Colab CPU/GPU  
+    **Token limit:** 128  
+    **Benchmark:** PIQA, HellaSwag, ARC  
+    **Version:** Evo v2.2 (Memory + Web Retrieval + Feedback Learning)  
+    **🕒 Last Evolution:** {get_last_update()}
+    """)
+
+    with gr.Row():
+        question = gr.Textbox(label="Ask anything", placeholder="e.g. What’s the best way to boil water?")
+
+    with gr.Row():
+        option1 = gr.Textbox(label="Option A")
+        option2 = gr.Textbox(label="Option B")
+
+    context = gr.Textbox(label="📂 Optional Context", lines=2, placeholder="Paste any extra background info here")
+
+    run_btn = gr.Button("🔍 Run Comparison")
+
+    with gr.Row():
+        evo_out = gr.Textbox(label="🧠 EvoRAG's Reasoned Answer")
+        gpt_out = gr.Textbox(label="🤖 GPT-3.5's Suggestion")
+
+    with gr.Row():
+        feedback_dropdown = gr.Dropdown([
+            "👍 Evo was correct. Retrain from this.",
+            "👎 Evo was wrong. Don't retrain."
+        ], label="Was Evo’s answer useful?")
+        submit_btn = gr.Button("📬 Submit Feedback")
+
+    feedback_status = gr.Textbox(label="Feedback Status")
+
+    with gr.Row():
+        retrain_btn = gr.Button("🔄 Retrain Evo Now")
+        retrain_status = gr.Textbox(label="Retraining Status")
+
+    hall = gr.Markdown("""
+    ## 🏆 Evo Hall of Fame (Top Reasoning Entries)
+    *(Coming soon)*
+    """)
+
+    run_btn.click(fn=compare, inputs=[question, option1, option2, context], outputs=[evo_out, gpt_out])
+    submit_btn.click(fn=lambda fb, q, o1, o2, ctx, eo: handle_feedback(fb, q, o1, o2, ctx, eo),
+                     inputs=[feedback_dropdown, question, option1, option2, context, evo_out],
+                     outputs=feedback_status)
+    retrain_btn.click(fn=manual_retrain, outputs=retrain_status)
+
+demo.launch()
+