Upload 3 files

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image
+from ultralytics import YOLO
+
+# Load the YOLO model
+model = YOLO('best.pt')
+
+def predict(image):
+    # Convert PIL Image to numpy array
+    img_array = np.array(image)
+    
+    # Perform inference
+    results = model(img_array)
+    
+    # Plot results
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+    
+    # Get detection counts and confidences
+    class_counts = {i: 0 for i in range(len(model.names))}
+    class_confidences = {i: [] for i in range(len(model.names))}
+    
+    for box in results[0].boxes:
+        cls = int(box.cls[0])
+        conf = float(box.conf[0])
+        class_counts[cls] += 1
+        class_confidences[cls].append(conf)
+    
+    # Create output string
+    output = "Detection Results:\n"
+    for i in range(len(model.names)):
+        count = class_counts[i]
+        avg_conf = np.mean(class_confidences[i]) if class_confidences[i] else 0
+        output += f"{model.names[i]}: {count} detections (Avg. Confidence: {avg_conf:.2f})\n"
+    
+    return im, output
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Image(type="pil", label="Detected Image"),
+        gr.Textbox(label="Detection Results")
+    ],
+    title="Helmet and License Plate Detection",
+    description="Upload an image to detect helmets, license plates, and motorcyclists."
+)
+
+# Launch the interface
+iface.launch()

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e339c22b8c597e5702dd82873b68147fc7e5cf94f0c3ee865901f62686b3e9c8
+size 5591279

requirements.txt

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+streamlit
+ultralytics
+Pillow
+numpy
+opencv-python-headless