Update app.py

app.py

@@ -1,59 +1,98 @@
-import cv2
-import numpy as np
-import gradio as gr
-import torch
-from ultralytics import YOLO
-# Load the YOLO model (adjust the path to your model weights and config)
-model = torch.hub.load('best.pt')  # Change to your model path
+# import cv2
+# import numpy as np
+# import gradio as gr
+# import torch
+# from ultralytics import YOLO
+# # Load the YOLO model (adjust the path to your model weights and config)
+# model = torch.hub.load('best.pt')  # Change to your model path
 
-def detect_fire(frame):
-    # Convert the frame to RGB (YOLO models usually expect this format)
-    img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+# def detect_fire(frame):
+#     # Convert the frame to RGB (YOLO models usually expect this format)
+#     img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     
-    # Use the model to detect objects
-    results = model(img)
+#     # Use the model to detect objects
+#     results = model(img)
 
-    # Get the predictions
-    predictions = results.pred[0]  # Assuming a single image
+#     # Get the predictions
+#     predictions = results.pred[0]  # Assuming a single image
 
-    # Draw boxes on the detected fire objects
-    for *xyxy, conf, cls in predictions:
-        label = model.names[int(cls)]
-        if label == "fire":  # Adjust based on your class name for fire
-            cv2.rectangle(img, (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3])), (255, 0, 0), 2)
-            cv2.putText(img, f"{label} {conf:.2f}", (int(xyxy[0]), int(xyxy[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+#     # Draw boxes on the detected fire objects
+#     for *xyxy, conf, cls in predictions:
+#         label = model.names[int(cls)]
+#         if label == "fire":  # Adjust based on your class name for fire
+#             cv2.rectangle(img, (int(xyxy[0]), int(xyxy[1])), (int(xyxy[2]), int(xyxy[3])), (255, 0, 0), 2)
+#             cv2.putText(img, f"{label} {conf:.2f}", (int(xyxy[0]), int(xyxy[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
 
-    return img
+#     return img
 
-def webcam_demo():
-    # Start video capture from webcam
-    cap = cv2.VideoCapture(0)
+# def webcam_demo():
+#     # Start video capture from webcam
+#     cap = cv2.VideoCapture(0)
 
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
+#     while True:
+#         ret, frame = cap.read()
+#         if not ret:
+#             break
         
-        # Detect fire in the current frame
-        frame_with_detections = detect_fire(frame)
+#         # Detect fire in the current frame
+#         frame_with_detections = detect_fire(frame)
+
+#         # Display the result
+#         cv2.imshow("Fire Detection", frame_with_detections)
+
+#         if cv2.waitKey(1) & 0xFF == ord('q'):
+#             break
+
+#     cap.release()
+#     cv2.destroyAllWindows()
+
+# # Create a Gradio interface
+# iface = gr.Interface(
+#     fn=webcam_demo,
+#     inputs=[],
+#     outputs="image",
+#     title="Fire Detection using Webcam",
+#     description="This application detects fire using a webcam feed."
+# )
+
+# # Launch the Gradio app
+# iface.launch()
+import gradio as gr
+import PIL.Image as Image
+from ultralytics import YOLO
+
+# Load the YOLOv8 model
+model = YOLO("best.pt")
 
-        # Display the result
-        cv2.imshow("Fire Detection", frame_with_detections)
+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,
+    )
 
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
 
-    cap.release()
-    cv2.destroyAllWindows()
+    return im
 
-# Create a Gradio interface
 iface = gr.Interface(
-    fn=webcam_demo,
-    inputs=[],
-    outputs="image",
-    title="Fire Detection using Webcam",
-    description="This application detects fire using a webcam feed."
+    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.",
 )
 
-# Launch the Gradio app
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()