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app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load your custom regression model
+model_path = "kia_mnist_keras_model.weights.h5"  
+model = tf.keras.Sequential([
+    tf.keras.layers.Flatten(input_shape=[28, 28]),
+    tf.keras.layers.Rescaling(1./255.),
+    tf.keras.layers.Dense(300, activation="relu"),
+    tf.keras.layers.Dense(100, activation="relu"),
+    tf.keras.layers.Dense(10, activation="softmax")
+])
+model.load_weights(model_path)
+
+labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
+
+# Define regression function
+def predict_regression(image):
+    # Preprocess image
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((28, 28)).convert('L') #resize the image to 28x28 and converts it to gray scale
+    image = np.array(image)
+    print(image.shape)
+    # Predict
+    prediction = model.predict(image[None, ...])  # Assuming single regression value
+    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
+    return confidences
+
+# Create Gradio interface
+input_image = gr.Image()
+output_text = gr.Textbox(label="Predicted Value")
+interface = gr.Interface(fn=predict_regression, 
+                         inputs=input_image, 
+                         outputs=gr.Label(),
+                         examples=["images/0.jpeg", "images/1.jpeg", "images/2.jpeg", "images/5.jpeg"],   
+                         description="A custom regression model for image regression using .h5 file.")
+interface.launch()

kia_mnist_keras_model.weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:93d2db3e726864243d6c83cbd9039aaf671b564ec141f51c2bff674ff67faae6
+size 3220912

requirements.txt

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+tensorflow