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| import os | |
| # 🛡️ Ensure model is saved before importing anything that loads it | |
| if not os.path.exists("trained_model/config.json"): | |
| print("⚙️ No model found. Initializing and saving EvoTransformer...") | |
| from init_model import initialize_and_save_model | |
| initialize_and_save_model() | |
| else: | |
| print("✅ EvoTransformer already initialized.") | |
| import gradio as gr | |
| import random | |
| from inference import generate_response | |
| from logger import log_user_feedback | |
| from dashboard import update_dashboard_plot | |
| from watchdog import retrain_model | |
| # 🎲 Random examples | |
| examples = [ | |
| { | |
| "goal": "Escape from a burning house", | |
| "option1": "Run out through the front door", | |
| "option2": "Hide in the bathroom" | |
| }, | |
| { | |
| "goal": "Improve sleep quality", | |
| "option1": "Use phone in bed", | |
| "option2": "Turn off screens 1 hour before bed" | |
| }, | |
| { | |
| "goal": "Increase productivity at work", | |
| "option1": "Multitask all day", | |
| "option2": "Use Pomodoro technique" | |
| }, | |
| { | |
| "goal": "Lose weight safely", | |
| "option1": "Skip meals", | |
| "option2": "Exercise regularly and eat balanced meals" | |
| }, | |
| { | |
| "goal": "Ace an exam", | |
| "option1": "Cram the night before", | |
| "option2": "Study consistently for 2 weeks" | |
| }, | |
| ] | |
| def load_random_example(): | |
| example = random.choice(examples) | |
| return example["goal"], example["option1"], example["option2"] | |
| # 🧠 Model suggestion UI | |
| 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") | |
| with gr.Row(): | |
| compare_btn = gr.Button("🔍 Compare") | |
| random_btn = gr.Button("🎲 Load Random Example") | |
| 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] | |
| ) | |
| random_btn.click( | |
| fn=load_random_example, | |
| inputs=[], | |
| outputs=[goal_input, option1_input, option2_input] | |
| ) | |
| 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() | |
| dashboard_plot = update_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) | |