building the interface

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+model_path = 'model'
+model = tf.saved_model.load(model_path)
+
+labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']
+
+def predict_image(image):
+  image_resized = image.resize((224, 224))
+  image_array = np.array(image_resized).astype(np.float32) / 255.0
+  image_array = np.expand_dims(image_array, axis=0)
+
+  predictions = model.signatures['serving_default'](tf.convert_to_tensor(image_array, dtype=tf.float32))['output_0']
+    
+  # Highest prediction
+  top_index = np.argmax(predictions.numpy(), axis=1)[0]
+  top_label = labels[top_index]
+  top_probability = predictions.numpy()[0][top_index]
+
+  return {top_label:top_probability}  
+
+# Example images
+example_images = [
+    ["exp_eye_images/0_right_h.png"],
+    ["exp_eye_images/03fd50da928d_dr"],
+    ["exp_eye_images/108_right_h"],
+    ["exp_eye_images/1062_right_c"],
+    ["exp_eye_images/1084_right_c"],
+    ["exp_eye_images/image_1002_g"]
+]
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=1, label="Prediction"),
+    examples=example_images,
+    title="Eye Diseases Classifier",
+    description="Upload an image of an eye fundus, and the model will predict it.\n\n**Disclaimer:** This model is intended as a form of learning process in the field of health-related machine learning and was trained with a limited amount and variety of data with a total of about 4000 data, so the prediction results may not always be correct. There is still a lot of room for improvisation on this model in the future.",
+    allow_flagging="never"
+)
+
+interface.launch(share=True)