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+FROM python:3.10-slim
+
+WORKDIR /code
+
+COPY requirements.txt .
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

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
+
+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")
+            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']:
+            image = Image.open(uploaded_file).convert("RGB")
+        elif file_type == 'pdf':
+            pdf_data = uploaded_file.read()
+            images = pdf_to_images_pymupdf(pdf_data)
+            if not images:
+                return jsonify({'error': 'Failed to convert PDF.'}), 500
+            image = Image.open(io.BytesIO(images[0])).convert("RGB")
+        else:
+            return jsonify({'error': 'Unsupported file type'}), 400
+
+        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})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host="0.0.0.0", port=7860)