Deployment ready

Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["gunicorn","-b","0.0.0.0:7860","app:app"]

app.py

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+from flask import Flask, request, jsonify
+from keras.models import load_model
+from PIL import Image, ImageOps
+import numpy as np
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app,origin=['*','http://localhost:3000'],allow_headers=['Content-Type','Authorization','Access-Control-Allow-Credentials','Access-Control-Allow-Origin','Access-Control-Allow-Headers','x-xsrf-token','Access-Control-Allow-Methods','Access-Control-Allow-Headers','Access-Control-Allow-Headers','Access-Control-Allow-Origin','Access-Control-Allow-Methods','Authorization','X-Requested-With','Access-Control-Request-Headers','Access-Control-Request-Method'])
+port = 4000
+
+# Load the model
+model = load_model("keras_model.h5", compile=False)
+
+# Load the labels
+class_names = [line.strip() for line in open("labels.txt", "r").readlines()]
+
+# Confidence threshold for predictions
+confidence_threshold = 0.7
+
+@app.route('/')
+def hello():
+    return f"Server is running on port {port}"
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file provided'}), 400
+
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({'error': 'No selected file'}), 400
+
+    if file:
+        # Process the image file
+        image = Image.open(file.stream).convert("RGB")
+        size = (224, 224)
+        image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
+
+        # Convert image to numpy array and normalize
+        image_array = np.asarray(image)
+        normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+
+        # Prepare the image for prediction
+        data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+        data[0] = normalized_image_array
+
+        # Predict
+        prediction = model.predict(data)
+        max_confidence = np.max(prediction)
+
+        if max_confidence >= confidence_threshold:
+            index = np.argmax(prediction)
+            class_name = class_names[index]
+            confidence_score = float(prediction[0][index])
+        else:
+            # Classify as "other" if confidence is below threshold
+            class_name = "Other"
+            confidence_score = float(max_confidence)
+
+        # Return the result
+        response = jsonify({'class': class_name, 'confidence_score': confidence_score})
+    return response
+if __name__ == '__main__':
+    app.run(debug=True, port=port)

keras_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:afe614bfd48d792b6b0f3654406cbada6cd13f0a2ad6a98c6655b5636a50ee15
+size 2453432

labels.txt

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+0 Cataract
+1 Normal

requirements.txt

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+absl-py==2.0.0
+astunparse==1.6.3
+blinker==1.7.0
+cachetools==5.3.2
+certifi==2023.11.17
+charset-normalizer==3.3.2
+click==8.1.7
+colorama==0.4.6
+dm-tree==0.1.8
+Flask==3.0.0
+Flask-Cors==4.0.0
+flatbuffers==23.5.26
+gast==0.5.4
+google-auth==2.25.2
+google-auth-oauthlib==1.1.0
+google-pasta==0.2.0
+grpcio==1.60.0
+gunicorn==21.2.0
+h5py==3.10.0
+idna==3.6
+itsdangerous==2.1.2
+Jinja2==3.1.2
+keras==2.15.0
+libclang==16.0.6
+Markdown==3.5.1
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+mdurl==0.1.2
+ml-dtypes==0.2.0
+namex==0.0.7
+numpy==1.26.2
+oauthlib==3.2.2
+opt-einsum==3.3.0
+packaging==23.2
+Pillow==10.1.0
+protobuf==4.23.4
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+Pygments==2.17.2
+requests==2.31.0
+requests-oauthlib==1.3.1
+rich==13.7.0
+rsa==4.9
+six==1.16.0
+tensorboard==2.15.1
+tensorboard-data-server==0.7.2
+tensorflow==2.15.0
+tensorflow-estimator==2.15.0
+tensorflow-io-gcs-filesystem==0.31.0
+termcolor==2.4.0
+typing_extensions==4.9.0
+urllib3==2.1.0
+Werkzeug==3.0.1
+wrapt==1.14.1