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.gitattributes

@@ -33,3 +33,8 @@ 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
+sample/1.jpeg filter=lfs diff=lfs merge=lfs -text
+sample/2.jpeg filter=lfs diff=lfs merge=lfs -text
+sample/3.jpeg filter=lfs diff=lfs merge=lfs -text
+sample/4.jpeg filter=lfs diff=lfs merge=lfs -text
+sample/5.jpeg filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,22 @@
+FROM python:3.9-slim
+
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy requirements first for better caching
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application
+COPY . .
+
+# Expose the port the app runs on
+EXPOSE 8080
+
+# Command to run the application
+CMD ["python", "app.py"]

app.py

@@ -0,0 +1,51 @@
+import onnxruntime
+import numpy as np
+import gradio as gr
+from PIL import Image
+
+labels = {
+    0: "No DR",
+    1: "Mild",
+    2: "Moderate",
+    3: "Severe",
+    4: "Proliferative DR",
+}
+session = onnxruntime.InferenceSession('model.onnx')
+
+def transform_image(image):
+    image = image.resize((224, 224))
+    img_array = np.array(image, dtype=np.float32) / 255.0
+    mean = np.array([0.5353, 0.3628, 0.2486], dtype=np.float32)
+    std = np.array([0.2126, 0.1586, 0.1401], dtype=np.float32)
+    img_array = (img_array - mean) / std
+    img_array = np.transpose(img_array, (2, 0, 1))
+    return np.expand_dims(img_array, axis=0).astype(np.float32) 
+def predict(input_img):
+    """Predict DR grade from input image using ONNX model"""
+    input_tensor = transform_image(input_img)
+    input_name = session.get_inputs()[0].name
+    output_name = session.get_outputs()[0].name
+    prediction = session.run([output_name], {input_name: input_tensor})[0][0]
+    exp_preds = np.exp(prediction - np.max(prediction))
+    probabilities = exp_preds / exp_preds.sum()    
+    confidences = {labels[i]: float(probabilities[i]) for i in labels}
+    #filtered_confidences = {key: confidences[key] for key in ["No DR", "Severe"]}
+    #return filtered_confidences
+    return confidences
+
+dr_app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(),
+    title="Diabetic Retinopathy Detection",
+    description="",
+    examples=[
+        "sample/1.jpeg",
+        "sample/2.jpeg",
+        "sample/3.jpeg",
+        "sample/4.jpeg",
+    ],
+    analytics_enabled=False,
+)
+if __name__ == "__main__":
+    dr_app.launch(server_name="0.0.0.0", server_port=8080)

model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2df12f77e5a9240ad729d61a4e63c0304cb232bc99f47c4a166c2330efa0780
+size 28227960

requirements.txt

@@ -0,0 +1,4 @@
+onnxruntime 
+numpy 
+gradio 
+Pillow