Upload 8 files

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Mild.png filter=lfs diff=lfs merge=lfs -text
+Moderate.png filter=lfs diff=lfs merge=lfs -text
+No_DR.png filter=lfs diff=lfs merge=lfs -text
+Proliferate_DR[[:space:]](1).png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,69 @@
+from keras.models import load_model
+from keras.preprocessing import image
+import numpy as np
+import os
+import gradio as gr
+loaded_model = load_model('diabetic_retinopathy_model.h5')
+import gradio as gr
+import numpy as np
+from tensorflow.keras.preprocessing import image
+
+# Class mapping
+class_mapping = {
+    0: 'No DR',
+    1: 'Mild',
+    2: 'Moderate',
+    3: 'Severe',
+    4: 'Proliferative DR'
+}
+
+# URL of the fixed example image to display
+example_image_url = "1.jpg"  # Replace with the actual URL
+
+def predict_diabetic_retinopathy(test_image, loaded_model, height=512, width=512):
+    # Always return the example image
+    try:
+        if test_image is None:
+            return "No image uploaded. Please upload an image.", example_image_url
+
+        # Ensure the image is in the correct format
+        img = image.img_to_array(test_image)
+        
+        # Resize the image while maintaining the aspect ratio
+        img = np.array(image.smart_resize(img, (height, width)))
+        
+        img_array = np.expand_dims(img, axis=0)
+        img_array /= 255.0  # Normalize the image array
+        
+        # Make predictions
+        predictions = loaded_model.predict(img_array)
+
+        # Convert predictions to the corresponding class
+        predicted_class = np.argmax(predictions)
+
+        # Return the predicted class and the example image URL
+        return f"**Predicted Diabetic Retinopathy Stage:** {class_mapping[predicted_class]}", example_image_url
+    
+    except Exception as e:
+        return f"An error occurred: {str(e)}", example_image_url
+
+# Create the Gradio interface with fixed example images
+example_images = [
+    "No_DR.png",
+    "Mild.png",
+    "Moderate.png",
+    "Proliferate_DR.png"
+]
+
+iface = gr.Interface(
+    fn=lambda img: predict_diabetic_retinopathy(img, loaded_model),
+    inputs=gr.Image(type="numpy", label="Upload Retina Image"),
+    outputs=[gr.Markdown(label="Prediction Result"), gr.Image(value=example_image_url, label="Example Image")],
+    title="Diabetic Retinopathy Prediction",
+    description="Upload an image of the retina to predict the stage of diabetic retinopathy.",
+    theme="default",
+    examples=example_images
+)
+
+# Launch the interface
+iface.launch()

diabetic_retinopathy_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d11483d5063500d6c41919d43c65044c038b3fbb3b2cd05abb2dd664d2cd7bf
+size 333766768

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+tensorflow==2.17.0 
+numpy==1.24.3
+opencv-python