Create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load pretrained DETR model for object detection
+detection_model = pipeline("object-detection", model="facebook/detr-resnet-50")
+
+# Function to assess vehicle damage
+def assess_vehicle_damage(image):
+    try:
+        # Use the model to predict object locations and labels
+        predictions = detection_model(image)
+
+        # Format results to highlight detected objects and potential damage
+        report = "🔍 Vehicle Damage Assessment:\n"
+        for pred in predictions:
+            label = pred['label']
+            score = pred['score']
+            box = pred['box']
+            report += (
+                f"- {label} detected with confidence {score:.2f}.\n"
+                f"  Location: (X: {box['xmin']:.1f}, Y: {box['ymin']:.1f}, "
+                f"Width: {box['xmax'] - box['xmin']:.1f}, Height: {box['ymax'] - box['ymin']:.1f})\n"
+            )
+
+        # Add general recommendations based on detected objects
+        report += "\n💡 Recommendations:\n"
+        if any("car" in pred['label'].lower() for pred in predictions):
+            report += "- Inspect detected areas closely for damage severity.\n"
+        else:
+            report += "- No visible vehicle parts detected. Please upload a clearer image.\n"
+
+        return report
+    except Exception as e:
+        return f"Error processing the image: {e}"
+
+# Gradio interface
+interface = gr.Interface(
+    fn=assess_vehicle_damage,
+    inputs=gr.Image(type="file", label="Upload Vehicle Image"),
+    outputs=gr.Textbox(label="Damage Assessment Report"),
+    title="Vehicle Damage Assessor",
+    description=(
+        "Upload an image of a vehicle to detect damaged parts and get an assessment report. "
+        "The app uses advanced AI models to identify objects and predict potential issues."
+    ),
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    interface.launch()