Create app.py

app.py

@@ -0,0 +1,41 @@
+import cv2
+import requests
+import numpy as np
+import gradio as gr
+
+# Model URL
+image_model_url = 'https://teachablemachine.withgoogle.com/models/ZPfAhDYCh/model.json'
+
+# Load the model
+net = cv2.dnn.readNetFromTensorflow(requests.get(image_model_url).content)
+
+# Function to classify the image
+def classify_image(frame):
+    # Flip the frame horizontally for better classification
+    frame = cv2.flip(frame, 1)
+
+    # Prepare the frame for classification
+    blob = cv2.dnn.blobFromImage(frame, size=(224, 224), swapRB=True, crop=False)
+    net.setInput(blob)
+    
+    # Get the predictions
+    predictions = net.forward()
+
+    # Find the index of the class with the highest confidence
+    class_index = np.argmax(predictions)
+    
+    # Print the predicted label
+    if class_index == 0:
+        label = 'Class 0'
+    elif class_index == 1:
+        label = 'Class 1'
+    else:
+        label = 'Unknown'
+    
+    return label
+
+# Create Gradio interface
+iface = gr.Interface(fn=classify_image, inputs="webcam", outputs="text")
+
+# Launch the interface
+iface.launch()