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+# Use a base image with Python pre-installed
+FROM python:3.8-slim
+
+# Set the working directory
+WORKDIR /app
+
+# Copy the application code into the container
+COPY . /app
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose the port that the app will run on
+EXPOSE 8080
+
+# Start the Flask app using gunicorn (production-ready)
+CMD ["gunicorn", "-b", "0.0.0.0:8080", "app:app"]

app.py

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+from flask import Flask, request, jsonify 
+from transformers import AutoModelForImageClassification, AutoProcessor
+from PIL import Image
+import io
+import fitz  # PyMuPDF
+from flask_cors import CORS
+import os
+
+app = Flask(__name__)
+CORS(app)
+
+model_name = "AsmaaElnagger/Diabetic_RetinoPathy_detection"
+model = AutoModelForImageClassification.from_pretrained(model_name)
+processor = AutoProcessor.from_pretrained(model_name)
+
+def pdf_to_images_pymupdf(pdf_data):
+    try:
+        pdf_document = fitz.open(stream=pdf_data, filetype="pdf")
+        images = []
+        for page_num in range(pdf_document.page_count):
+            page = pdf_document.load_page(page_num)
+            pix = page.get_pixmap()
+            img_data = pix.tobytes("jpeg")  # Or "png"
+            images.append(img_data)
+        return images
+    except Exception as e:
+        print(f"Error converting PDF: {e}")
+        return None
+
+@app.route('/classify', methods=['POST'])
+def classify_file():
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file provided'}), 400
+
+    uploaded_file = request.files['file']
+    file_type = uploaded_file.filename.rsplit('.', 1)[1].lower()
+
+    try:
+        if file_type in ['jpg', 'jpeg', 'png', 'gif']:
+            # Handle image upload
+            img_data = uploaded_file.read()
+            image = Image.open(io.BytesIO(img_data)).convert("RGB")
+            inputs = processor(images=image, return_tensors="pt")
+            outputs = model(**inputs)
+            logits = outputs.logits
+            predicted_class_idx = logits.argmax(-1).item()
+            result = model.config.id2label[predicted_class_idx]
+            return jsonify({'result': result})
+
+        elif file_type == 'pdf':
+            # Handle PDF upload
+            pdf_data = uploaded_file.read()
+            images = pdf_to_images_pymupdf(pdf_data)
+
+            if images:
+                # Process the first image in the pdf, you may need to loop through all images.
+                image = Image.open(io.BytesIO(images[0])).convert("RGB")
+                inputs = processor(images=image, return_tensors="pt")
+                outputs = model(**inputs)
+                logits = outputs.logits
+                predicted_class_idx = logits.argmax(-1).item()
+                result = model.config.id2label[predicted_class_idx]
+                return jsonify({'result': result})
+            else:
+                return jsonify({'error': 'PDF conversion failed.'}), 500
+
+        else:
+            return jsonify({'error': 'Unsupported file type'}), 400
+
+    except Exception as e:
+        return jsonify({'error': f'An error occurred: {e}'}), 500
+
+if __name__ == '__main__':
+    # For Hugging Face Spaces, we need to bind to 0.0.0.0 and use the default port 8080
+    app.run(debug=False, host="0.0.0.0", port=int(os.environ.get("PORT", 8080)))