--- a
+++ b/app.py
@@ -0,0 +1,168 @@
+"""
+=============================================================================================
+Project     : Chest X-Ray Pathology Detection and Localization using Deep Learning
+Author Name : Rammuni Ravidu Suien Silva
+UoW No      : 16267097
+IIT No      : 2016134
+Module      : Final Year Project 20/21
+Supervisor  : Mr Pumudu Fernando
+
+Prototype   : Web Interface - BackEnd [Draft: .v01]
+University of Westminster, UK || IIT Sri Lanka
+=============================================================================================
+"""
+import json
+import os
+from datetime import datetime
+
+import numpy as np
+# Flask Imports
+from flask import Flask, request, render_template
+from flask import send_file
+from flask_jsglue import JSGlue
+# Tensorflow Keras imports
+from tensorflow.keras.models import load_model
+# For secure src links
+from werkzeug.utils import secure_filename
+
+# System Library import
+from lab_cxr_scripts.lab_cxr import CXRPrediction, CXRLocalization
+
+# Model 0, 2 :- xray_labels_set[0] || Model 1 :- xray_labels_set[1]
+xray_labels_set = [["Enlarged Cardiomediastinum", "Cardiomegaly", "Lung Lesion", "Lung Opacity", "Edema",
+                    "Consolidation", "Pneumonia", "Atelectasis", "Pneumothorax", "Pleural Effusion",
+                    "Pleural Other", "Fracture", "Support Devices"],
+                   ["Nodule", "Cardiomegaly", "Emphysema", "Fibrosis", "Edema", "Consolidation", "Pneumonia",
+                    "Atelectasis", "Pneumothorax", "Pleural Effusion", "Mass", "Infiltration", "Hernia",
+                    "Plueral Thickening"]]
+
+# Labels for classification tasks
+xray_labels = xray_labels_set[0]
+# Dependency pip install pyopenssl
+# Flask Configs
+app = Flask(__name__)
+app.config['MAX_CONTENT_LENGTH'] = 20 * 1024 * 1024  # Request data limited to 20MB
+jsglue = JSGlue(app)
+
+# TODO: USER GUIDE
+
+# model load
+models = [[
+    load_model('models/MIMIC/PAR-64-MODEL-MIMIC-FINAL-2.h5',
+               custom_objects={'weighted_loss': CXRPrediction.get_weighted_loss(1, 1)}),
+    load_model('models/MIMIC/PAR-128-MODEL-MIMIC-FINAL-2.h5',
+               custom_objects={'weighted_loss': CXRPrediction.get_weighted_loss(1, 1)})
+], [
+    load_model('models/NIH/PAR-64-MODEL-FINAL-NIH-2.h5',
+               custom_objects={'weighted_loss': CXRPrediction.get_weighted_loss(1, 1)}),
+    load_model('models/NIH/PAR-128-MODEL-FINAL-NIH-2.h5',
+               custom_objects={'weighted_loss': CXRPrediction.get_weighted_loss(1, 1)})
+]]
+model = models[0]
+cur_cxr_hash = 'none'
+
+"""
+==================================================================================================================
+                                            Web request functions
+==================================================================================================================
+"""
+
+
+# Web page startup
+@app.route('/')
+def start_web():
+    return render_template("index.html")
+
+
+# CXR Image upload API
+@app.route('/predict/<int:model_id>', methods=['GET', 'POST'])
+def upload(model_id):
+    if request.method == 'POST':
+        print("Model ID", model_id)
+
+        # Selecting Model and labels set
+        global model, xray_labels
+        model = models[model_id % len(models)]
+        xray_labels = xray_labels_set[model_id % len(xray_labels_set)]
+
+        global cur_cxr_hash
+        preds = []
+        file_count = len(request.files)
+        if file_count > 8:
+            return
+        for file_num in range(file_count):
+            # Getting image file from post request through the Web
+            cxr_img_file = request.files['file_' + str(file_num)]
+            # Generating Hash of the image file
+            hashed_filename = CXRPrediction.hash_cxr(cxr_img_file)
+            print(hashed_filename)
+            cur_cxr_hash = hashed_filename
+
+            # Saving the CXR image to uploads
+            cxr_img_path = os.path.dirname(__file__)
+            file_path = os.path.join(
+                cxr_img_path, 'uploads', secure_filename(hashed_filename))
+            cxr_img_file.save(file_path)
+
+            # Detection results calculation
+            preds.append(np.array(CXRPrediction.model_predict(file_path, model)[0]).tolist())
+
+        # Final results calculation considering the results of all the uploaded images
+        final_preds = np.round(np.multiply(np.mean(preds, axis=0), 100), 2)
+        final_preds_max = np.round(np.multiply(np.max(preds, axis=0), 100), 2)
+        final_preds_min = np.round(np.multiply(np.min(preds, axis=0), 100), 2)
+
+        print(final_preds)
+        # Creating the detection results dictionary/ JSON
+        predictions_dict = {}
+        for i in range(0, len(xray_labels)):
+            det_rate_str = str(final_preds[i]) + "% (" + str(final_preds_max[i]) + "% - " + str(
+                final_preds_min[i]) + "%)"
+            predictions_dict[xray_labels[i]] = det_rate_str
+
+        # Creating detection result JSON to be sent
+        json_predictions = json.dumps(predictions_dict, indent=4)
+
+        result = json_predictions
+        return result
+    return None
+
+
+@app.route('/localize')
+def localization():  # Localization API
+    global cur_cxr_hash
+    start = datetime.now()
+    filepath = 'localizations/' + cur_cxr_hash.split('.')[0]
+
+    if os.path.exists(filepath):
+        file_count = len([name for name in os.listdir(filepath) if os.path.isfile(os.path.join(filepath, name))])
+        if not file_count == len(xray_labels):
+            # If the localized img is already there no need to re-process
+            CXRLocalization.create_cxr_localization_heatmap(cur_cxr_hash, model[len(model) - 1], xray_labels)
+    else:
+        # Calling Localization Function
+        CXRLocalization.create_cxr_localization_heatmap(cur_cxr_hash, model[len(model) - 1], xray_labels)
+
+    print(datetime.now() - start)
+    return str(len(xray_labels))  # Returning the localized labels
+
+
+# Function for sending the localized CXR image
+@app.route('/get_cxr_detect_img/<int:pathology_id>')
+def get_cxr_detect_img(pathology_id):
+    print(pathology_id)
+    global cur_cxr_hash
+    localized_image_name = xray_labels[pathology_id] + '-localizedHeatmap-' + cur_cxr_hash
+    filepath = 'localizations/' + cur_cxr_hash.split('.')[0] + '/'
+    return send_file(filepath + localized_image_name, mimetype='image/jpg')
+
+
+# Function for getting symptoms
+@app.route('/get_symptoms')
+def get_symptoms():
+    return send_file('static/files/Symptoms.json', mimetype='application/json')
+
+
+print("Server Running...")
+if __name__ == '__main__':
+    app.run(debug=True)  # Debugging