Update app.py

app.py

@@ -1,22 +1,30 @@
 import gradio as gr
+import torch
+from PIL import Image
+import numpy as np
 
-# Define the function to be used in the interface
-def greet(name):
-    return "Hello " + name + "!!"
+# Load your YOLO model (Make sure the model is in PyTorch format)
+model = torch.hub.load('ultralytics/yolov5', 'yolov8', pretrained=True)  # Replace with YOLOv8
 
-# Create a description for the interface
+# Define the prediction function for YOLO model
+def predict(image):
+    image = Image.fromarray(np.array(image))  # Convert image to PIL format
+    results = model(image)  # Get predictions from YOLO model
+    return f"Predicted class: {results.names[results.pred[0, -1].item()]}"  # Extract class name
+
+# Define the description and interface
 description = """
-Welcome to the Gradio interface! This simple app detects the mouse feet observing from below.
-You can try it out by uploading an image!
+Welcome to the Gradio interface! This simple app detects mouse feet observing from below.
+Upload an image to see the prediction results!
 """
 
-# Define the interface with inputs and outputs
+# Create the Gradio interface
 demo = gr.Interface(
-    fn=greet, 
+    fn=predict, 
     inputs=gr.Image(label="Upload an Image"), 
-    outputs=gr.Textbox(label="Greeting"), 
+    outputs=gr.Textbox(label="Prediction Result"), 
     live=True,
-    title="Greeting App",
+    title="Mouse Feet Detection",
     description=description
 )