Update main.py

main.py

@@ -1,44 +1,103 @@
-from flask import Flask, request, jsonify, render_template
-from flask_cors import CORS
+from flask import Flask, flash, request, redirect, render_template
+import os
+import cv2
+import imutils
 import numpy as np
-import pandas as pd
-from sklearn.linear_model import LogisticRegression
+from tensorflow.keras.models import load_model
+from werkzeug.utils import secure_filename
+
+# Load the Brain Tumor CNN Model
+braintumor_model = load_model('models/braintumor.h5')
+
+# Configuring Flask
+UPLOAD_FOLDER = 'static/uploads'
+ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
 
 app = Flask(__name__)
-app.static_folder = 'static'
-app.static_url_path = '/static'
-
-app.secret_key = "flask-nielit-2023"
-
-CORS(app)
-
-@app.route('/')
-def iris():
-  return render_template("index.html")
-
-@app.route('/irisf', methods=["POST"])
-def page():
-  swidth=eval(request.form.get("swidth"))
-  sheight=eval(request.form.get("sheight"))
-  pwidth=eval(request.form.get("pwidth"))
-  pheight=eval(request.form.get("pheight"))
-  
-  url="https://raw.githubusercontent.com/lovnishverma/datasets/main/iris.csv"
-  
-  data=pd.read_csv(url, header=None)
-  flower=data.values
-  
-  #Split
-  x=flower[:,:4]
-  y=flower[:,-1]
-  
-  model=LogisticRegression()
-  model.fit(x,y)
-  
-  arr=model.predict([[swidth,sheight,pwidth,pheight]])
-
-  return render_template("index.html", data=str(arr[0]))
+app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+app.secret_key = "secret key"
+
+def allowed_file(filename):
+    """Check if the file is a valid image format"""
+    return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
+
+def preprocess_imgs(set_name, img_size):
+    """
+    Preprocess the image by resizing and applying VGG16 preprocessing
+    """
+    set_new = []
+    for img in set_name:
+        img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC)
+        set_new.append(img)
+    return np.array(set_new)
+
+def crop_imgs(set_name, add_pixels_value=0):
+    """
+    Crop the region of interest (ROI) in the image (brain tumor detection)
+    """
+    set_new = []
+    for img in set_name:
+        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+        gray = cv2.GaussianBlur(gray, (5, 5), 0)
+
+        # Threshold the image and find contours to crop the image
+        thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
+        thresh = cv2.erode(thresh, None, iterations=2)
+        thresh = cv2.dilate(thresh, None, iterations=2)
+
+        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        cnts = imutils.grab_contours(cnts)
+        c = max(cnts, key=cv2.contourArea)
+
+        # Find the extreme points and crop the image
+        extLeft = tuple(c[c[:, :, 0].argmin()][0])
+        extRight = tuple(c[c[:, :, 0].argmax()][0])
+        extTop = tuple(c[c[:, :, 1].argmin()][0])
+        extBot = tuple(c[c[:, :, 1].argmax()][0])
+
+        ADD_PIXELS = add_pixels_value
+        new_img = img[extTop[1]-ADD_PIXELS:extBot[1]+ADD_PIXELS,
+                      extLeft[0]-ADD_PIXELS:extRight[0]+ADD_PIXELS].copy()
+        set_new.append(new_img)
+
+    return np.array(set_new)
+
+@app.route('/braintumor')
+def brain_tumor():
+    return render_template('braintumor.html')
+
+@app.route('/resultbt', methods=['POST'])
+def resultbt():
+    if request.method == 'POST':
+        firstname = request.form['firstname']
+        lastname = request.form['lastname']
+        email = request.form['email']
+        phone = request.form['phone']
+        gender = request.form['gender']
+        age = request.form['age']
+        file = request.files['file']
+
+        if file and allowed_file(file.filename):
+            filename = secure_filename(file.filename)
+            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
+            flash('Image successfully uploaded and displayed below')
+
+            img = cv2.imread('static/uploads/' + filename)
+            img = crop_imgs([img])
+            img = img.reshape(img.shape[1:])
+            img = preprocess_imgs([img], (224, 224))
+
+            pred = braintumor_model.predict(img)
+            if pred < 0.5:
+                pred = 0
+            else:
+                pred = 1
 
+            return render_template('resultbt.html', filename=filename, fn=firstname, ln=lastname, age=age, r=pred, gender=gender)
+        else:
+            flash('Allowed image types are - png, jpg, jpeg')
+            return redirect(request.url)
 
 if __name__ == '__main__':
-    app.run()
+    app.run(debug=True)