Format codebase with uv run pre-commit run --all-files

libs/python/agent/benchmarks/interactive.py

@@ -9,60 +9,56 @@ Models are loaded/unloaded one at a time to avoid memory issues.
 import asyncio
 import os
 from datetime import datetime
-from typing import List, Dict, Any
+from typing import Any, Dict, List
 
 from utils import (
     ModelWrapper,
-    take_screenshot,
+    get_available_models,
     save_prediction_visualization,
-    get_available_models
+    take_screenshot,
 )
 
 
 async def predict_with_all_models(image, instruction: str, models) -> List[Dict[str, Any]]:
     """
     Predict click coordinates with all models sequentially.
-    
+
     Args:
         image: PIL Image to analyze
         instruction: Instruction text
         models: List of model instances
-        
+
     Returns:
         List of prediction results
     """
     predictions = []
-    
+
     for model in models:
         model_wrapper = ModelWrapper(model)
         print(f"\n🔄 Loading {model_wrapper.model_name}...")
-        
+
         try:
             # Load model
             await model_wrapper.load_model()
-            
+
             # Predict
             coords = await model_wrapper.predict_click(image, instruction)
-            
-            predictions.append({
-                'model_name': model_wrapper.model_name,
-                'coords': coords,
-                'error': None
-            })
-            
+
+            predictions.append(
+                {"model_name": model_wrapper.model_name, "coords": coords, "error": None}
+            )
+
             if coords:
                 print(f"✅ {model_wrapper.model_name}: ({coords[0]}, {coords[1]})")
             else:
                 print(f"❌ {model_wrapper.model_name}: No prediction")
-                
+
         except Exception as e:
             print(f"❌ {model_wrapper.model_name}: ERROR - {str(e)}")
-            predictions.append({
-                'model_name': model_wrapper.model_name,
-                'coords': None,
-                'error': str(e)
-            })
-        
+            predictions.append(
+                {"model_name": model_wrapper.model_name, "coords": None, "error": str(e)}
+            )
+
         finally:
             # Always unload model to free memory
             try:
@@ -70,7 +66,7 @@ async def predict_with_all_models(image, instruction: str, models) -> List[Dict[
                 print(f"🗑️  Unloaded {model_wrapper.model_name}")
             except Exception as e:
                 print(f"⚠️  Error unloading {model_wrapper.model_name}: {e}")
-    
+
     return predictions
 
 
@@ -103,87 +99,91 @@ async def main():
     Main interactive loop.
     """
     print_header()
-    
+
     # Get available models
     models = get_available_models()
     print_models(models)
-    
+
     # Create output directory for visualizations
     output_dir = "interactive_output"
     os.makedirs(output_dir, exist_ok=True)
-    
+
     session_count = 0
     last_screenshot = None
     screenshot_timestamp = None
-    
+
     while True:
         try:
             # Get user input
             print(f"\n{'='*40}")
             user_input = input("🎯 Enter instruction (or command): ").strip()
-            
+
             if not user_input:
                 continue
-                
+
             # Handle commands
-            if user_input.lower() in ['quit', 'exit', 'q']:
+            if user_input.lower() in ["quit", "exit", "q"]:
                 print("👋 Goodbye!")
                 break
-                
-            elif user_input.lower() == 'models':
+
+            elif user_input.lower() == "models":
                 print_models(models)
                 continue
-                
-            elif user_input.lower() == 'screenshot':
+
+            elif user_input.lower() == "screenshot":
                 print("📸 Taking screenshot...")
                 try:
                     last_screenshot = take_screenshot()
                     screenshot_timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-                    screenshot_path = os.path.join(output_dir, f"screenshot_{screenshot_timestamp}.png")
+                    screenshot_path = os.path.join(
+                        output_dir, f"screenshot_{screenshot_timestamp}.png"
+                    )
                     last_screenshot.save(screenshot_path)
                     print(f"✅ Screenshot captured and saved to: {screenshot_path}")
                     print(f"📝 Ready for instructions! Screenshot size: {last_screenshot.size}")
                 except Exception as e:
                     print(f"❌ Error taking screenshot: {e}")
                 continue
-            
+
             # Handle instruction input
             if last_screenshot is None:
-                print("⚠️  No screenshot available! Please take a screenshot first using 'screenshot' command.")
+                print(
+                    "⚠️  No screenshot available! Please take a screenshot first using 'screenshot' command."
+                )
                 continue
-                
+
             session_count += 1
             print(f"\n🎯 Session {session_count}: '{user_input}'")
             print(f"📷 Using screenshot from: {screenshot_timestamp}")
-            
+
             # Predict with all models using last screenshot
             print(f"\n🤖 Testing {len(models)} models on screenshot...")
             predictions = await predict_with_all_models(last_screenshot, user_input, models)
-            
+
             # Display results summary
-            print(f"\n📊 Results Summary:")
+            print("\n📊 Results Summary:")
             print("-" * 50)
             for pred in predictions:
-                if pred['coords']:
+                if pred["coords"]:
                     print(f"✅ {pred['model_name']}: ({pred['coords'][0]}, {pred['coords'][1]})")
-                elif pred['error']:
+                elif pred["error"]:
                     print(f"❌ {pred['model_name']}: ERROR - {pred['error']}")
                 else:
                     print(f"❌ {pred['model_name']}: No prediction")
-            
+
             # Save visualization
             timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
             vis_filename = f"session_{session_count:03d}_{timestamp}.png"
             vis_path = os.path.join(output_dir, vis_filename)
-            
+
             try:
                 save_prediction_visualization(last_screenshot, user_input, predictions, vis_path)
                 print(f"\n💾 Visualization saved to: {vis_path}")
             except Exception as e:
                 print(f"⚠️  Error saving visualization: {e}")
-            
+
             print(f"\n✨ Session {session_count} completed!")
-            
+
         except KeyboardInterrupt:
             print("\n\n👋 Interrupted by user. Goodbye!")
             break