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it20187682/app.py

+import os
+import shutil
+from flask import Flask, request, jsonify
+from const import OUTPUT_DIR
+from detect  import run
+from predict import start_predict
+from flask_cors import CORS 
+
+#lahiri
+from image_service import predict_disease
+
+APP_NAME = 'DME_IDENTIFIER'
+
+app = Flask(APP_NAME)
+CORS(app)
+
+def clean_dir(save_dir):
+    try:
+        shutil.rmtree(save_dir)
+        print(f"Directory '{save_dir}' removed successfully.")
+    except OSError as e:
+        print(f"Error: {e}")
+
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    # Check if an image was uploaded
+    if 'image' not in request.files:
+        return jsonify({'error': 'No image uploaded'})
+
+    file = request.files['image']
+
+    # Save the uploaded image to a designated directory
+    if file:
+        
+        file = request.files['image']
+        image = file.read()
+        save_dir = 'temp'
+        # clean_dir(save_dir)
+
+        if not os.path.exists(save_dir):
+            os.makedirs(save_dir)
+
+    image_path = os.path.join(save_dir, "image.png")  # Specify the path where you want to save the image
+    with open(image_path, 'wb') as image_file:
+        image_file.write(image)
+
+
+    # clean output dir befor crop
+    clean_dir(OUTPUT_DIR)
+
+    run()
+    predict_class , predicted_percentage = start_predict()
+
+    # clean output dir after crop
+    # clean_dir(save_dir)
+    # clean_dir(OUTPUT_DIR)
+
+
+    prediction = predict_class
+    prediction_label = 'DME' if prediction == 0 else 'NORMAL'
+
+    # my return jsonify({'predicted_class': prediction , "prediction_label" : prediction_label , "predicted_percentage" : predicted_percentage})
+    return jsonify({'time': prediction , "top" : prediction_label , "confidence" : predicted_percentage, 'image' : {'height': 1584, 'width': 2376},})
+
+@app.route('/predict/glaucoma', methods=['POST'])
+# @cross_origin(origin='*',headers=['Content- Type','Authorization'])
+def predictG():
+    print("hihi")
+    # Check if an image was uploaded
+    if 'image' not in request.files:
+        print("hoooo1")
+        return jsonify({'error': 'No image uploaded'})
+
+    # Load and preprocess the image received from the Flask API
+    file = request.files['image']
+    image = file.read()
+    save_dir = 'tempG'
+        # clean_dir(save_dir)
+
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+
+    # Save the image as "image.png"
+    image_path = os.path.join(save_dir, "image.png")  # Specify the path where you want to save the image
+    with open(image_path, 'wb') as image_file:
+        image_file.write(image)
+    prediction = predict_disease(image)
+
+    # return jsonify({'predicted_class': prediction})
+    return (prediction)
+
+
+
+if APP_NAME == 'DME_IDENTIFIER':
+    # app.run(debug=False)
+    app.run(host='0.0.0.0', port=8081, debug=True)