--- a
+++ b/app.py
@@ -0,0 +1,86 @@
+import os
+from flask import Flask, flash, request, redirect, url_for, render_template, send_from_directory
+from werkzeug.utils import secure_filename
+from tensorflow.keras.models import load_model
+import numpy as np
+#from keras.preprocessing import image
+from tensorflow.keras.preprocessing import image
+
+
+
+model=load_model("Esophageal_model.h5")
+
+UPLOAD_FOLDER = 'static/img'
+if not os.path.exists(UPLOAD_FOLDER):
+    os.makedirs(UPLOAD_FOLDER)
+
+
+ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'}
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+def allowed_file(filename):
+    return '.' in filename and \
+           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+
+
+# Home Page
+@app.route('/')
+def index():
+    return render_template('home.html')
+
+
+@app.route('/prediction', methods=['GET', 'POST'])
+def upload_file():
+    if request.method == 'POST':
+        import uuid
+        u = uuid.uuid4()
+        # check if the post request has the file part
+        if 'file' not in request.files:
+            flash('No file part')
+            return redirect(request.url)
+        file = request.files['file']
+        # if user does not select file, browser also
+        # submit an empty part without filename
+        if file.filename == '':
+            flash('No selected file')
+            return redirect(request.url)
+        if file and allowed_file(file.filename):
+            filename = secure_filename(file.filename)
+            filename="temp"+u.hex+".jpg"
+            fullname=os.path.join(UPLOAD_FOLDER, "temp"+u.hex+".jpg")
+            file.save(fullname)
+            test_image = image.load_img('static/img/'+filename, target_size = (224,224))
+            test_image = image.img_to_array(test_image)
+            test_image = np.expand_dims(test_image, axis = 0)
+            test_image = test_image.astype('float') / 255
+            result = model.predict(test_image)
+            pred_prob = result.item()
+            print(result)
+            if result[0]>0.5:
+                label = 'NON-Esophageal'
+                accuracy = round(pred_prob * 100, 2)
+            else:
+                pred_1 = round((1 - pred_prob) * 100, 2)
+                if pred_1 < 75:
+                    label = 'Early Detection of Esophageal'
+                    accuracy = round((1 - pred_prob) * 100, 2)
+                else:
+                    label = 'Esophageal'
+                    accuracy = round((1 - pred_prob) * 100, 2)
+                    
+         
+    return render_template('index.html', label=label, image_file_name=filename, accuracy=accuracy)
+
+
+@app.route('/upload/<filename>')
+def send_file(filename):
+    return send_from_directory(UPLOAD_FOLDER, filename)
+
+
+if __name__ == '__main__':
+    app.run(debug=False)
+    
+    
+