Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+# Load the model
+model = YOLO('best.pt')
+
+def get_intelligence(frame, box, label):
+    x1, y1, x2, y2 = map(int, box.xyxy[0])
+    w_px = x2 - x1
+    h_px = y2 - y1
+    
+    # Distance estimation logic
+    dist_m = (2.5 * 700) / w_px
+    dist_cat = "Near" if dist_m < 15 else "Medium" if dist_m < 30 else "Far"
+    
+    # Aspect ratio logic for direction
+    aspect_ratio = w_px / h_px
+    direction = "Front/Rear" if aspect_ratio < 1.2 else "Side View"
+    
+    return dist_cat, direction
+
+def predict(img):
+    results = model(img, conf=0.5)
+    annotated_img = img.copy()
+    
+    status_reports = []
+
+    for r in results:
+        for box in r.boxes:
+            cls_id = int(box.cls[0])
+            label = r.names[cls_id]
+            
+            dist, direction = get_intelligence(img, box, label)
+            
+            # Create a label for the report
+            report = f"Detected {label} ({direction}) - Distance: {dist}"
+            status_reports.append(report)
+            
+            # Draw on image
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            cv2.rectangle(annotated_img, (x1, y1), (x2, y2), (0, 255, 0), 3)
+            cv2.putText(annotated_img, f"{label} {dist}", (x1, y1 - 10), 
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+            
+    return annotated_img, "\n".join(status_reports) if status_reports else "No emergency vehicles detected."
+
+# Build the Gradio UI
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy"),
+    outputs=[gr.Image(type="numpy"), gr.Textbox(label="Intelligence Report")],
+    title="EVobj: Emergency Vehicle Detection System",
+    description="Upload an image of traffic to detect emergency vehicles and estimate proximity."
+)
+
+if __name__ == "__main__":
+    demo.launch()