Update app.py

app.py

@@ -1,11 +1,11 @@
-import streamlit as st
-from ultralytics import YOLO
-import cv2
-import numpy as np
-from PIL import Image
+# import streamlit as st
+# from ultralytics import YOLO
+# import cv2
+# import numpy as np
+# from PIL import Image
 
-model = YOLO("best.pt") 
-model.predict(source=0,imgsize=640,conf=0.6,show=True)
+# model = YOLO("best.pt") 
+# model.predict(source=0,imgsize=640,conf=0.6,show=True)
 
 
 # # Load the YOLO model (replace with your model path)
@@ -151,5 +151,33 @@ model.predict(source=0,imgsize=640,conf=0.6,show=True)
 #         # Release the video capture
 #         video_capture.release()
 
+
+import torch
+import gradio as gr
+from PIL import Image
+
+# Load the YOLOv11 model
+model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', source='local')  # replace 'yolov5' with 'yolo11' if needed
+
+# Define the prediction function
+def detect_fire(image):
+    # Run the YOLO model on the uploaded image
+    results = model(image)
+    # Check if fire is detected based on the model's output classes
+    detected = any('fire' in results.names[label] for label in results.xyxy[0][:, -1].tolist())
+    return "Fire Detected" if detected else "No Fire Detected"
+
+# Create and launch the Gradio interface
+interface = gr.Interface(
+    fn=detect_fire,
+    inputs=gr.Image(type='pil', label="Upload Image"),
+    outputs="text",
+    title="Fire Detection with YOLOv11",
+    description="Upload an image to classify whether fire is detected or not."
+)
+
+interface.launch()
+
+