Update app.py

app.py

@@ -1,41 +1 @@
-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()
+pip install opencv-python