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import os
# ✅ Check if trained_model/config.json exists, if not, initialize the model
if not os.path.exists("trained_model/config.json"):
from init_model import initialize_and_save_model
print("⚙️ Reinitializing EvoTransformer model...")
initialize_and_save_model()
import gradio as gr
from inference import generate_response
from logger import log_user_feedback
from dashboard import update_dashboard_plot
from watchdog import retrain_model
evo_output = gr.Textbox(label="🧠 EvoTransformer Suggestion")
gpt_output = gr.Textbox(label="💬 GPT-3.5 Suggestion")
feedback_output = gr.Textbox(visible=False)
def evo_chat(goal, sol1, sol2):
response = generate_response(goal, sol1, sol2)
evo = response.get("evo_suggestion", "Error")
gpt = response.get("gpt_suggestion", "Error")
return evo, gpt
def handle_feedback(goal, sol1, sol2, winner):
try:
log_user_feedback(goal, sol1, sol2, winner)
return "✅ Feedback logged. Thank you!"
except Exception as e:
return f"❌ Failed to log: {e}"
with gr.Blocks(title="EvoTransformer v2.1 – Compare Options and Learn") as demo:
gr.Markdown("## 🧠 EvoTransformer v2.1 – Compare Options and Learn")
with gr.Row():
goal_input = gr.Textbox(label="Goal", placeholder="e.g. Escape from house on fire")
with gr.Row():
option1_input = gr.Textbox(label="Option 1", placeholder="e.g. Exit house through main door")
option2_input = gr.Textbox(label="Option 2", placeholder="e.g. Hide under bed")
compare_btn = gr.Button("🔍 Compare")
with gr.Row():
evo_output.render()
gpt_output.render()
with gr.Row():
winner_dropdown = gr.Radio(["Solution 1", "Solution 2"], label="Which was better?")
feedback_btn = gr.Button("✅ Log Feedback")
feedback_output.render()
compare_btn.click(
fn=evo_chat,
inputs=[goal_input, option1_input, option2_input],
outputs=[evo_output, gpt_output]
)
feedback_btn.click(
fn=handle_feedback,
inputs=[goal_input, option1_input, option2_input, winner_dropdown],
outputs=[feedback_output]
)
with gr.Row():
gr.Markdown("### 📊 Dashboard")
dashboard_plot = gr.Plot()
update_dashboard_plot(dashboard_plot)
with gr.Row():
retrain_button = gr.Button("♻️ Retrain Evo")
retrain_status = gr.Textbox(label="Retrain Status")
retrain_button.click(fn=retrain_model, inputs=[], outputs=[retrain_status])
if __name__ == "__main__":
demo.launch(share=True)
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