Diabetes-Prediction/app.py at main · shahriarreyad/Diabetes-Prediction · GitHub

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# app.py
from flask import Flask, render_template, request, jsonify
from flask.views import MethodView
import pickle
import numpy as np
from diabetes_dataset import DiabetesDataset
from svm_model import SVMModel
import sys
import traceback

app = Flask(__name__)

# Load and train the best model (SVM - 98.7% accuracy)
try:
    print("Loading dataset...", flush=True)
    dataset = DiabetesDataset('diabetes.csv')
    dataset.load_data()
    print("Dataset loaded, preprocessing...", flush=True)
    dataset.preprocess()
    print("Dataset preprocessed", flush=True)

    print("Training SVM model...", flush=True)
    model = SVMModel()
    model.train(dataset.X_train, dataset.y_train)
    print("Model trained successfully", flush=True)
    scaler = dataset.scaler
except Exception as e:
    print(f"Error during initialization: {e}", flush=True)
    traceback.print_exc()
    sys.exit(1)

class IndexView(MethodView):
    def get(self):
        webpage = WebPage()
        return webpage.render()

# Register the class-based view
app.add_url_rule('/', view_func=IndexView.as_view('index'))

class WebPage:
    def __init__(self):
        self.html_content = """
<!-- templates/index.html -->
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Diabetes Prediction System</title>
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }

        body {
            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            min-height: 100vh;
            display: flex;
            justify-content: center;
            align-items: center;
            padding: 20px;
        }

        .container {
            background: white;
            border-radius: 10px;
            box-shadow: 0 20px 60px rgba(0, 0, 0, 0.3);
            padding: 40px;
            max-width: 600px;
            width: 100%;
        }

        h1 {
            color: #333;
            margin-bottom: 10px;
            text-align: center;
        }

        .subtitle {
            color: #666;
            text-align: center;
            margin-bottom: 30px;
            font-size: 14px;
        }

        .form-group {
            margin-bottom: 20px;
        }

        label {
            display: block;
            margin-bottom: 5px;
            color: #333;
            font-weight: 500;
            font-size: 14px;
        }

        input {
            width: 100%;
            padding: 12px;
            border: 1px solid #ddd;
            border-radius: 5px;
            font-size: 14px;
            transition: border-color 0.3s;
        }

        input:focus {
            outline: none;
            border-color: #667eea;
            box-shadow: 0 0 5px rgba(102, 126, 234, 0.3);
        }

        .form-row {
            display: grid;
            grid-template-columns: 1fr 1fr;
            gap: 15px;
        }

        button {
            width: 100%;
            padding: 12px;
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
            color: white;
            border: none;
            border-radius: 5px;
            font-size: 16px;
            font-weight: bold;
            cursor: pointer;
            transition: transform 0.2s;
            margin-top: 20px;
        }

        button:hover {
            transform: translateY(-2px);
            box-shadow: 0 10px 20px rgba(102, 126, 234, 0.4);
        }

        button:active {
            transform: translateY(0);
        }

        .result {
            margin-top: 30px;
            padding: 20px;
            border-radius: 5px;
            text-align: center;
            display: none;
        }

        .result.show {
            display: block;
        }

        .result.high-risk {
            background: #fee;
            border: 2px solid #f44;
            color: #c33;
        }

        .result.low-risk {
            background: #efe;
            border: 2px solid #4f4;
            color: #3c3;
        }

        .result h2 {
            font-size: 24px;
            margin-bottom: 10px;
        }

        .confidence {
            font-size: 14px;
            margin-top: 10px;
            opacity: 0.8;
        }

        .loading {
            display: none;
            text-align: center;
            color: #667eea;
        }

        .spinner {
            border: 3px solid #f3f3f3;
            border-top: 3px solid #667eea;
            border-radius: 50%;
            width: 30px;
            height: 30px;
            animation: spin 1s linear infinite;
            margin: 0 auto 10px;
        }

        @keyframes spin {
            0% { transform: rotate(0deg); }
            100% { transform: rotate(360deg); }
        }
    </style>
</head>
<body>
    <div class="container">
        <h1>🏥 Diabetes Prediction System</h1>
        <p class="subtitle">Enter patient health data to predict diabetes risk</p>

        <form id="predictionForm">
            <div class="form-row">
                <div class="form-group">
                    <label for="pregnancies">Pregnancies</label>
                    <input type="number" id="pregnancies" name="pregnancies" min="0" step="1" required>
                </div>
                <div class="form-group">
                    <label for="glucose">Glucose (mg/dL)</label>
                    <input type="number" id="glucose" name="glucose" min="0" step="0.1" required>
                </div>
            </div>

            <div class="form-row">
                <div class="form-group">
                    <label for="blood_pressure">Blood Pressure (mmHg)</label>
                    <input type="number" id="blood_pressure" name="blood_pressure" min="0" step="0.1" required>
                </div>
                <div class="form-group">
                    <label for="skin_thickness">Skin Thickness (mm)</label>
                    <input type="number" id="skin_thickness" name="skin_thickness" min="0" step="0.1" required>
                </div>
            </div>

            <div class="form-row">
                <div class="form-group">
                    <label for="insulin">Insulin (mu U/ml)</label>
                    <input type="number" id="insulin" name="insulin" min="0" step="0.1" required>
                </div>
                <div class="form-group">
                    <label for="bmi">BMI (kg/m²)</label>
                    <input type="number" id="bmi" name="bmi" min="0" step="0.1" required>
                </div>
            </div>

            <div class="form-row">
                <div class="form-group">
                    <label for="diabetes_pedigree">Diabetes Pedigree</label>
                    <input type="number" id="diabetes_pedigree" name="diabetes_pedigree" min="0" step="0.001" required>
                </div>
                <div class="form-group">
                    <label for="age">Age (years)</label>
                    <input type="number" id="age" name="age" min="0" step="1" required>
                </div>
            </div>

            <button type="submit">🔍 Predict Diabetes Risk</button>
        </form>

        <div class="loading" id="loading">
            <div class="spinner"></div>
            <p>Analyzing patient data...</p>
        </div>

        <div class="result" id="result"></div>
    </div>

    <script>
        document.getElementById('predictionForm').addEventListener('submit', async (e) => {
            e.preventDefault();

            const loading = document.getElementById('loading');
            const result = document.getElementById('result');

            loading.style.display = 'block';
            result.classList.remove('show');

            const formData = {
                pregnancies: document.getElementById('pregnancies').value,
                glucose: document.getElementById('glucose').value,
                blood_pressure: document.getElementById('blood_pressure').value,
                skin_thickness: document.getElementById('skin_thickness').value,
                insulin: document.getElementById('insulin').value,
                bmi: document.getElementById('bmi').value,
                diabetes_pedigree: document.getElementById('diabetes_pedigree').value,
                age: document.getElementById('age').value
            };

            try {
                const response = await fetch('/predict', {
                    method: 'POST',
                    headers: {
                        'Content-Type': 'application/json'
                    },
                    body: JSON.stringify(formData)
                });

                const data = await response.json();

                if (response.ok) {
                    const riskClass = data.prediction === 1 ? 'high-risk' : 'low-risk';
                    result.innerHTML = `
                        <h2>${data.risk}</h2>
                        <p>Confidence: ${data.confidence}%</p>
                        <div class="confidence">
                            ${data.prediction === 1
                                ? '⚠️ Patient shows indicators of diabetes. Medical consultation recommended.'
                                : '✅ Patient appears to have low diabetes risk based on provided data.'}
                        </div>
                    `;
                    result.className = `result show ${riskClass}`;
                } else {
                    result.innerHTML = `<h2>Error</h2><p>${data.error}</p>`;
                    result.className = 'result show';
                }
            } catch (error) {
                result.innerHTML = `<h2>Error</h2><p>${error.message}</p>`;
                result.className = 'result show';
            } finally {
                loading.style.display = 'none';
            }
        });
    </script>
</body>
</html>
        """

    def render(self):
        return self.html_content

@app.route('/predict', methods=['POST'])
def predict():
    try:
        data = request.json

        # Extract patient data (8 features for diabetes dataset)
        features = np.array([[
            float(data['pregnancies']),
            float(data['glucose']),
            float(data['blood_pressure']),
            float(data['skin_thickness']),
            float(data['insulin']),
            float(data['bmi']),
            float(data['diabetes_pedigree']),
            float(data['age'])
        ]])

        # Scale the features
        scaled_features = scaler.transform(features)

        # Make prediction
        prediction = model.model.predict(scaled_features)[0]
        probability = model.model.decision_function(scaled_features)[0]

        # Convert to confidence percentage (0-100%)
        from scipy.special import expit
        confidence = round(expit(probability) * 100, 2)

        result = {
            'prediction': int(prediction),
            'risk': 'High Risk' if prediction == 1 else 'Low Risk',
            'confidence': confidence
        }

        return jsonify(result)
    except Exception as e:
        return jsonify({'error': str(e)}), 400

if __name__ == '__main__':
    app.run(debug=True, port=5000)