Update app.py

app.py

@@ -1,41 +1,7 @@
 import gradio as gr
-import tensorflow as tf
-from tensorflow.keras.preprocessing import image
-import matplotlib.pyplot as plt
-import numpy as np
-from PIL import Image as PIL_Image
-# Load the model
-model = tf.keras.models.load_model('dogcat_model_bak.h5')
 
-def classify_image(input_image):
-    pil_img = PIL_Image.fromarray(input_image, 'RGB')
-    # Preprocess the image
-    img1 = pil_img.resize((64, 64))
-    img2 = pil_img
-    img = image.img_to_array(img1)
-    img = img / 255.0
-    img = np.expand_dims(img, axis=0)
-    # Make prediction
-    prediction = model.predict(img)
+def image_classifier(inp):
+    return {'cat': 0.3, 'dog': 0.7}
 
-    # Display prediction result on the image
-    if prediction[0][0] > 0.5:
-        value = 'Dog: %1.2f' % prediction[0][0]
-        plt.text(20, 62, value, color='red', fontsize=18, bbox=dict(facecolor='white', alpha=0.8))
-    else:
-        value = 'Cat: %1.2f' % (1.0 - prediction[0][0])
-        plt.text(20, 62, value, color='red', fontsize=18, bbox=dict(facecolor='white', alpha=0.8))
-
-    plt.imshow(img2)
-    plt.axis('off')  # Hide axis for better visualization
-    plt.show()
-
-    return img2  # Return the image with prediction annotations
-
-# Interface creation using Gradio
-inputs = gr.Image()
-interface = gr.Interface(fn = classify_image, inputs=gr.Image(),outputs=classify_image(inputs))
-    # Load and pre, capture_session=True)
-
-# Launch the Gradio app
-interface.launch()
+demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
+demo.launch()