Delete BrainTumorPP

BrainTumorPP/.gitattributes

@@ -1,35 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

BrainTumorPP/Dockerfile

@@ -1,20 +0,0 @@
-# Using Python 3.9 base image
-FROM python:3.9
-
-# Set the working directory to /code
-WORKDIR /code
-
-# Install necessary system dependencies, including libGL.so.1
-RUN apt-get update && apt-get install -y libgl1 && apt-get clean
-
-# Copy requirements.txt to /code
-COPY ./requirements.txt /code/requirements.txt
-
-# Install dependencies from requirements.txt
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
-
-# Copy the entire project content to /code
-COPY . /code
-
-# CMD to run Gunicorn
-CMD ["gunicorn", "main:app", "-b", "0.0.0.0:7860"]

BrainTumorPP/README.md

@@ -1,11 +0,0 @@
----
-title: Flask
-emoji: 🚀
-colorFrom: yellow
-colorTo: red
-sdk: docker
-pinned: false
-license: cc
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

BrainTumorPP/cnn.txt

@@ -1,67 +0,0 @@
-In this Flask application, the model you're loading (`braintumor_model`) is a Convolutional Neural Network (CNN) trained to detect brain tumors in images. The CNN model is used to classify the images after preprocessing and passing them through the network. 
-Here's a breakdown of how CNN is used and how it's integrated into your Flask app:
-
-Key Concepts of CNN in Your Application
-
-1. Convolutional Layers (Feature Extraction):
-   CNNs are designed to automatically learn spatial hierarchies of features from the input image (in this case, brain tumor images).
-   The convolutional layers apply convolutional filters (kernels) that detect low-level features like edges, corners, and textures. As the image passes through multiple layers of convolutions, it progressively detects more complex features like shapes or regions of interest.
-
-2. Pooling Layers (Downsampling):
-   After convolutions, pooling layers (often Max Pooling) are applied to reduce the spatial dimensions of the feature maps.
-   This helps in reducing the computational complexity while preserving important features.
-
-3. Fully Connected Layers (Classification):
-   After the feature extraction and downsampling, the CNN typically flattens the resulting feature maps into a 1D vector and feeds it into fully connected layers (Dense layers).
-   The final fully connected layer outputs the prediction, which can be a binary classification in your case (whether a tumor is present or not).
-
-4. Activation Functions (Non-linearity):
-   The CNN typically uses activation functions like ReLU (Rectified Linear Unit) after each convolutional and fully connected layer to introduce non-linearity, allowing the model to learn complex patterns.
-   The final layer likely uses a sigmoid activation function (since it's a binary classification) to output a value between 0 and 1. A value close to 0 indicates no tumor, while a value close to 1 indicates a tumor.
-
-How the CNN Works in Your Flask App
-
-1. Model Loading:
-   You load a pre-trained CNN model using `braintumor_model = load_model('models/braintumor.h5')`.
-   This model is assumed to be trained on a dataset of brain images, where it learns to classify whether a brain tumor is present or not.
-
-2. Image Preprocessing:
-   Before the image is fed into the model for prediction, it's preprocessed using two main functions:
-     `crop_imgs`: Crops the region of interest (ROI) where the tumor is likely located. This reduces the unnecessary image data, focusing the model on the area that matters most.
-     `preprocess_imgs`: Resizes the image to the target size (224x224), which is the input size expected by the CNN. The CNN likely uses VGG16 or a similar architecture, which typically accepts 224x224 pixel images.
-
-3. Image Prediction:
-   - Once the image is preprocessed, it is passed into the CNN for prediction:
-   
-     pred = braintumor_model.predict(img)
-    
-   The model outputs a value between 0 and 1. This is the probability that the image contains a tumor.
-     If `pred < 0.5`, the model classifies the image as **no tumor** (`pred = 0`).
-     If `pred >= 0.5`, the model classifies the image as **tumor detected** (`pred = 1`).
-
-4. Displaying Results:
-   Based on the prediction, the result is displayed on the `resultbt.html` page, where the user is informed if the image contains a tumor or not.
-
-A High-Level Overview of CNN in Action:
-Image Input: A brain MRI image is uploaded by the user.
-Preprocessing: The image is cropped to focus on the relevant region (tumor area), resized to the required input size for the CNN, and normalized (if necessary).
-CNN Prediction: The processed image is passed through the CNN, which performs feature extraction and classification. The output is a probability score (0 or 1) indicating the likelihood of a tumor being present.
-Output: The app displays whether a tumor is present or not based on the CNN's prediction.
-
-CNN Model Workflow (High-Level)
-
-1. Convolution Layers: Learn to detect features like edges, textures, and structures in the image.
-2. Pooling Layers: Reduce the dimensionality while retaining key features.
-3. Fully Connected Layers: Use the learned features to make a classification decision (tumor vs. no tumor).
-4. Prediction: The model outputs a binary classification result: `0` (no tumor) or `1` (tumor detected).
-
-Training of the CNN Model (Assumed):
-The model (`braintumor_model.h5`) you are loading in the app is assumed to be pre-trained on a large dataset of brain tumor images (e.g., MRI scans), where it has learned the distinguishing features of images with and without tumors. Typically, this training would involve:
-Convolutional layers for feature extraction.
-Pooling layers to reduce spatial dimensions.
-Fully connected layers to classify the image as containing a tumor or not.
-  
-This pre-trained model can then be used for inference (prediction) on new images that are uploaded by the user.
-
-Your application uses a Convolutional Neural Network (CNN) to detect brain tumors in images. 
-The CNN is trained to learn features from medical images, and when a user uploads an image, the app preprocesses it, passes it through the model, and provides a prediction (tumor detected or not). The model’s decision is based on its learned understanding of what a tumor looks like, making it an effective tool for automatic detection.

BrainTumorPP/explain.md

@@ -1,213 +0,0 @@
-### **Code Explanation for Brain Tumor Detection Application Using Flask**
-
-This Flask-based application allows users to upload an image (like an MRI scan) to predict if a brain tumor is detected using a pre-trained CNN (Convolutional Neural Network) model. The results, along with user details, are saved in a MongoDB database for review.
-
----
-
-### **1. Importing Required Libraries**
-
-```python
-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
-```
-
-- **Flask**: A lightweight web framework to create web applications.
-- **OpenCV (cv2)**: Library for image processing.
-- **imutils**: Helper functions for image manipulation.
-- **NumPy**: Array and mathematical operations.
-- **TensorFlow/Keras**: To load the pre-trained brain tumor detection model.
-- **MongoDB**: To store user inputs and model predictions.
-- **Werkzeug**: For securely handling file uploads.
-- **Datetime**: To save the timestamp for each prediction.
-
----
-
-### **2. Loading the Pre-trained Model**
-
-```python
-braintumor_model = load_model('models/braintumor.h5')
-```
-- The brain tumor model (`braintumor.h5`) is loaded. This is a CNN-based model trained to detect brain tumors from images.
-
----
-
-### **3. Flask Application Configuration**
-
-```python
-app = Flask(__name__)
-app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0  # Disable caching
-app.secret_key = "nielitchandigarhpunjabpolice"
-```
-
-- Flask is initialized, and caching for images is disabled to ensure updated images load after upload.
-- A **secret key** is set for session management, which helps in managing messages (like flash messages).
-
----
-
-### **4. MongoDB Connection**
-
-```python
-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
-```
-- Connects to **MongoDB Atlas** (cloud-hosted database).
-- A database named `brain_tumor_detection` and collection `predictions` are created to store user details and predictions.
-
----
-
-### **5. File Upload Helper Function**
-
-```python
-ALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg'])
-
-def allowed_file(filename):
-    return '.' in filename and filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
-```
-- Only files with extensions **png, jpg, jpeg** are allowed to ensure proper image input.
-
----
-
-### **6. Image Preprocessing**
-
-**a. `preprocess_imgs`: Resizing Images**
-
-```python
-def preprocess_imgs(set_name, img_size):
-    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)
-```
-- Resizes the input image to a specific size (224x224) required by the model.
-
-**b. `crop_imgs`: Region of Interest (ROI) Extraction**
-
-```python
-def crop_imgs(set_name, add_pixels_value=0):
-    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]
-        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-        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])
-        new_img = img[extTop[1]:extBot[1], extLeft[0]:extRight[0]].copy()
-        set_new.append(new_img)
-    return np.array(set_new)
-```
-- This function identifies the **region of interest (ROI)**, cropping only the area where the brain is located for better accuracy.
-
----
-
-### **7. Routes in Flask**
-
-**a. `/` Route - Main Page**
-
-```python
-@app.route('/')
-def brain_tumor():
-    return render_template('braintumor.html')
-```
-- Displays the main upload form (`braintumor.html`).
-
-**b. `/resultbt` Route - Prediction**
-
-```python
-@app.route('/resultbt', methods=['POST'])
-def resultbt():
-    # 1. Extract user inputs
-    firstname = request.form['firstname']
-    file = request.files['file']
-    
-    # 2. Validate image
-    if file and allowed_file(file.filename):
-        temp_file = tempfile.NamedTemporaryFile(delete=False)
-        file.save(temp_file.name)
-
-        # 3. Process Image
-        img = cv2.imread(temp_file.name)
-        img = crop_imgs([img])
-        img = preprocess_imgs([img], (224, 224))
-
-        # 4. Predict
-        pred = braintumor_model.predict(img)
-        prediction = 'Tumor Detected' if pred[0][0] >= 0.5 else 'No Tumor Detected'
-        confidence_score = float(pred[0][0])
-
-        # 5. Save to MongoDB
-        result = {
-            "firstname": firstname,
-            "prediction": prediction,
-            "confidence_score": confidence_score,
-            "timestamp": datetime.utcnow()
-        }
-        collection.insert_one(result)
-
-        # 6. Return Results
-        return render_template('resultbt.html', r=prediction)
-    else:
-        flash('Invalid file format!')
-        return redirect(request.url)
-```
-- **Step-by-step Flow**:
-  1. Accept user inputs and uploaded image.
-  2. Validate the file format.
-  3. Preprocess the image (crop and resize).
-  4. Use the CNN model to predict if there is a tumor.🚀
-  5. Save the prediction and user details to MongoDB.
-  6. Return the result.
-
-**c. `/dbresults` Route - Fetch Predictions**
-
-```python
-@app.route('/dbresults')
-def dbresults():
-    all_results = collection.find().sort("timestamp", -1)
-    tumor_count = sum(1 for r in all_results if r['prediction'] == 'Tumor Detected')
-    total_patients = collection.count_documents({})
-    return render_template('dbresults.html', total_patients=total_patients, tumor_count=tumor_count)
-```
-- Fetches all predictions from MongoDB and aggregates results (total patients, tumors detected).
-
----
-
-### **8. Running the App**
-
-```python
-if __name__ == '__main__':
-    app.run(debug=True)
-```
-- Runs the Flask application in **debug mode**.
-
----
-
-### **Summary of Flow**
-1. User uploads an MRI image and provides basic details.
-2. Image is preprocessed (cropped, resized) for the CNN model.
-3. The model predicts if a **brain tumor is detected** or not.
-4. Results are stored in MongoDB and displayed back to the user.
-5. Admins can view all results via the `/dbresults` route.
-
----
-
-### **Tips**
-1. **Flask Routes** handle user requests (`/`, `/resultbt`, `/dbresults`).
-2. **OpenCV** helps preprocess images.
-3. **MongoDB** stores user details and model predictions.
-4. **Model Prediction** is done via a pre-trained Keras model.
-5. Templates (`braintumor.html`, `resultbt.html`) are used to display data.
-

BrainTumorPP/main.py

@@ -1,157 +0,0 @@
-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: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
-
-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__':
-    app.run(debug=True)

BrainTumorPP/models/braintumor.h5

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7a756efdea8aa2748819ea2ccfbf2e0f4dad62236726532adb50439f06f55165
-size 59111488

BrainTumorPP/requirements.txt

@@ -1,16 +0,0 @@
-flask
-flask-cors
-gunicorn
-Jinja2
-pandas
-numpy
-scikit-learn
-tensorflow
-imutils
-opencv-python
-matplotlib
-Werkzeug
-Pillow
-pymongo
-datetime
-

BrainTumorPP/templates/braintumor.html

@@ -1,230 +0,0 @@
-<!doctype html>
-<html lang="en">
-
-<head>
-    <!-- Required meta tags -->
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-
-    <!-- Bootstrap CSS -->
-    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta3/dist/css/bootstrap.min.css" rel="stylesheet"
-        integrity="sha384-eOJMYsd53ii+scO/bJGFsiCZc+5NDVN2yr8+0RDqr0Ql0h+rP48ckxlpbzKgwra6" crossorigin="anonymous">
-
-    <!-- Custom Styles -->
-    <style type="text/css">
-        body {
-            background-image: url(static/brain.gif);
-            background-position: center;
-            background-size: cover;
-            font-family: 'Roboto', sans-serif;
-            margin-top: 40px;
-            color: #fff;
-        }
-
-        .navbar {
-            background-color: #212121 !important;
-        }
-
-        .navbar-brand {
-            font-weight: bold;
-            font-size: 1.5rem;
-        }
-
-        .regform {
-            background-color: rgba(0, 0, 0, 0.6);
-            padding: 20px;
-            text-align: center;
-            border-radius: 15px 15px 0 0;
-            box-shadow: 0 10px 15px rgba(0, 0, 0, 0.5);
-            margin-top: 20px;
-        }
-
-        .regform h1 {
-            font-size: 2rem;
-            font-weight: bold;
-        }
-
-        .main-form {
-            background-color: rgba(0, 0, 0, 0.7);
-            margin: 20px auto;
-            padding: 30px;
-            border-radius: 10px;
-            max-width: 900px;
-        }
-
-        .form-group label {
-            font-size: 1rem;
-            font-weight: 500;
-        }
-
-        .form-control {
-            border-radius: 5px;
-            box-shadow: none;
-            border: 2px solid #bbb;
-            margin-bottom: 15px;
-        }
-
-        .form-control:focus {
-            border-color: #5cb85c;
-        }
-
-        .btn-submit {
-            background-color: #28a745;
-            color: white;
-            font-size: 1.1rem;
-            padding: 15px 30px;
-            border-radius: 5px;
-            border: none;
-            transition: background-color 0.3s ease;
-        }
-
-        .btn-submit:hover {
-            background-color: #218838;
-        }
-
-        .form-text {
-            font-size: 0.85rem;
-        }
-
-        .col-md-6 {
-            margin-bottom: 15px;
-        }
-
-        .col-md-6 label,
-        .col-md-6 input,
-        .col-md-6 select {
-            font-size: 1rem;
-        }
-
-        .footer {
-            text-align: center;
-            margin-top: 30px;
-            font-size: 0.9rem;
-            color: #ccc;
-        }
-
-        /* Responsive design */
-        @media (max-width: 767px) {
-            .regform h1 {
-                font-size: 1.6rem;
-            }
-
-            .main-form {
-                padding: 20px;
-            }
-        }
-    </style>
-
-    <title>Brain Tumor Detection</title>
-</head>
-
-<body>
-    <nav class="navbar navbar-expand-lg navbar-dark bg-dark">
-        <div class="container-fluid">
-            <a class="navbar-brand" href="/">CNN Brain Tumor Detection</a>
-            <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarNav"
-                aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation">
-                <span class="navbar-toggler-icon"></span>
-            </button>
-            <div class="collapse navbar-collapse" id="navbarNav">
-                <ul class="navbar-nav ms-auto">
-                    <li class="nav-item">
-                        <a class="nav-link" href="/dbresults">View MongoDB Results</a>
-                    </li>
-                </ul>
-            </div>
-        </div>
-    </nav>
-
-    <div class="regform">
-        <h1>Brain Tumor Detection Form</h1>
-    </div>
-
-    <div class="container">
-        <form action="resultbt" class="main-form needs-validation" method="POST" enctype="multipart/form-data" novalidate>
-            <div class="row">
-                <div class="col-md-6">
-                    <div class="form-group">
-                        <label for="firstname">First Name</label>
-                        <input type="text" name="firstname" id="firstname" class="form-control" required>
-                    </div>
-                </div>
-                <div class="col-md-6">
-                    <div class="form-group">
-                        <label for="lastname">Last Name</label>
-                        <input type="text" name="lastname" id="lastname" class="form-control" required>
-                    </div>
-                </div>
-            </div>
-
-            <div class="form-group">
-                <label for="phone">Phone Number</label>
-                <input type="tel" name="phone" id="phone" class="form-control" required>
-                <small class="form-text">* Include your area code</small>
-            </div>
-
-            <div class="form-group">
-                <label for="email">Email Address</label>
-                <input type="email" name="email" id="email" class="form-control" required>
-            </div>
-
-            <div class="row">
-                <div class="col-md-6">
-                    <div class="form-group">
-                        <label for="gender">Gender</label>
-                        <select name="gender" id="gender" class="form-control" required>
-                            <option value="male">Male</option>
-                            <option value="female">Female</option>
-                        </select>
-                    </div>
-                </div>
-                <div class="col-md-6">
-                    <div class="form-group">
-                        <label for="age">Age</label>
-                        <input type="number" name="age" id="age" class="form-control" required>
-                    </div>
-                </div>
-            </div>
-
-            <div class="form-group">
-                <label for="file">Upload Your Brain MRI</label>
-                <input type="file" class="form-control" id="file" name="file" required>
-            </div>
-
-            <div class="text-center">
-                <button type="submit" class="btn-submit">Submit</button>
-            </div>
-        </form>
-    </div>
-
-    <div class="footer">
-        <p>&copy; 2024 Brain Tumor Detection - All Rights Reserved</p>
-    </div>
-
-    <!-- Bootstrap Bundle with Popper -->
-    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta3/dist/js/bootstrap.bundle.min.js"
-        integrity="sha384-JEW9xMcG8R+pH31jmWH6WWP0WintQrMb4s7ZOdauHnUtxwoG2vI5DkLtS3qm9Ekf"
-        crossorigin="anonymous"></script>
-
-    <!-- Enable Bootstrap validation -->
-    <script>
-        (function () {
-            'use strict'
-            // Fetch all the forms we want to apply custom Bootstrap validation styles to
-            var forms = document.querySelectorAll('.needs-validation')
-            // Loop over them and prevent submission
-            Array.prototype.slice.call(forms)
-                .forEach(function (form) {
-                    form.addEventListener('submit', function (event) {
-                        if (!form.checkValidity()) {
-                            event.preventDefault()
-                            event.stopPropagation()
-                        }
-                        form.classList.add('was-validated')
-                    }, false)
-                })
-        })()
-    </script>
-</body>
-
-</html>

BrainTumorPP/templates/dbresults.html

@@ -1,151 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Database Results</title>
-
-    <!-- Custom Styles -->
-    <style>
-        body {
-            font-family: 'Roboto', sans-serif;
-            background-image: url('static/brain.gif');
-            background-position: center;
-            background-size: cover;
-            color: #fff;
-            padding-top: 20px;
-        }
-
-        h1 {
-            text-align: center;
-            font-size: 2rem;
-            font-weight: bold;
-            color: #fff;
-        }
-
-        .stats-container {
-            width: 90%;
-            margin: 20px auto;
-            text-align: center;
-            background-color: rgba(0, 0, 0, 0.7);
-            padding: 20px;
-            border-radius: 10px;
-            box-shadow: 0 5px 10px rgba(0, 0, 0, 0.5);
-        }
-
-        .stats-container h2 {
-            font-size: 1.5rem;
-            margin: 10px 0;
-            color: #fff;
-        }
-
-        table {
-            width: 90%;
-            margin: 30px auto;
-            border-collapse: collapse;
-            background-color: rgba(0, 0, 0, 0.6);
-            border-radius: 10px;
-            box-shadow: 0 10px 20px rgba(0, 0, 0, 0.6);
-        }
-
-        th, td {
-            border: 1px solid #ddd;
-            padding: 12px 18px;
-            text-align: center;
-            font-size: 1.1rem;
-        }
-
-        th {
-            background-color: #343a40;
-            color: #fff;
-        }
-
-        td {
-            background-color: rgba(255, 255, 255, 0.1);
-            color: #fff;
-        }
-
-        tr:nth-child(even) {
-            background-color: rgba(255, 255, 255, 0.15);
-        }
-
-        tr:hover {
-            background-color: rgba(255, 255, 255, 0.3);
-        }
-
-        .footer {
-            text-align: center;
-            margin-top: 30px;
-            color: #ccc;
-            font-size: 0.9rem;
-        }
-
-        @media (max-width: 767px) {
-            h1 {
-                font-size: 1.6rem;
-            }
-
-            table {
-                width: 100%;
-            }
-
-            th, td {
-                padding: 10px 12px;
-                font-size: 1rem;
-            }
-        }
-    </style>
-</head>
-<body>
-
-    <!-- Page Title -->
-    <h1>Stored Results</h1>
-
-    <!-- Stats Section -->
-    <div class="stats-container">
-        <h2>Total Patients: {{ total_patients }}</h2>
-        <h2>Total with Tumor: {{ tumor_count }}</h2>
-        <h2>Total without Tumor: {{ no_tumor_count }}</h2>
-    </div>
-
-    <!-- Table Container -->
-    <div class="table-container">
-        <table>
-            <thead>
-                <tr>
-                    <th>First Name</th>
-                    <th>Last Name</th>
-                    <th>Email</th>
-                    <th>Phone</th>
-                    <th>Gender</th>
-                    <th>Age</th>
-                    <th>Prediction</th>
-                    <th>Confidence Score</th>
-                    <th>Timestamp</th>
-                </tr>
-            </thead>
-            <tbody>
-                {% for result in results %}
-                <tr>
-                    <td>{{ result.firstname }}</td>
-                    <td>{{ result.lastname }}</td>
-                    <td>{{ result.email }}</td>
-                    <td>{{ result.phone }}</td>
-                    <td>{{ result.gender }}</td>
-                    <td>{{ result.age }}</td>
-                    <td>{{ result.prediction }}</td>
-                    <td>{{ result.confidence_score }}</td>
-                    <td>{{ result.timestamp }}</td>
-                </tr>
-                {% endfor %}
-            </tbody>
-        </table>
-    </div>
-
-    <!-- Footer -->
-    <div class="footer">
-        <p>&copy; 2024 Brain Tumor Detection - All Rights Reserved</p>
-    </div>
-
-</body>
-</html>

BrainTumorPP/templates/resultbt.html

@@ -1,147 +0,0 @@
-<!doctype html>
-<html lang="en">
-
-<head>
-    <!-- Required meta tags -->
-    <meta charset="utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1">
-
-    <!-- Bootstrap CSS -->
-    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta3/dist/css/bootstrap.min.css" rel="stylesheet"
-        integrity="sha384-eOJMYsd53ii+scO/bJGFsiCZc+5NDVN2yr8+0RDqr0Ql0h+rP48ckxlpbzKgwra6" crossorigin="anonymous">
-
-    <!-- Custom Styles -->
-    <style type="text/css">
-        body {
-            background-image: url(static/brain.gif);
-            background-position: center;
-            background-size: cover;
-            font-family: 'Roboto', sans-serif;
-            margin-top: 40px;
-            color: #fff;
-        }
-
-        .navbar {
-            background-color: #212121 !important;
-        }
-
-        .navbar-brand {
-            font-weight: bold;
-            font-size: 1.5rem;
-        }
-
-        .regform {
-            width: 800px;
-            background-color: rgba(253, 252, 252, 0.8);
-            margin: auto;
-            color: #0f0f0f;
-            padding: 20px;
-            text-align: center;
-            border-radius: 15px 15px 0px 0px;
-            box-shadow: 0 10px 15px rgba(0, 0, 0, 0.5);
-        }
-
-        .regform h1 {
-            font-size: 2rem;
-            font-weight: bold;
-        }
-
-        .main-form {
-            width: 800px;
-            margin: auto;
-            background-color: rgba(0, 0, 0, 0.7);
-            padding: 30px;
-            border-radius: 10px;
-            color: #FFFFFF;
-            box-shadow: 0 10px 15px rgba(0, 0, 0, 0.7);
-        }
-
-        .main-form p {
-            font-size: 1.2rem;
-            line-height: 1.8;
-        }
-
-        .main-form p i {
-            font-style: italic;
-            color: #28a745;
-        }
-
-        .footer {
-            text-align: center;
-            margin-top: 30px;
-            font-size: 0.9rem;
-            color: #ccc;
-        }
-
-        /* Responsive design */
-        @media (max-width: 767px) {
-            .regform h1 {
-                font-size: 1.6rem;
-            }
-
-            .main-form {
-                padding: 20px;
-            }
-
-            .main-form p {
-                font-size: 1rem;
-            }
-        }
-    </style>
-
-    <title>Brain Tumor Detection</title>
-</head>
-
-<body>
-    <!-- Navbar -->
-    <nav class="navbar navbar-expand-lg navbar-dark bg-dark">
-        <div class="container-fluid">
-            <a class="navbar-brand" href="/">CNN</a>
-            <button class="navbar-toggler" type="button" data-bs-toggle="collapse"
-                data-bs-target="#navbarSupportedContent" aria-controls="navbarSupportedContent" aria-expanded="false"
-                aria-label="Toggle navigation">
-                <span class="navbar-toggler-icon"></span>
-            </button>
-            <div class="collapse navbar-collapse" id="navbarSupportedContent">
-                <ul class="navbar-nav ms-auto mb-2 mb-lg-0">
-                    <li class="nav-item">
-                        <a class="nav-link" href="/dbresults">MongoDB</a>
-                    </li>
-                </ul>
-            </div>
-        </div>
-    </nav>
-
-    <!-- Result Form -->
-    <div class='regform mt-3'>
-        <h1>Brain Tumor Test Results</h1>
-    </div>
-
-    <div class='main-form'>
-        <div>
-            <div class="col" style='margin-top: 30px; margin-bottom: 30px;'>
-                <p><strong>First Name:</strong> {{fn}}</p>
-                <p><strong>Last Name:</strong> {{ln}}</p>
-                <p><strong>Age:</strong> {{age}}</p>
-                <p><strong>Gender:</strong> {{gender}}</p>
-
-      
-                <div>
-                    <p><strong>Result:</strong> <i>{{r}}</i></p>
-                </div>
-            </div>
-        </div>
-    </div>
-
-    <!-- Footer -->
-    <div class="footer">
-        <p>&copy; 2024 Brain Tumor Detection - All Rights Reserved</p>
-    </div>
-
-    <!-- Bootstrap Bundle with Popper -->
-    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta3/dist/js/bootstrap.bundle.min.js"
-        integrity="sha384-JEW9xMcG8R+pH31jmWH6WWP0WintQrMb4s7ZOdauHnUtxwoG2vI5DkLtS3qm9Ekf"
-        crossorigin="anonymous"></script>
-</body>
-
-</html>