Create app.py

app.py

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+import cv2
+import gradio as gr
+from ultralytics import YOLO
+
+# Load the YOLO model (update the path to your fire detection model weights)
+model = YOLO('path/to/your/best.pt')  # Replace 'path/to/your/best.pt' with the actual path to your model file
+
+def detect_fire(frame):
+    # Convert the frame to RGB format (YOLO expects this format)
+    img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    
+    # Perform fire detection using the YOLO model
+    results = model(img)
+
+    # Draw bounding boxes and labels on the detected fire areas
+    for bbox in results[0].boxes:
+        xyxy = bbox.xyxy[0]  # Bounding box coordinates
+        conf = bbox.conf[0]  # Confidence score
+        cls = int(bbox.cls[0])  # Class ID
+        label = model.names[cls]  # Class name
+
+        if label == "fire":  # Make sure this matches the label in your trained model
+            # Draw a rectangle around the detected fire
+            cv2.rectangle(img, (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3])), (255, 0, 0), 2)
+            # Put the label text above the rectangle
+            cv2.putText(img, f"{label} {conf:.2f}", (int(xyxy[0]), int(xyxy[1]) - 10), 
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+
+    # Convert the image back to BGR format for display
+    return cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+# Create a Gradio interface for fire detection
+iface = gr.Interface(
+    fn=detect_fire,
+    inputs=gr.Image(source="webcam", tool="editor", streaming=True),  # Use webcam as the input source
+    outputs="image",
+    title="Fire Detection using YOLO",
+    description="This application detects fire in real-time using a YOLO model."
+)
+
+# Launch the Gradio app
+iface.launch()