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### Onnx model
Uploaded `onnx` version of coffee classification model along with python file`onnx_server.py` that runs it in flask

coffee_model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:692fcb1f895602df35597f2e935c4c5cdf05bb2c15a62c08e69b0e1c881cfb4b
+size 29999661

onnx_server.py

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+from flask import Flask, request, jsonify, render_template
+from flask_cors import CORS  # Import CORS
+import onnxruntime as rt
+import numpy as np
+import cv2
+import io
+from PIL import Image
+
+app = Flask(__name__)
+
+# Enable CORS for all routes
+CORS(app)
+
+# Load ONNX model
+MODEL_PATH = "coffee_model.onnx"  # Ensure the correct path
+session = rt.InferenceSession(MODEL_PATH)
+
+@app.route("/", methods=["GET"])
+def home():
+    context = jsonify({"message": "ONNX Model API is running!"})
+    return render_template("home.html")
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    try:
+        if 'file' not in request.files:
+            return jsonify({"error": "No file uploaded"}), 400
+
+        file = request.files['file']
+        image = Image.open(io.BytesIO(file.read()))
+        image = np.array(image)
+        image = cv2.resize(image, (224, 224))  # Resize to model input size
+        image = image.astype(np.float32) / 255.0  # Normalize
+        image = np.expand_dims(image, axis=0)  # Add batch dimension
+
+        # Get input name from the model
+        input_name = session.get_inputs()[0].name
+
+        # Run model inference
+        result = session.run(None, {input_name: image})
+        prediction = np.argmax(result[0])
+        confidence = np.max(result[0])
+
+        # Map predictions to class names
+        classes = ['Health leaves', 'leaf rust', 'phoma']
+        predicted_class = classes[prediction]
+
+        return jsonify({"class": predicted_class, "confidence": float(confidence)})
+
+    except Exception as e:
+        print(f"Error: {e}")  # Log error for debugging
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000, debug=True)