Update main.py

main.py

@@ -6,6 +6,8 @@ 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')
@@ -18,6 +20,11 @@ app.secret_key = "nielitchandigarhpunjabpolice"  # Secret key for session manage
 # Allowed image file extensions
 ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
 
+# Connect to MongoDB Atlas
+client = MongoClient("mongodb+srv://test:test@cluster0.sxci1.mongodb.net/?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
@@ -36,90 +43,100 @@ def preprocess_imgs(set_name, img_size):
 def crop_imgs(set_name, add_pixels_value=0):
     """
     Crop the region of interest (ROI) in the image for brain tumor detection.
-    This method finds contours in the image, then crops the detected tumor region.
     """
     set_new = []
     for img in set_name:
-        # Convert to grayscale and apply Gaussian blur
         gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
         gray = cv2.GaussianBlur(gray, (5, 5), 0)
-
-        # Threshold the image and find contours
         thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
         thresh = cv2.erode(thresh, None, iterations=2)
         thresh = cv2.dilate(thresh, None, iterations=2)
-
-        # Find the largest contour and crop the image around it
         cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
         cnts = imutils.grab_contours(cnts)
         c = max(cnts, key=cv2.contourArea)
-
-        # Get the extreme points (left, right, top, bottom) of the contour for cropping
         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 some extra pixels around the contour to ensure the full tumor is captured
         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():
-    """
-    The main page that renders the HTML form for the user to upload an image
-    and input their details.
-    """
+    """Render the HTML form for the user to upload an image."""
     return render_template('braintumor.html')
 
 @app.route('/resultbt', methods=['POST'])
 def resultbt():
-    """
-    The result page that processes the uploaded image, makes predictions using 
-    the trained brain tumor detection model, and displays the result.
-    """
+    """Process the uploaded image and save prediction results to MongoDB."""
     if request.method == 'POST':
-        # Get user input from the form (name, email, phone, etc.)
         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']  # The uploaded image file
+        file = request.files['file']
 
         if file and allowed_file(file.filename):
-            # Use tempfile to create a temporary file path for the uploaded image
             temp_file = tempfile.NamedTemporaryFile(delete=False)
-            filename = secure_filename(file.filename)  # Secure the filename
-            file.save(temp_file.name)  # Save file to the temp location
+            filename = secure_filename(file.filename)
+            file.save(temp_file.name)
 
             flash('Image successfully uploaded and displayed below')
 
-            # Read the image, crop the region of interest (tumor), and preprocess
+            # Process the image
             img = cv2.imread(temp_file.name)
-            img = crop_imgs([img])  # Crop the tumor area from the image
-            img = img.reshape(img.shape[1:])  # Reshape for the model input
-            img = preprocess_imgs([img], (224, 224))  # Resize image to 224x224
+            img = crop_imgs([img])
+            img = img.reshape(img.shape[1:])
+            img = preprocess_imgs([img], (224, 224))
 
-            # Model prediction: 0 - no tumor, 1 - tumor detected
+            # Make prediction
             pred = braintumor_model.predict(img)
-            if pred < 0.5:
-                pred = 0  # No tumor detected
-            else:
-                pred = 1  # Tumor detected
-
-            # Return the result to the user, including input data and prediction
-            return render_template('resultbt.html', filename=filename, fn=firstname, ln=lastname, age=age, r=pred, gender=gender)
+            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:
-            # If the file is not valid, show an error message
             flash('Allowed image types are - png, jpg, jpeg')
             return redirect(request.url)
 
+@app.route('/dbresults')
+def dbresults():
+    """Fetch all results from MongoDB and render them in a template."""
+    # Fetch all documents from the MongoDB collection
+    all_results = collection.find()  # Returns a cursor
+    
+    # Convert cursor to a list of dictionaries
+    results_list = []
+    for result in all_results:
+        result['_id'] = str(result['_id'])  # Convert ObjectId to string for JSON serialization
+        results_list.append(result)
+
+    # Pass the results to the HTML template
+    return render_template('dbresults.html', results=results_list)
+
+
 if __name__ == '__main__':
-    # Run the app in debug mode for development purposes
     app.run(debug=True)