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app.py

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+# Step 1: Import libraries
+from transformers import DetrImageProcessor, DetrForObjectDetection
+from PIL import Image, ImageDraw
+import torch
+import gradio as gr
+
+# Step 2: Load model and processor
+model = DetrForObjectDetection.from_pretrained("hilmantm/detr-traffic-accident-detection")
+processor = DetrImageProcessor.from_pretrained("hilmantm/detr-traffic-accident-detection")
+
+# Step 3: Define the inference logic
+def detect_traffic_accidents(image):
+    # Preprocess image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Run model inference
+    outputs = model(**inputs)
+
+    # Extract detections
+    target_sizes = torch.tensor([image.size[::-1]])  # Width, height
+    results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
+    
+    # Annotate image with bounding boxes
+    draw = ImageDraw.Draw(image)
+    for box, score, label in zip(results["boxes"], results["scores"], results["labels"]):
+        if score > 0.9:  # Confidence threshold
+            x_min, y_min, x_max, y_max = box
+            draw.rectangle([x_min, y_min, x_max, y_max], outline="red", width=3)
+            draw.text((x_min, y_min), f"{model.config.id2label[label]}: {score:.2f}", fill="red")
+
+    return image
+
+# Step 4: Define the Gradio Interface
+def api_endpoint(input_image):
+    output_image = detect_traffic_accidents(input_image)
+    return output_image
+
+# Step 5: Launch Gradio app
+iface = gr.Interface(
+    fn=api_endpoint,
+    inputs=gr.Image(type="pil"),  # Accepts images
+    outputs=gr.Image(type="pil"),  # Returns annotated image
+)
+
+iface.launch(server_name="0.0.0.0", server_port=7860)  # Accessible via LAN/WAN