Create app.py

app.py

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+import torch
+import gradio as gr
+import numpy as np
+import pandas as pd
+from ultralytics import YOLO
+import cv2
+
+# Load the trained YOLO model
+model = YOLO('best.pt')
+
+def predict_image(image):
+    """
+    Perform object detection on the input image
+    
+    Args:
+        image (numpy.ndarray): Input image
+    
+    Returns:
+        tuple: Annotated image, results dataframe, precision-recall table
+    """
+    # Perform prediction
+    results = model(image)[0]
+    
+    # Create a copy of the image for annotation
+    annotated_image = image.copy()
+    
+    # Prepare results for display
+    detection_results = []
+    
+    # Draw bounding boxes and collect detection info
+    for box in results.boxes:
+        # Extract box coordinates
+        x1, y1, x2, y2 = box.xyxy[0].int().tolist()
+        
+        # Get class and confidence
+        cls = int(box.cls[0])
+        conf = float(box.conf[0])
+        class_name = model.names[cls]
+        
+        # Draw bounding box
+        cv2.rectangle(annotated_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        
+        # Put text on the image
+        label = f'{class_name} {conf:.2f}'
+        cv2.putText(annotated_image, label, (x1, y1-10), 
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+        
+        # Collect detection details
+        detection_results.append({
+            'Class': class_name,
+            'Confidence': f'{conf:.4f}',
+            'Bounding Box': f'[{x1},{y1},{x2},{y2}]'
+        })
+    
+    # Create DataFrame for detections
+    detections_df = pd.DataFrame(detection_results)
+    
+    # Generate precision-recall metrics (placeholder - you'll need to replace with actual metrics)
+    pr_metrics = generate_pr_metrics()
+    
+    return annotated_image, detections_df, pr_metrics
+
+def generate_pr_metrics():
+    """
+    Generate placeholder precision and recall metrics
+    
+    Note: Replace this with actual calculations from your training results
+    """
+    # This is a placeholder - you should replace with actual metrics from your training
+    metrics_data = {
+        'Class': model.names.values(),
+        'Precision': [0.85, 0.90, 0.88],  # Example values
+        'Recall': [0.80, 0.85, 0.82]      # Example values
+    }
+    
+    metrics_df = pd.DataFrame(metrics_data)
+    return metrics_df
+
+# Create Gradio interface
+def launch_interface():
+    # Input image upload
+    image_input = gr.Image(type="numpy", label="Upload Image")
+    
+    # Outputs
+    annotated_image_output = gr.Image(label="Annotated Image")
+    detections_output = gr.Dataframe(label="Detection Results")
+    metrics_output = gr.Dataframe(label="Precision-Recall Metrics")
+    
+    # Gradio interface
+    demo = gr.Interface(
+        fn=predict_image,
+        inputs=image_input,
+        outputs=[
+            annotated_image_output, 
+            detections_output, 
+            metrics_output
+        ],
+        title="YOLO Object Detection",
+        description="Upload an image for object detection"
+    )
+    
+    return demo
+
+# Launch the app
+if __name__ == "__main__":
+    demo = launch_interface()
+    demo.launch()