Update app.py

app.py

@@ -1,99 +1,54 @@
-
-
-
+import os
 import gradio as gr
-from inference import predict
-from logger import log_feedback_to_firebase
-from watchdog import manual_retrain
-from dashboard import render_dashboard
-from openai import OpenAI
 from watchdog import manual_retrain
+from inference import generate_response
+from logger import log_feedback
+from dashboard import display_dashboard
 
-import os
+# ✅ Ensure model is initialized
+if not os.path.exists("trained_model/config.json"):
+    print("📦 No model found in 'trained_model/'. Initializing...")
+    exec(open("init_save.py").read())
 
-# Initialize OpenAI client
-client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])  # Add this secret in your HF Space
+print("===== Application Startup at 2025-07-14 12:XX:XX =====")
 
-def gpt_predict(goal, sol1, sol2):
-    prompt = (
-        f"You're solving a commonsense reasoning problem. Choose the better solution to achieve the goal.\n\n"
-        f"Goal: {goal}\n\nOption 1: {sol1}\nOption 2: {sol2}\n\n"
-        f"Which option is better? Reply with only 'Solution 1' or 'Solution 2'."
-    )
-    try:
-        response = client.chat.completions.create(
-            model="gpt-3.5-turbo",
-            messages=[{"role": "user", "content": prompt}],
-            temperature=0
-        )
-        reply = response.choices[0].message.content
-        return "Solution 1" if "1" in reply else "Solution 2"
-    except Exception as e:
-        return f"GPT Error: {str(e)}"
+# ✅ Interface logic
+def evo_chat(goal, sol1, sol2):
+    response = generate_response(goal, sol1, sol2)
+    return response
 
-def compare(goal, sol1, sol2, correct):
-    evo = predict(goal, sol1, sol2)
-    gpt = gpt_predict(goal, sol1, sol2)
-
-    if evo == gpt == correct:
-        verdict = "✅ Both Evo and GPT-3.5 are correct!"
-    elif evo == correct and gpt != correct:
-        verdict = "🧠 Evo got it right. GPT-3.5 missed it."
-    elif gpt == correct and evo != correct:
-        verdict = "🤖 GPT-3.5 got it right. Evo missed it."
-    else:
-        verdict = "❌ Both models got it wrong."
-
-    log_feedback_to_firebase(goal, sol1, sol2, evo, gpt, correct, "from app.py")
-
-    return evo, gpt, verdict
-
-def trigger_retrain():
+def retrain_model():
     success = manual_retrain()
-    return "✅ Evo retrained successfully!" if success else "⚠️ Retraining failed."
-
-with gr.Blocks() as demo:
-    with gr.Tab("⚔️ Evo vs GPT-3.5 Showdown"):
-        gr.Markdown("## ⚔️ Evo vs GPT-3.5 – Real-Time Commonsense Showdown")
-        gr.Markdown(
-            "> 🧠 EvoTransformer v2.1 – PIQA Accuracy: 69.7% (vs GPT-3.5 ≈ 81%)\n"
-            "> 13M Parameters • Fully Scratch-Trained • Leans Smart\n"
-            "> 🧪 *Note: EvoTransformer is a scratch-built model trained on 1K PIQA examples. It may occasionally misinterpret context or idioms. That’s part of its evolution.*"
-        )
-        gr.Markdown(
-            "> 🔄 *EvoTransformer is not static. Every time you provide feedback, Evo learns and evolves. Welcome to real-time neural evolution.*"
-        )
+    return "✅ Retrained successfully!" if success else "⚠️ Retrain failed."
 
-        with gr.Row():
-            goal = gr.Text(label="Goal")
-        with gr.Row():
-            sol1 = gr.Text(label="Solution 1")
-            sol2 = gr.Text(label="Solution 2")
-        correct = gr.Radio(choices=["Solution 1", "Solution 2"], label="✅ Which is actually correct?", value="Solution 1")
+def log_user_feedback(goal, sol1, sol2, correct):
+    log_feedback(goal, sol1, sol2, correct)
+    return "✅ Feedback logged. Thank you!"
 
-        btn = gr.Button("Submit")
-        evo_out = gr.Text(label="🧠 EvoTransformer Response")
-        gpt_out = gr.Text(label="🤖 GPT-3.5 Response")
-        verdict_out = gr.Text(label="⚖️ Verdict")
+# ✅ UI
+with gr.Blocks(title="EvoTransformer v2.1") as demo:
+    gr.Markdown("### 🧠 EvoTransformer v2.1 – Compare Options and Learn")
 
-        btn.click(fn=compare, inputs=[goal, sol1, sol2, correct], outputs=[evo_out, gpt_out, verdict_out])
+    with gr.Row():
+        goal = gr.Textbox(label="Goal")
+    with gr.Row():
+        sol1 = gr.Textbox(label="Option 1")
+        sol2 = gr.Textbox(label="Option 2")
 
-        gr.Markdown("#### 🔍 Try These Examples:")
-        examples = [
-            ["Start a fire", "Use a match", "Pour water", "Solution 1"],
-            ["Warm up food", "Use microwave", "Put it in fridge", "Solution 1"],
-            ["Charge a phone", "Plug it in", "Put it on grass", "Solution 1"],
-            ["Get rid of bad smell", "Open window", "Close door", "Solution 1"],
-        ]
-        gr.Examples(examples=examples, inputs=[goal, sol1, sol2, correct])
+    output = gr.Textbox(label="Model Suggestion")
+    run_btn = gr.Button("🔍 Compare")
+    run_btn.click(fn=evo_chat, inputs=[goal, sol1, sol2], outputs=output)
 
-        retrain_btn = gr.Button("🔁 Retrain EvoTransformer")
-        retrain_status = gr.Text(label="📢 Retrain Status")
-        retrain_btn.click(fn=trigger_retrain, outputs=[retrain_status])
+    with gr.Row():
+        correct = gr.Radio(["Solution 1", "Solution 2"], label="Which was better?")
+        log_btn = gr.Button("✅ Log Feedback")
+        log_btn.click(fn=log_user_feedback, inputs=[goal, sol1, sol2, correct], outputs=None)
 
-        gr.Markdown("Made with ❤️ by Dr. Heman Mohabeer — EvoTransformer is not just code. It's evolution.")
+    with gr.Accordion("📊 Dashboard", open=False):
+        display_dashboard()
 
-    with gr.Tab("📊 Evo Dashboard"):
-        render_dashboard()
+    retrain_btn = gr.Button("♻️ Retrain Evo")
+    retrain_status = gr.Textbox(label="Retrain Status")
+    retrain_btn.click(fn=retrain_model, outputs=retrain_status)
 
 demo.launch()