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from flask import Flask, flash, request, redirect, render_template
import os
import cv2
import imutils
import numpy as np
from tensorflow.keras.preprocessing.image import load_img, img_to_array
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_binary.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['btpredictions'] # 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
@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:
# Load and preprocess the image
img = load_img(temp_file.name, target_size=(128, 128)) # Resize image to match model's input size
img_array = img_to_array(img) # Convert image to array
img_array = np.expand_dims(img_array, axis=0) / 255.0 # Normalize image and add batch dimension
# Make prediction
pred = braintumor_model.predict(img_array)
prediction = pred[0][0]
confidence = prediction if prediction > 0.5 else 1 - prediction # Calculate confidence
predicted_class = 'Tumor Detected' if prediction > 0.5 else 'No Tumor Detected' # Determine class based on threshold
# Prepare data for MongoDB with JSON-serializable fields
result = {
"firstname": firstname,
"lastname": lastname,
"email": email,
"phone": phone,
"gender": gender,
"age": age,
"image_name": filename,
"prediction": predicted_class,
"confidence_score": float(confidence), # Ensure it's a standard float for JSON serialization
"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=predicted_class, 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)
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