Update app.py

app.py

@@ -5,4 +5,48 @@ import streamlit as st
 import numpy as np
 from PIL import Image
 model=YOLO("best.pt")
+# Set the title of the app
+st.title("Fire Detection App")
+
+# Sidebar for input options
+input_option = st.sidebar.selectbox("Select Input Method", ["Upload Image"])
+
+if input_option == "Upload Image":
+    # Upload Image
+    uploaded_file = st.file_uploader("Choose an Image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Open and display the uploaded image
+        img = Image.open(uploaded_file)
+        st.image(img, caption='User Image', use_column_width=True)
+        
+        # Convert the image to a numpy array
+        img_np = np.array(img)
+
+        # Make predictions
+        results = model.predict(source=img_np, conf=0.5)  # Adjust confidence threshold as needed
+
+        # Variable to check if fire is detected
+        fire_detected = False
+
+        # Draw bounding boxes on the image
+        for result in results:
+            boxes = result.boxes.xyxy  # Bounding boxes
+            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
+
+        # Show the resulting image with bounding boxes
+        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.")