Upload 3 files

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load the model
+model = tf.keras.models.load_model('dogcat_model_bak.h5')
+
+def classify_image(input_image):
+    # Load and preprocess the image
+    img1 = image.load_img(input_image.name, target_size=(64, 64))
+    img2 = image.load_img(input_image.name)
+    img = image.img_to_array(img1)
+    img = img / 255.0
+    img = np.expand_dims(img, axis=0)
+
+    # Make prediction
+    prediction = model.predict(img)
+
+    # 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.inputs.Image()
+outputs = gr.outputs.Image()
+interface = gr.Interface(classify_image, inputs, outputs, capture_session=True)
+
+# Launch the Gradio app
+interface.launch()

dogcat_model_bak.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2ca81c1f1706fec46cac5b436131eb3ec00795e03f5fb1b393b03b8d77581bd
+size 9793928

requirements.txt

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+gradio
+tensorflow
+numpy
+pandas
+matplotlib