Update app.py

app.py

@@ -1,87 +1,107 @@
-# app.py
 import gradio as gr
-import json
-import csv
-import io
 from evo_transformer import EvoTransformer
 from plot import plot_radar_chart
+import tempfile
+import json
+import csv
+from PIL import Image
 
-# Global instance
+# Initialize EvoTransformer
 evo = EvoTransformer()
 
-def evolve_model(num_generations):
-    evo.reset()  # reset before evolution
-    evo.evolve(num_generations)
-    final_config = evo.config
-    history = evo.get_history()
-    evaluation = evo.evaluate()
-
-    # Radar Chart
-    try:
-        radar_img = plot_radar_chart(final_config)
-    except Exception as e:
-        print("Radar chart error:", e)
-        radar_img = None
-
-    # Prepare history display
-    history_cards = []
-    for i, h in enumerate(history):
-        text = f"Gen {i + 1} Config: {h}"
-        history_cards.append(text)
-
-    # Prepare CSV and JSON
-    csv_file = io.StringIO()
-    csv_writer = csv.DictWriter(csv_file, fieldnames=history[0].keys())
-    csv_writer.writeheader()
-    csv_writer.writerows(history)
-    csv_bytes = io.BytesIO(csv_file.getvalue().encode())
-
-    json_file = io.StringIO(json.dumps(history, indent=2))
-    json_bytes = io.BytesIO(json_file.getvalue().encode())
+def evolve_model(generations):
+    evo.reset()  # Reset state
+    evo.run_evolution(generations)
+
+    # Get final config
+    final_config = evo.history[-1]
+    summary = f"layers: {final_config['layers']}\nattention_heads: {final_config['attention_heads']}\n"
+    summary += f"ffn_dim: {final_config['ffn_dim']}\ndropout: {final_config['dropout']}\n"
+    summary += f"memory: {final_config['memory']}"
+
+    # Simulate results
+    accuracy = round(final_config['layers'] * 1.23 + final_config['dropout'] * 10, 2)
+    params = round(final_config['layers'] * final_config['ffn_dim'] * final_config['attention_heads'] / 1000, 2)
+    accuracy_str = f"{accuracy:.2f}%"
+    params_str = f"{params:.2f}M params"
+
+    # Radar chart
+    radar_img = plot_radar_chart(final_config)
+
+    # History logs
+    gen_configs = []
+    for i, cfg in enumerate(evo.history):
+        gen_configs.append(f"Gen {i+1} Config: {cfg}")
+
+    # Write CSV
+    csv_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode='w', newline='')
+    writer = csv.DictWriter(csv_file, fieldnames=final_config.keys())
+    writer.writeheader()
+    writer.writerows(evo.history)
+    csv_file.close()
+
+    # Write JSON
+    json_file = tempfile.NamedTemporaryFile(delete=False, suffix=".json", mode='w')
+    json.dump(evo.history, json_file)
+    json_file.close()
+
+    # Pad history to 10 generations
+    while len(gen_configs) < 10:
+        gen_configs.append("")
 
     return (
-        f"{evaluation['accuracy']:.2f}%",
-        f"{evaluation['params']:.2f}M params",
-        str(final_config),
-        radar_img,
-        None,  # placeholder for future diagram
-        *history_cards,
-        ("evo_history.csv", csv_bytes),
-        ("evo_history.json", json_bytes)
+        accuracy_str,             # 1
+        params_str,               # 2
+        summary,                  # 3
+        radar_img,                # 4
+        None,                     # 5 (diagram placeholder)
+        gen_configs[0],           # 6
+        gen_configs[1],           # 7
+        gen_configs[2],           # 8
+        gen_configs[3],           # 9
+        gen_configs[4],           # 10
+        gen_configs[5],           # 11
+        gen_configs[6],           # 12
+        gen_configs[7],           # 13
+        gen_configs[8],           # 14
+        gen_configs[9],           # 15
+        csv_file.name,            # 16
+        json_file.name            # 17
     )
 
+# Gradio UI
 with gr.Blocks(title="🧬 EvoTransformer – Evolving Transformer Architectures") as demo:
     gr.Markdown("## 🧬 EvoTransformer – Evolving Transformer Architectures\nSimulate trait mutation and adaptive architecture generation.")
 
     with gr.Row():
-        num_generations = gr.Slider(minimum=1, maximum=10, value=3, label="Number of Generations", step=1)
+        generations_input = gr.Slider(minimum=1, maximum=10, step=1, value=3, label="Number of Generations")
         evolve_btn = gr.Button("🧬 Evolve Architecture")
 
     with gr.Row():
-        accuracy_text = gr.Textbox(label="Simulated Accuracy")
-        param_text = gr.Textbox(label="Estimated Parameters")
-        config_text = gr.Textbox(label="Current Config Summary")
+        accuracy_output = gr.Textbox(label="Simulated Accuracy")
+        params_output = gr.Textbox(label="Estimated Parameters")
+        summary_output = gr.Textbox(label="Current Config Summary")
 
     gr.Markdown("## 🧬 Evolution History")
+    radar_output = gr.Image(label="Final Generation Trait Radar", type="pil")
+    diagram_output = gr.Image(label="Illustrative Transformer Structure")  # Not used, just placeholder
 
-    radar_output = gr.Image(label="Final Generation Trait Radar", type="pil", interactive=False)
-    diagram_output = gr.Image(label="Illustrative Transformer Structure", visible=False)
-
-    history_boxes = [gr.Textbox(label=f"Gen {i+1} Config") for i in range(10)]
+    with gr.Accordion("Downloadable Files", open=False):
+        csv_output = gr.File(label="Download CSV History")
+        json_output = gr.File(label="Download JSON History")
 
-    with gr.Accordion("Downloadable Files", open=True):
-        csv_out = gr.File(label="Download CSV History")
-        json_out = gr.File(label="Download JSON History")
+    gen_outputs = [gr.Textbox(label=f"Gen {i+1} Config") for i in range(10)]
 
     evolve_btn.click(
-        fn=evolve_model,
-        inputs=[num_generations],
+        evolve_model,
+        inputs=[generations_input],
         outputs=[
-            accuracy_text, param_text, config_text,
+            accuracy_output, params_output, summary_output,
             radar_output, diagram_output,
-            *history_boxes,
-            csv_out, json_out
+            *gen_outputs,
+            csv_output, json_output
         ]
     )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()