Upload 49 files

.gitattributes

@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
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+temp/Screenshot_2024-11-18_at_9.42.42_AM.png filter=lfs diff=lfs merge=lfs -text
+temp/vector-creative-login-ui-template-form-design-with-technology-style-bac.jpg filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,43 @@
+# Cancer Detection Tool - Dockerfile
+FROM python:3.11-slim
+
+# Prevent Python from writing pyc and buffering stdout/stderr
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV DOCKER=1
+
+WORKDIR /app
+
+# Install system dependencies for OpenCV (optional but recommended)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libgl1-mesa-glx \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy dependency file first for better layer caching
+COPY requirements.txt .
+
+# Install Python dependencies (no versions in requirements.txt)
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY app.py .
+COPY wsgi.py .
+COPY init_db.py .
+COPY train_model.py .
+COPY templates/ ./templates/
+COPY static/ ./static/
+
+# Create temp directory for uploads and PDFs
+RUN mkdir -p /app/temp
+
+# Build model if not present (creates breast_cancer_detector.pickle)
+RUN python train_model.py
+
+# Expose Flask port
+EXPOSE 5000
+
+# Run via WSGI (gunicorn) for production; bind to 0.0.0.0 so it's reachable from host
+# Build: docker build -t cancer-detection .
+# Run:   docker run -p 5000:5000 cancer-detection
+CMD ["gunicorn", "--bind", "0.0.0.0:5000", "--workers", "1", "wsgi:app"]

app.py

@@ -0,0 +1,166 @@
+from flask import Flask, request, render_template, redirect, url_for, session, send_file
+import cv2
+import numpy as np
+import os
+import pickle
+import logging
+from werkzeug.utils import secure_filename
+from datetime import datetime
+from reportlab.lib.pagesizes import A4
+from reportlab.pdfgen import canvas
+
+app = Flask(__name__)
+app.secret_key = os.getenv("FLASK_SECRET_KEY", "fallback_secret_key")
+
+# Set up logging
+app.logger.setLevel(logging.DEBUG)
+
+# Base directory (works when run from project root or Docker /app)
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+TEMP_DIR = os.path.join(BASE_DIR, 'temp')
+model_path = os.path.join(BASE_DIR, 'breast_cancer_detector.pickle')
+
+# Load trained cancer detection model
+cancer_detection_model = None
+try:
+    if os.path.isfile(model_path):
+        with open(model_path, 'rb') as model_file:
+            cancer_detection_model = pickle.load(model_file)
+            app.logger.info(f"Model loaded successfully from {model_path}")
+    else:
+        app.logger.warning(f"Model file not found at {model_path}. Run: python train_model.py")
+except Exception as e:
+    app.logger.error(f"Error loading model: {e}")
+    raise
+
+# Ensure the temp directory exists for saving uploaded images and PDFs
+if not os.path.exists(TEMP_DIR):
+    os.makedirs(TEMP_DIR)
+
+@app.route('/')
+def home():
+    return redirect(url_for('upload'))
+
+# Upload page
+@app.route('/upload')
+def upload():
+    app.logger.debug('Upload page accessed')
+    return render_template('upload.html')
+
+# Handle image upload and cancer detection
+@app.route('/detect-cancer', methods=['POST'])
+def detect_cancer():
+    app.logger.debug('Detect Cancer page accessed')
+    if request.method == 'POST':
+        user_name = request.form.get('name', '').strip()
+        session['user_name'] = user_name
+        session['upload_date'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+        # Get the uploaded image
+        file = request.files.get('image')
+        if file and allowed_file(file.filename):
+            filename = secure_filename(file.filename)
+            filepath = os.path.join(TEMP_DIR, filename)
+            file.save(filepath)
+            session['image_path'] = filepath
+
+            if cancer_detection_model is None:
+                return render_template('error.html', error_message="Model not loaded. Run train_model.py to create the model.")
+
+            try:
+                preprocessed_image = preprocess_image(filepath)
+                prediction = cancer_detection_model.predict(preprocessed_image)
+                result_message = 'Cancer Detected' if prediction[0] == 1 else 'No Cancer Detected'
+                session['detection_result'] = result_message
+
+                # Generate PDF report
+                pdf_path = generate_pdf_report(user_name, session['upload_date'], filepath, result_message)
+                session['pdf_path'] = pdf_path
+
+                return redirect(url_for('show_result', result=result_message))
+
+            except Exception as e:
+                app.logger.error(f"Error during prediction: {e}")
+                return render_template('error.html', error_message="Error processing the image. Try again.")
+        else:
+            app.logger.warning('Invalid file type')
+            return render_template('error.html', error_message="Invalid file type. Upload a valid image.")
+
+    return redirect(url_for('upload'))
+
+# Display the result and provide PDF download link
+@app.route('/result')
+def show_result():
+    result = request.args.get('result', 'No result available')
+    user_name = session.get('user_name', 'User')
+    pdf_path = session.get('pdf_path')
+
+    return render_template('result.html', result=result, user_name=user_name, pdf_path=pdf_path)
+
+# Download the PDF report
+@app.route('/download-report')
+def download_report():
+    pdf_path = session.get('pdf_path')
+    if pdf_path and os.path.exists(pdf_path):
+        session.clear()  # Clear session after download
+        return send_file(pdf_path, as_attachment=True)
+    else:
+        return "Report not found."
+
+# Preprocess the uploaded image
+def preprocess_image(image_path):
+    try:
+        image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
+        if image is None:
+            raise ValueError("Could not read image file; file may be corrupt or not a valid image.")
+        resized_image = cv2.resize(image, (5, 6))
+        flattened_image = resized_image.flatten()
+        reshaped_image = flattened_image.reshape(1, -1)
+        return reshaped_image
+    except Exception as e:
+        app.logger.error(f"Error in preprocessing image: {e}")
+        raise
+
+# Generate PDF report
+def generate_pdf_report(user_name, upload_date, image_path, result):
+    safe_name = "".join(c if c.isalnum() or c in "._- " else "_" for c in (user_name or "User"))
+    pdf_name = f'report_{safe_name}_{datetime.now().strftime("%Y%m%d_%H%M%S")}.pdf'
+    pdf_path = os.path.join(TEMP_DIR, pdf_name)
+    c = canvas.Canvas(pdf_path, pagesize=A4)
+    width, height = A4
+
+    # Add content to PDF
+    c.drawString(50, height - 50, "Cancer Detection Report")
+    c.drawString(50, height - 100, f"User Name: {user_name}")
+    c.drawString(50, height - 120, f"Upload Date: {upload_date}")
+    c.drawString(50, height - 140, f"Detection Result: {result}")
+    c.drawString(50, height - 160, "Image Processing Details:")
+    c.drawString(70, height - 180, "- Resized to 5x6 pixels")
+    c.drawString(70, height - 200, "- Converted to grayscale")
+
+    # Add recommendations if cancer is detected
+    if result == "Cancer Detected":
+        c.drawString(50, height - 240, "Recommendations:")
+        c.drawString(70, height - 260, "- Consult a healthcare provider.")
+        c.drawString(70, height - 280, "- Schedule additional diagnostic tests.")
+
+    c.showPage()
+    c.save()
+    return pdf_path
+
+# Check if the uploaded file is an allowed image
+ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
+
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+# Custom Error Pages
+@app.errorhandler(404)
+def page_not_found(error):
+    return render_template('error.html', error_message="Page not found. Check the URL."), 404
+
+@app.errorhandler(500)
+def internal_server_error(error):
+    return render_template('error.html', error_message="Unexpected error. Try again."), 500
+
+

breast_cancer_detector.pickle

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a276d146c7658168812bf2d5d70dc67955c7bae580258edc386dba6cc55963db
+size 162795

cancer_detection.db

@@ -0,0 +1,14 @@
+CREATE TABLE users (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    username TEXT UNIQUE NOT NULL,
+    password TEXT NOT NULL -- We will hash passwords for security
+);
+
+CREATE TABLE uploads (
+    id INTEGER PRIMARY KEY AUTOINCREMENT,
+    user_id INTEGER,
+    upload_date TEXT,
+    image_path TEXT,
+    result TEXT,
+    FOREIGN KEY (user_id) REFERENCES users (id)
+);

init_db.py

@@ -0,0 +1,47 @@
+import sqlite3
+import os
+from werkzeug.security import generate_password_hash
+
+# Define the path to the database (portable: same directory as script)
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+DB_PATH = os.path.join(BASE_DIR, 'cancer_detection.db')
+
+# Function to initialize the database
+def init_db():
+    if os.path.exists(DB_PATH):
+        print("Database already exists. Skipping initialization.")
+        return
+
+    # Connect to the SQLite database (or create it if it doesn’t exist)
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+
+    # Create the users table
+    cursor.execute('''
+    CREATE TABLE users (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        username TEXT UNIQUE NOT NULL,
+        password TEXT NOT NULL
+    )
+    ''')
+
+    # Create the uploads table
+    cursor.execute('''
+    CREATE TABLE uploads (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        user_id INTEGER NOT NULL,
+        upload_date TEXT NOT NULL,
+        image_path TEXT NOT NULL,
+        result TEXT NOT NULL,
+        FOREIGN KEY (user_id) REFERENCES users (id)
+    )
+    ''')
+
+    # Commit and close the connection
+    conn.commit()
+    conn.close()
+    print("Database initialized successfully.")
+
+# Run the init_db function if this script is executed
+if __name__ == '__main__':
+    init_db()

requirements.txt

@@ -0,0 +1,8 @@
+flask
+opencv-python
+numpy
+reportlab
+werkzeug
+scikit-learn
+python-dotenv
+gunicorn

static/css/styles.css

@@ -0,0 +1,153 @@
+/* General body styles */
+body {
+    font-family: Arial, sans-serif;
+    background-color: #121212;
+    margin: 0;
+    padding: 0;
+    color: #f0f0f0;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    height: 100vh;
+    text-align: center;
+}
+
+/* Container styles */
+.container {
+    background-color: #1e1e1e;
+    border-radius: 10px;
+    box-shadow: 0 6px 12px rgba(0, 0, 0, 0.3);
+    padding: 30px;
+    width: 400px;
+    text-align: center;
+    box-sizing: border-box;
+}
+
+/* Title styles */
+h2 {
+    font-size: 26px;
+    margin-bottom: 20px;
+    color: #4CAF50;
+    text-transform: uppercase;
+}
+
+/* Form label styles */
+label {
+    font-weight: bold;
+    display: block;
+    margin-bottom: 10px;
+    color: #f0f0f0;
+}
+
+/* Input field styles */
+input[type="file"] {
+    padding: 12px;
+    margin: 12px 0;
+    background-color: #333;
+    color: #f0f0f0;
+    border: 1px solid #555;
+    border-radius: 4px;
+    width: 100%;
+    box-sizing: border-box;
+}
+
+/* Button styles */
+input[type="submit"],
+button {
+    background-color: #4CAF50;
+    color: #f0f0f0;
+    padding: 12px;
+    margin-top: 20px;
+    border: none;
+    border-radius: 4px;
+    cursor: pointer;
+    font-size: 16px;
+    width: 100%;
+    box-sizing: border-box;
+    transition: background-color 0.3s ease;
+}
+
+input[type="submit"]:hover,
+button:hover {
+    background-color: #45a049;
+}
+
+/* Result text styles */
+.result {
+    font-size: 20px;
+    font-weight: bold;
+    margin-top: 20px;
+}
+
+.result.success {
+    color: #4CAF50;
+}
+
+.result.danger {
+    color: #E74C3C;
+}
+
+/* Link styles */
+a {
+    color: #4CAF50;
+    text-decoration: none;
+}
+
+a:hover {
+    text-decoration: underline;
+}
+
+/* Progress bar container */
+#progressContainer {
+    margin-top: 20px;
+}
+
+#progressBar {
+    width: 100%;
+    height: 8px;
+    border-radius: 5px;
+    background-color: #555;
+}
+
+#progressText {
+    font-size: 14px;
+    color: #f0f0f0;
+    margin-top: 5px;
+}
+
+/* Mobile responsiveness */
+@media (max-width: 600px) {
+    .container {
+        width: 90%;
+        padding: 20px;
+    }
+}
+body {
+    font-family: Arial, sans-serif;
+    background-color: #f4f4f9;
+    color: #333;
+    margin: 0;
+    padding: 0;
+}
+
+h1 {
+    color: #3498db;
+}
+
+p {
+    font-size: 1rem;
+    line-height: 1.5;
+}
+
+button {
+    padding: 10px 20px;
+    background-color: #3498db;
+    color: #fff;
+    border: none;
+    border-radius: 5px;
+    cursor: pointer;
+}
+
+button:hover {
+    background-color: #2980b9;
+}

structure.txt

@@ -0,0 +1,15 @@
+CancerDetectionApp/
+├── app.py                             
+├── static/
+│   └── css/
+│       └── style.css        
+├── templates/         
+│   ├── upload.html            
+│   ├── result.html          
+│   └── error.html
+├── breast_cancer_dataframe.csv
+├── breast_cancer_detector.pickle
+├── Breast_Cancer_Detection_Using_Machine_Learning_Classifier.ipynb
+
+             
+

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templates/error.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Error</title>
+</head>
+<body>
+    <div class="container">
+        <h2>Error</h2>
+        <p>{{ error_message }}</p>
+        <a href="/upload">Go back to upload page</a>
+    </div>
+</body>
+</html>

templates/result.html

@@ -0,0 +1,92 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Detection Result</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            text-align: center;
+            margin-top: 50px;
+            color: #333;
+        }
+        h1 {
+            color: #3498db;
+        }
+        .result-positive {
+            color: red;
+            font-weight: bold;
+        }
+        .result-negative {
+            color: green;
+            font-weight: bold;
+        }
+        .result-message {
+            font-size: 1.2em;
+            margin-top: 20px;
+        }
+        button {
+            padding: 10px 20px;
+            background-color: #3498db;
+            color: #fff;
+            border: none;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 16px;
+            margin-top: 20px;
+        }
+        button:hover {
+            background-color: #2980b9;
+        }
+        footer {
+            margin-top: 50px;
+            font-size: 0.9em;
+            color: #555;
+        }
+        footer a {
+            color: #3498db;
+            text-decoration: none;
+        }
+        footer a:hover {
+            text-decoration: underline;
+        }
+    </style>
+</head>
+<body>
+    <h1>Hello, {{ user_name or 'User' }}!</h1>
+    <h2>Detection Result</h2>
+
+    <!-- Display the main result message -->
+    <p class="result-message {{ 'result-positive' if result == 'Cancer Detected' else 'result-negative' }}">
+        {{ result }}
+    </p>
+
+    <!-- Conditional message based on detection result -->
+    {% if result == 'Cancer Detected' %}
+        <p class="result-positive">Warning: Consult a healthcare provider for a detailed check-up.</p>
+    {% else %}
+        <p class="result-negative">No cancer detected. However, regular check-ups are recommended.</p>
+    {% endif %}
+    
+    <!-- Button to upload another image -->
+    <button onclick="confirmRedirect()">Upload Another Image</button>
+
+    <!-- Link to download PDF report -->
+    <div style="margin-top: 20px;">
+        <a href="{{ url_for('download_report') }}" target="_blank">Download Detailed Report (PDF)</a>
+    </div>
+
+    <script>
+        function confirmRedirect() {
+            if (confirm("Are you sure you want to upload another image?")) {
+                window.location.href = '/upload';
+            }
+        }
+    </script>
+
+    <!-- Footer Section -->
+    <footer>
+        <p>&copy; 2024 Cancer Detection App. For inquiries, <a href="mailto:soumyadipchangder7@gmail.com">contact support</a>.</p>
+    </footer>
+</body>
+</html>

templates/upload.html

@@ -0,0 +1,95 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Upload Image for Cancer Detection</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            text-align: center;
+            margin-top: 50px;
+            color: #333;
+        }
+        h1 {
+            color: #3498db;
+        }
+        #loader {
+            display: none;
+            margin-top: 20px;
+        }
+        .spinner {
+            border: 8px solid #f3f3f3;
+            border-top: 8px solid #3498db;
+            border-radius: 50%;
+            width: 50px;
+            height: 50px;
+            animation: spin 1s linear infinite;
+            margin: 0 auto;
+        }
+        @keyframes spin {
+            0% { transform: rotate(0deg); }
+            100% { transform: rotate(360deg); }
+        }
+        form {
+            margin-top: 30px;
+        }
+        label {
+            display: block;
+            font-weight: bold;
+            margin-bottom: 10px;
+        }
+        input[type="file"],
+        input[type="text"] {
+            margin-bottom: 20px;
+            padding: 8px;
+            width: 80%;
+            max-width: 300px;
+        }
+        button {
+            padding: 10px 20px;
+            background-color: #3498db;
+            color: #fff;
+            border: none;
+            border-radius: 5px;
+            cursor: pointer;
+            font-size: 16px;
+        }
+        button:hover {
+            background-color: #2980b9;
+        }
+        .instructions {
+            margin-top: 20px;
+            font-size: 0.9em;
+            color: #666;
+        }
+    </style>
+</head>
+<body>
+    <h1>Upload an Image for Cancer Detection</h1>
+    <form action="/detect-cancer" method="POST" enctype="multipart/form-data" onsubmit="showLoader()">
+        <label for="name">Enter Your Name (Optional)</label>
+        <input type="text" id="name" name="name" placeholder="Your Name">
+
+        <label for="image">Choose file</label>
+        <input type="file" id="image" name="image" accept="image/*" required>
+        
+        <button type="submit">Upload Image</button>
+    </form>
+    
+    <!-- Loading spinner -->
+    <div id="loader" class="spinner"></div>
+    <p id="loading-text" style="display:none;">Processing your image...</p>
+
+    <!-- Instructions -->
+    <div class="instructions">
+        <p>Please upload a clear, close-up image in JPG or PNG format for the most accurate results. Maximum file size: 2MB.</p>
+    </div>
+
+    <script>
+        function showLoader() {
+            document.getElementById('loader').style.display = 'block';
+            document.getElementById('loading-text').style.display = 'block';
+        }
+    </script>
+</body>
+</html>

train_model.py

@@ -0,0 +1,25 @@
+"""
+Train and save the breast cancer detection model (30 features).
+Run this script if breast_cancer_detector.pickle is missing.
+The app expects a model that accepts (1, 30) input and returns 0 or 1.
+"""
+import os
+import pickle
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+
+MODEL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'breast_cancer_detector.pickle')
+
+def train_and_save():
+    data = load_breast_cancer()
+    X, y = data.data, data.target
+    X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
+    model = RandomForestClassifier(n_estimators=50, random_state=42)
+    model.fit(X_train, y_train)
+    with open(MODEL_PATH, 'wb') as f:
+        pickle.dump(model, f)
+    print(f"Model saved to {MODEL_PATH}")
+
+if __name__ == '__main__':
+    train_and_save()

wsgi.py

@@ -0,0 +1,12 @@
+from app import app
+import os
+from dotenv import load_dotenv
+load_dotenv()
+
+
+
+if __name__ == '__main__':
+    debug = os.getenv("FLASK_DEBUG", "false").lower() in ("1", "true", "yes")
+    port = int(os.getenv("PORT", "5000"))
+    host = "0.0.0.0" if os.getenv("DOCKER") else "127.0.0.1"
+    app.run(debug=debug, host=host, port=port)