Update main.py

main.py

@@ -1,157 +1,163 @@
-from flask import Flask, flash, request, redirect, render_template
-import os
-import cv2
-import imutils
-import numpy as np
-from tensorflow.keras.models import load_model
-from werkzeug.utils import secure_filename
-import tempfile
-from pymongo import MongoClient
-from datetime import datetime
-
-# Load the Brain Tumor CNN Model
-braintumor_model = load_model('models/braintumor.h5')
-
-# Configuring Flask application
-app = Flask(__name__)
-app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0  # Disable caching for images
-app.secret_key = "nielitchandigarhpunjabpolice"  # Secret key for session management
-
-# Allowed image file extensions
-ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
-
-# Connect to MongoDB Atlas
-client = MongoClient("mongodb+srv://test:[email protected]/?retryWrites=true&w=majority")
-db = client['brain_tumor_detection']  # Database name
-collection = db['predictions']  # Collection name
-
-def allowed_file(filename):
-    """Check if the file is a valid image format (png, jpg, jpeg)."""
-    return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
-
-def preprocess_imgs(set_name, img_size):
-    """
-    Preprocess images by resizing them to the target size (224x224 for VGG16) 
-    and applying appropriate resizing techniques.
-    """
-    set_new = []
-    for img in set_name:
-        img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC)  # Resize image
-        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 for brain tumor detection.
-    """
-    set_new = []
-    for img in set_name:
-        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-        gray = cv2.GaussianBlur(gray, (5, 5), 0)
-        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)
-        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('/')
-def brain_tumor():
-    """Render the HTML form for the user to upload an image."""
-    return render_template('braintumor.html')
-
-@app.route('/resultbt', methods=['POST'])
-def resultbt():
-    """Process the uploaded image and save prediction results to MongoDB."""
-    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):
-            temp_file = tempfile.NamedTemporaryFile(delete=False)
-            filename = secure_filename(file.filename)
-            file.save(temp_file.name)
-
-            flash('Image successfully uploaded and displayed below')
-
-            # Process the image
-            img = cv2.imread(temp_file.name)
-            img = crop_imgs([img])
-            img = img.reshape(img.shape[1:])
-            img = preprocess_imgs([img], (224, 224))
-
-            # Make prediction
-            pred = braintumor_model.predict(img)
-            prediction = 'Tumor Detected' if pred[0][0] >= 0.5 else 'No Tumor Detected'
-            confidence_score = float(pred[0][0])
-
-            # Prepare data for MongoDB
-            result = {
-                "firstname": firstname,
-                "lastname": lastname,
-                "email": email,
-                "phone": phone,
-                "gender": gender,
-                "age": age,
-                "image_name": filename,
-                "prediction": prediction,
-                "confidence_score": confidence_score,
-                "timestamp": datetime.utcnow()
-            }
-
-            # Insert data into MongoDB
-            collection.insert_one(result)
-
-            # Return the result to the user
-            return render_template('resultbt.html', filename=filename, fn=firstname, ln=lastname, age=age, r=prediction, gender=gender)
-        else:
-            flash('Allowed image types are - png, jpg, jpeg')
-            return redirect(request.url)
-
-@app.route('/dbresults')
-def dbresults():
-    """Fetch all results from MongoDB, show aggregated data, and render in a template."""
-    # Fetch all documents from MongoDB, sorted by timestamp in descending order
-    all_results = collection.find().sort("timestamp", -1)  # Sort by timestamp, latest first
-
-    # Convert cursor to a list of dictionaries
-    results_list = []
-    tumor_count = 0
-    no_tumor_count = 0
-
-    for result in all_results:
-        result['_id'] = str(result['_id'])  # Convert ObjectId to string for JSON serialization
-        results_list.append(result)
-
-        # Count total patients with tumor and without tumor
-        if result['prediction'] == 'Tumor Detected':
-            tumor_count += 1
-        else:
-            no_tumor_count += 1
-
-    total_patients = len(results_list)  # Total number of patients
-
-    # Pass the results and aggregated counts to the HTML template
-    return render_template('dbresults.html', 
-                           results=results_list, 
-                           total_patients=total_patients, 
-                           tumor_count=tumor_count, 
-                           no_tumor_count=no_tumor_count)
-
-
-if __name__ == '__main__':
+from flask import Flask, flash, request, redirect, render_template
+import os
+import cv2
+import imutils
+import numpy as np
+from tensorflow.keras.models import load_model
+from werkzeug.utils import secure_filename
+import tempfile
+from pymongo import MongoClient
+from datetime import datetime
+
+# Load the Brain Tumor CNN Model
+braintumor_model = load_model('models/braintumor.h5')
+
+# Configuring Flask application
+app = Flask(__name__)
+app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0  # Disable caching for images
+app.secret_key = "nielitchandigarhpunjabpolice"  # Secret key for session management
+
+# Allowed image file extensions
+ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
+
+# Connect to MongoDB Atlas
+client = MongoClient("mongodb+srv://test:[email protected]/?retryWrites=true&w=majority")
+db = client['brain_tumor_detection']  # Database name
+collection = db['predictions']  # Collection name
+
+def allowed_file(filename):
+    """Check if the file is a valid image format (png, jpg, jpeg)."""
+    return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
+
+def preprocess_imgs(set_name, img_size):
+    """
+    Preprocess images by resizing them to the target size (224x224 for VGG16) 
+    and applying appropriate resizing techniques.
+    """
+    set_new = []
+    for img in set_name:
+        img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC)  # Resize image
+        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 for brain tumor detection.
+    """
+    set_new = []
+    for img in set_name:
+        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+        gray = cv2.GaussianBlur(gray, (5, 5), 0)
+        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)
+        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('/')
+def brain_tumor():
+    """Render the HTML form for the user to upload an image."""
+    return render_template('braintumor.html')
+
+@app.route('/resultbt', methods=['POST'])
+def resultbt():
+    """Process the uploaded image and save prediction results to MongoDB."""
+    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):
+            temp_file = tempfile.NamedTemporaryFile(delete=False)
+            filename = secure_filename(file.filename)
+            file.save(temp_file.name)
+
+            flash('Image successfully uploaded and displayed below')
+
+            try:
+                # Process the image
+                img = cv2.imread(temp_file.name)
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB
+                img = crop_imgs([img])
+                img = img.reshape(img.shape[1:])
+                img = preprocess_imgs([img], (128, 128))  # Match model's input size
+                img = np.expand_dims(img, axis=0)  # Add batch dimension
+
+                # Make prediction
+                pred = braintumor_model.predict(img)
+                prediction = 'Tumor Detected' if pred[0][0] >= 0.5 else 'No Tumor Detected'
+                confidence_score = float(pred[0][0])
+
+                # Prepare data for MongoDB
+                result = {
+                    "firstname": firstname,
+                    "lastname": lastname,
+                    "email": email,
+                    "phone": phone,
+                    "gender": gender,
+                    "age": age,
+                    "image_name": filename,
+                    "prediction": prediction,
+                    "confidence_score": confidence_score,
+                    "timestamp": datetime.utcnow()
+                }
+
+                # Insert data into MongoDB
+                collection.insert_one(result)
+
+                # Return the result to the user
+                return render_template('resultbt.html', filename=filename, fn=firstname, ln=lastname, age=age, r=prediction, gender=gender)
+            finally:
+                os.remove(temp_file.name)  # Ensure temporary file is deleted
+        else:
+            flash('Allowed image types are - png, jpg, jpeg')
+            return redirect(request.url)
+
+
+@app.route('/dbresults')
+def dbresults():
+    """Fetch all results from MongoDB, show aggregated data, and render in a template."""
+    # Fetch all documents from MongoDB, sorted by timestamp in descending order
+    all_results = collection.find().sort("timestamp", -1)  # Sort by timestamp, latest first
+
+    # Convert cursor to a list of dictionaries
+    results_list = []
+    tumor_count = 0
+    no_tumor_count = 0
+
+    for result in all_results:
+        result['_id'] = str(result['_id'])  # Convert ObjectId to string for JSON serialization
+        results_list.append(result)
+
+        # Count total patients with tumor and without tumor
+        if result['prediction'] == 'Tumor Detected':
+            tumor_count += 1
+        else:
+            no_tumor_count += 1
+
+    total_patients = len(results_list)  # Total number of patients
+
+    # Pass the results and aggregated counts to the HTML template
+    return render_template('dbresults.html', 
+                           results=results_list, 
+                           total_patients=total_patients, 
+                           tumor_count=tumor_count, 
+                           no_tumor_count=no_tumor_count)
+
+
+if __name__ == '__main__':
     app.run(debug=True)