--- a
+++ b/leukemia.py
@@ -0,0 +1,93 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar  2 18:23:11 2022
+
+@author: Rushi
+"""
+
+#api libraries
+from flask import Flask, request, render_template
+
+#predicting libraries
+from tensorflow.keras import optimizers, preprocessing
+import tensorflow_addons as tfa
+import tensorflow as tf
+from keras.models import model_from_json
+import numpy as np
+import os
+
+
+app = Flask(__name__)
+
+def get_model():
+    global model
+    DATA_PATH = "D:/RK/Marwadi University/Sem-8/Project/C-NMC_Leukemia/"
+    os.chdir(DATA_PATH)
+
+    json_file = open("model.json","r")
+    loaded_model_json = json_file.read()
+    json_file.close()
+    model = model_from_json(loaded_model_json)
+    model.load_weights("model.h5")
+    LEARNING_RATE = 3e-5
+    optimizer = optimizers.Adam(lr=LEARNING_RATE)
+    model.compile(optimizer=optimizer, 
+                  loss='binary_crossentropy', 
+                  metrics=['accuracy',tfa.metrics.F1Score(num_classes=2, average='weighted')])
+
+    
+def load_image(img_path):
+    
+    img = tf.keras.preprocessing.image.load_img(img_path, target_size=(200, 200))
+    img_tensor = tf.keras.preprocessing.image.img_to_array(img)                    # (height, width, channels)
+    img_tensor = np.expand_dims(img_tensor, axis=0)         # (1, height, width, channels), add a dimension because the model expects this shape: (batch_size, height, width, channels)
+    img_tensor /= 255
+    
+    return img_tensor
+
+def predictions(img_path):
+    
+    new_image = load_image(img_path)
+    
+    preds = model.predict(new_image)
+    
+    prediction = np.argmax(preds)
+    pct = "{:.2f}".format(np.max(preds)*100)
+    if prediction == 1:
+        return ["The Prediction of the sample is: ALL", pct]
+    else:
+        return ["The Prediction of the sample is: HEM", pct]
+
+@app.route("/", methods=['GET','POST'])
+
+def home():
+    
+    return render_template('home.html')
+
+@app.route("/predict", methods = ['GET','POST'])
+
+def predict():
+    
+    if request.method == 'POST':
+        
+        get_model()
+        
+        file = request.files['file']
+        
+        filename = file.filename
+        
+        file_path = os.path.join('static', filename)
+        
+        file.save(file_path)
+        
+        print(file_path)
+        product = predictions(file_path)
+        str1 = str(product[1])
+        value = ("Prediction Confidence Percentage is: " + str1 + "%")
+        print(value)
+        
+        return render_template('predict.html', user_image = file_path, product = product[0], value = value) 
+
+if __name__ == "__main__":
+    app.run()
+    
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