Update app.py

app.py

@@ -57,42 +57,85 @@
 
 # # Launch the Gradio app
 # iface.launch()
-import gradio as gr
-import PIL.Image as Image
-from ultralytics import YOLO
+# import gradio as gr
+# import PIL.Image as Image
+# from ultralytics import YOLO
 
-# Load the YOLOv8 model
-model = YOLO("best.pt")
+# # Load the YOLOv8 model
+# model = YOLO("best.pt")
 
-def predict_image(img, conf_threshold, iou_threshold):
-    """Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
-    results = model.predict(
-        source=img,
-        conf=conf_threshold,
-        iou=iou_threshold,
-        show_labels=True,
-        show_conf=True,
-        imgsz=640,
-    )
+# def predict_image(img, conf_threshold, iou_threshold):
+#     """Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds."""
+#     results = model.predict(
+#         source=img,
+#         conf=conf_threshold,
+#         iou=iou_threshold,
+#         show_labels=True,
+#         show_conf=True,
+#         imgsz=640,
+#     )
 
-    for r in results:
-        im_array = r.plot()
-        im = Image.fromarray(im_array[..., ::-1])
+#     for r in results:
+#         im_array = r.plot()
+#         im = Image.fromarray(im_array[..., ::-1])
 
-    return im
+#     return im
+
+# iface = gr.Interface(
+#     fn=predict_image,
+#     inputs=[
+#         gr.Image(source="webcam", type="pil", label="Capture Image"),
+#         gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
+#         gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
+#     ],
+#     outputs=gr.Image(type="pil", label="Result"),
+#     live=True,  # Enables real-time processing
+#     title="Ultralytics Gradio",
+#     description="Capture images from your webcam for real-time inference using the Ultralytics YOLOv8n model.",
+# )
 
+# if __name__ == "__main__":
+#     iface.launch()
+
+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=predict_image,
-    inputs=[
-        gr.Image(source="webcam", type="pil", label="Capture Image"),
-        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
-        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
-    ],
-    outputs=gr.Image(type="pil", label="Result"),
-    live=True,  # Enables real-time processing
-    title="Ultralytics Gradio",
-    description="Capture images from your webcam for real-time inference using the Ultralytics YOLOv8n model.",
+    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."
 )
 
-if __name__ == "__main__":
-    iface.launch()
+# Launch the Gradio app
+iface.launch()