Update app.py

app.py

@@ -4,52 +4,56 @@ import cv2
 import numpy as np
 from PIL import Image
 
-# Load the YOLO model (replace with your model path)
-model = YOLO("best.pt")  # Use your YOLO model file here
+model = YOLO("best.pt") 
+model.predict(source=0,imgsize=640,conf=0.6,show=True)
 
-st.title("Fire Detection in Forest")
 
-# Sidebar for input options
-input_option = st.sidebar.selectbox("Select Input Method", ["Upload Image", "Use Webcam", "Upload Video"])
+# # Load the YOLO model (replace with your model path)
+# model = YOLO("best.pt")  # Use your YOLO model file here
 
-if input_option == "Upload Image":
-    # Upload Image
-    uploaded_file = st.file_uploader("Choose an Image", type=["jpg", "jpeg", "png"])
+# st.title("Fire Detection in Forest")
 
-    if uploaded_file is not None:
-        img = Image.open(uploaded_file)
-        st.image(img, caption='User Image')
-        st.write("Classifying...")
+# # Sidebar for input options
+# input_option = st.sidebar.selectbox("Select Input Method", ["Upload Image", "Use Webcam", "Upload Video"])
 
-        # Convert image to numpy array
-        img_np = np.array(img)
+# if input_option == "Upload Image":
+#     # Upload Image
+#     uploaded_file = st.file_uploader("Choose an Image", type=["jpg", "jpeg", "png"])
 
-        # Make predictions
-        results = model.predict(source=img_np, conf=0.5)
+#     if uploaded_file is not None:
+#         img = Image.open(uploaded_file)
+#         st.image(img, caption='User Image')
+#         st.write("Classifying...")
 
-        # Variable to check if fire is detected
-        fire_detected = False
+#         # Convert image to numpy array
+#         img_np = np.array(img)
 
-        # Draw bounding boxes on the image
-        for result in results:
-            boxes = result.boxes.xyxy
-            for box in boxes:
-                x1, y1, x2, y2 = box[:4].astype(int)
-                img_np = cv2.rectangle(img_np, (x1, y1), (x2, y2), (0, 255, 0), 2)
+#         # Make predictions
+#         results = model.predict(source=img_np, conf=0.5)
+
+#         # Variable to check if fire is detected
+#         fire_detected = False
+
+#         # Draw bounding boxes on the image
+#         for result in results:
+#             boxes = result.boxes.xyxy
+#             for box in boxes:
+#                 x1, y1, x2, y2 = box[:4].astype(int)
+#                 img_np = cv2.rectangle(img_np, (x1, y1), (x2, y2), (0, 255, 0), 2)
 
-                # Check if the detected class is "fire" (adjust based on your model's class mapping)
-                class_id = int(box[5])  # Assuming class ID is at the 6th position
-                if class_id == 0:  # Replace 0 with the actual class ID for fire if different
-                    fire_detected = True
+#                 # Check if the detected class is "fire" (adjust based on your model's class mapping)
+#                 class_id = int(box[5])  # Assuming class ID is at the 6th position
+#                 if class_id == 0:  # Replace 0 with the actual class ID for fire if different
+#                     fire_detected = True
 
-        # Show the resulting image
-        st.image(img_np, caption='Detected Fire', use_column_width=True)
+#         # Show the resulting image
+#         st.image(img_np, caption='Detected Fire', use_column_width=True)
 
-        # Display message based on fire detection
-        if fire_detected:
-            st.success("🔥 Fire Detected!")
-        else:
-            st.warning("No Fire Detected.")
+#         # Display message based on fire detection
+#         if fire_detected:
+#             st.success("🔥 Fire Detected!")
+#         else:
+#             st.warning("No Fire Detected.")
 
 
 # elif input_option == "Use Webcam":