Upload 17 files

Dockerfile

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+FROM python:3.9-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

Procfile

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+web: gunicorn app:app

app.py

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+import json
+import os
+import subprocess
+import threading
+import time
+import numpy as np
+import tensorflow as tf
+from flask import Flask, render_template, jsonify, request
+from werkzeug.utils import secure_filename
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'uploads'
+os.makedirs('uploads', exist_ok=True)
+
+training_status = {
+    "running": False,
+    "finished": False
+}
+
+
+def run_training(file_path):
+    training_status["running"] = True
+    training_status["finished"] = False
+
+    with open("training_results.json", "w") as f:
+        json.dump([], f)
+
+    server = subprocess.Popen(["python3", "server.py", file_path])
+    time.sleep(3)
+    client0 = subprocess.Popen(["python3", "client.py", "0", file_path])
+    client1 = subprocess.Popen(["python3", "client.py", "1", file_path])
+
+    server.wait()
+    client0.wait()
+    client1.wait()
+
+    training_status["running"] = False
+    training_status["finished"] = True
+
+
+@app.route("/")
+def index():
+    return render_template("landing.html")
+
+
+@app.route("/dashboard")
+def dashboard():
+    return render_template("index.html")
+
+
+@app.route("/predict")
+def predict():
+    return render_template("predict.html")
+
+
+@app.route("/predict_result", methods=["POST"])
+def predict_result():
+    diagnosis_type = request.form.get("diagnosis_type", "diabetes")
+
+    if diagnosis_type == "diabetes":
+        features = [
+            float(request.form.get("pregnancies", 0)),
+            float(request.form.get("glucose", 0)),
+            float(request.form.get("blood_pressure", 0)),
+            float(request.form.get("skin_thickness", 0)),
+            float(request.form.get("insulin", 0)),
+            float(request.form.get("bmi", 0)),
+            float(request.form.get("diabetes_pedigree", 0)),
+            float(request.form.get("age", 0)),
+        ]
+        model_path = "diabetes_model.keras"
+        disease_name = "Diabetes"
+
+    elif diagnosis_type == "heart":
+        features = [
+            float(request.form.get("age", 0)),
+            float(request.form.get("sex", 0)),
+            float(request.form.get("cp", 0)),
+            float(request.form.get("trestbps", 0)),
+            float(request.form.get("chol", 0)),
+            float(request.form.get("fbs", 0)),
+            float(request.form.get("restecg", 0)),
+            float(request.form.get("thalach", 0)),
+            float(request.form.get("exang", 0)),
+            float(request.form.get("oldpeak", 0)),
+            float(request.form.get("slope", 0)),
+            float(request.form.get("ca", 0)),
+            float(request.form.get("thal", 0)),
+        ]
+        model_path = "heart_model.keras"
+        disease_name = "Heart Disease"
+
+    else:
+        return jsonify({"error": "Invalid diagnosis type"})
+
+    X = np.array([features])
+    model = tf.keras.models.load_model(model_path)
+    prediction = model.predict(X)[0][0]
+
+    risk = "High Risk" if prediction > 0.5 else "Low Risk"
+    confidence = round(float(prediction) * 100, 1) if prediction > 0.5 else round((1 - float(prediction)) * 100, 1)
+
+    return jsonify({
+        "risk": risk,
+        "confidence": confidence,
+        "probability": round(float(prediction) * 100, 1),
+        "disease": disease_name
+    })
+
+
+@app.route("/start_training", methods=["POST"])
+def start_training():
+    if training_status["running"]:
+        return jsonify({"status": "already running"})
+
+    file_path = "diabetes.csv"
+
+    if "file" in request.files and request.files["file"].filename != "":
+        file = request.files["file"]
+        filename = secure_filename(file.filename)
+        file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        file.save(file_path)
+
+    thread = threading.Thread(target=run_training, args=(file_path,))
+    thread.start()
+    return jsonify({"status": "started"})
+
+
+@app.route("/status")
+def status():
+    results = []
+    if os.path.exists("training_results.json"):
+        with open("training_results.json", "r") as f:
+            results = json.load(f)
+    return jsonify({
+        "running": training_status["running"],
+        "finished": training_status["finished"],
+        "rounds": results
+    })
+
+
+if __name__ == "__main__":
+    app.run(debug=False, port=8000)

client.py

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+import flwr as fl
+import tensorflow as tf
+import numpy as np
+import pandas as pd
+from model import create_model
+
+def load_data(file_path, client_id):
+    data = pd.read_csv(file_path)
+    # last column is the target
+    X = data.iloc[:, :-1].values
+    y = data.iloc[:, -1].values
+    X = (X - X.mean(axis=0)) / (X.std(axis=0) + 1e-8)
+    split = int(len(X) * 0.8)
+    X_train, X_test = X[:split], X[split:]
+    y_train, y_test = y[:split], y[split:]
+    size = len(X_train) // 2
+    return (
+        X_train[client_id * size:(client_id + 1) * size],
+        y_train[client_id * size:(client_id + 1) * size],
+        X_test,
+        y_test
+    )
+
+class HospitalClient(fl.client.NumPyClient):
+
+    def __init__(self, client_id, file_path):
+        self.x_train, self.y_train, self.x_test, self.y_test = load_data(file_path, client_id)
+        self.model = create_model(input_shape=self.x_train.shape[1])
+
+    def get_parameters(self, config):
+        return self.model.get_weights()
+
+    def fit(self, parameters, config):
+        self.model.set_weights(parameters)
+        self.model.fit(self.x_train, self.y_train, epochs=5, verbose=0)
+        return self.model.get_weights(), len(self.x_train), {}
+
+    def evaluate(self, parameters, config):
+        self.model.set_weights(parameters)
+        loss, accuracy = self.model.evaluate(self.x_test, self.y_test, verbose=0)
+        return loss, len(self.x_test), {"accuracy": accuracy}
+
+if __name__ == "__main__":
+    import sys
+    client_id = int(sys.argv[1]) if len(sys.argv) > 1 else 0
+    file_path = sys.argv[2] if len(sys.argv) > 2 else "diabetes.csv"
+    fl.client.start_numpy_client(
+        server_address="localhost:8080",
+        client=HospitalClient(client_id, file_path)
+    )

diabetes.csv

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+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
+8,125,96,0,0,0,0.232,54,1
+4,110,92,0,0,37.6,0.191,30,0
+10,168,74,0,0,38,0.537,34,1
+10,139,80,0,0,27.1,1.441,57,0
+1,189,60,23,846,30.1,0.398,59,1
+5,166,72,19,175,25.8,0.587,51,1
+7,100,0,0,0,30,0.484,32,1
+0,118,84,47,230,45.8,0.551,31,1
+7,107,74,0,0,29.6,0.254,31,1
+1,103,30,38,83,43.3,0.183,33,0
+1,115,70,30,96,34.6,0.529,32,1
+3,126,88,41,235,39.3,0.704,27,0
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+9,119,80,35,0,29,0.263,29,1
+11,143,94,33,146,36.6,0.254,51,1
+10,125,70,26,115,31.1,0.205,41,1
+7,147,76,0,0,39.4,0.257,43,1
+1,97,66,15,140,23.2,0.487,22,0
+13,145,82,19,110,22.2,0.245,57,0
+5,117,92,0,0,34.1,0.337,38,0
+5,109,75,26,0,36,0.546,60,0
+3,158,76,36,245,31.6,0.851,28,1
+3,88,58,11,54,24.8,0.267,22,0
+6,92,92,0,0,19.9,0.188,28,0
+10,122,78,31,0,27.6,0.512,45,0
+4,103,60,33,192,24,0.966,33,0
+11,138,76,0,0,33.2,0.42,35,0
+9,102,76,37,0,32.9,0.665,46,1
+2,90,68,42,0,38.2,0.503,27,1
+4,111,72,47,207,37.1,1.39,56,1
+3,180,64,25,70,34,0.271,26,0
+7,133,84,0,0,40.2,0.696,37,0
+7,106,92,18,0,22.7,0.235,48,0
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+0,180,66,39,0,42,1.893,25,1
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model.py

@@ -0,0 +1,16 @@
+import tensorflow as tf
+
+def create_model(input_shape=8):
+    # neural network for binary classification
+    model = tf.keras.Sequential([
+        tf.keras.layers.Input(shape=(input_shape,)),
+        tf.keras.layers.Dense(64, activation='relu'),
+        tf.keras.layers.Dense(32, activation='relu'),
+        tf.keras.layers.Dense(1, activation='sigmoid')
+    ])
+    model.compile(
+        optimizer='adam',
+        loss='binary_crossentropy',
+        metrics=['accuracy']
+    )
+    return model

requirements.txt

@@ -0,0 +1,8 @@
+flask==3.1.2
+flwr==1.23.0
+tensorflow==2.19.1
+numpy==2.0.2
+matplotlib==3.9.4
+pandas==2.3.3
+werkzeug==3.1.5
+gunicorn

results.py

@@ -0,0 +1,29 @@
+import matplotlib.pyplot as plt
+
+rounds = [0, 1, 2, 3]
+loss = [2.29585599899292, 0.6346262693405151, 0.4165417551994324, 0.34342074394226074]
+accuracy = [0.12729999423027039, 0.826200008392334, 0.8719000220298767, 0.8988999724388123]
+
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
+
+# loss chart
+ax1.plot(rounds, loss, marker='o', color='steelblue', linewidth=2)
+ax1.set_title('Loss per Round')
+ax1.set_xlabel('Round')
+ax1.set_ylabel('Loss')
+ax1.set_xticks(rounds)
+ax1.grid(True)
+
+# accuracy chart
+ax2.plot(rounds, accuracy, marker='o', color='green', linewidth=2)
+ax2.set_title('Accuracy per Round')
+ax2.set_xlabel('Round')
+ax2.set_ylabel('Accuracy')
+ax2.set_xticks(rounds)
+ax2.grid(True)
+
+plt.suptitle('PrivaMed - Federated Learning Results')
+plt.tight_layout()
+plt.savefig('results.png')
+plt.show()
+print("Chart saved as results.png")

server.py

@@ -0,0 +1,73 @@
+import flwr as fl
+import numpy as np
+import json
+import pandas as pd
+from model import create_model
+import sys
+
+results_log = []
+
+
+def get_evaluate_fn(file_path):
+    data = pd.read_csv(file_path)
+    X = data.iloc[:, :-1].values
+    y = data.iloc[:, -1].values
+    X = (X - X.mean(axis=0)) / (X.std(axis=0) + 1e-8)
+    split = int(len(X) * 0.8)
+    X_test, y_test = X[split:], y[split:]
+    input_shape = X.shape[1]
+
+    # determine model name from file
+    if "diabetes" in file_path:
+        model_name = "diabetes_model.keras"
+    elif "heart" in file_path:
+        model_name = "heart_model.keras"
+    else:
+        model_name = "global_model.keras"
+
+    def evaluate(server_round, parameters, config):
+        model = create_model(input_shape=input_shape)
+        model.set_weights(parameters)
+        loss, accuracy = model.evaluate(X_test, y_test, verbose=0)
+
+        results_log.append({
+            "round": server_round,
+            "loss": round(loss, 4),
+            "accuracy": round(accuracy, 4)
+        })
+        with open("training_results.json", "w") as f:
+            json.dump(results_log, f)
+
+        if server_round == 3:
+            model.save(model_name)
+            print(f"Model saved as {model_name}")
+
+        return loss, {"accuracy": accuracy}
+
+    return evaluate
+
+
+def main(file_path="diabetes.csv"):
+    global results_log
+    results_log = []
+
+    with open("training_results.json", "w") as f:
+        json.dump([], f)
+
+    strategy = fl.server.strategy.FedAvg(
+        min_fit_clients=2,
+        min_evaluate_clients=2,
+        min_available_clients=2,
+        evaluate_fn=get_evaluate_fn(file_path),
+    )
+
+    fl.server.start_server(
+        server_address="localhost:8080",
+        config=fl.server.ServerConfig(num_rounds=3),
+        strategy=strategy,
+    )
+
+
+if __name__ == "__main__":
+    file_path = sys.argv[1] if len(sys.argv) > 1 else "diabetes.csv"
+    main(file_path)

templates/index.html

@@ -0,0 +1,205 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>PrivaMed - Dashboard</title>
+    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+    <style>
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+        body { font-family: 'Segoe UI', sans-serif; background: #0f172a; color: #e2e8f0; }
+        header { background: #1e293b; padding: 20px 40px; border-bottom: 1px solid #334155; display: flex; justify-content: space-between; align-items: center; }
+        header h1 { color: #38bdf8; font-size: 24px; }
+        header div { display: flex; gap: 16px; }
+        header a { color: #94a3b8; font-size: 14px; text-decoration: none; }
+        header a:hover { color: #38bdf8; }
+        .container { max-width: 1100px; margin: 40px auto; padding: 0 20px; }
+        .cards { display: grid; grid-template-columns: repeat(3, 1fr); gap: 20px; margin-bottom: 30px; }
+        .card { background: #1e293b; border-radius: 12px; padding: 24px; border: 1px solid #334155; }
+        .card h3 { color: #94a3b8; font-size: 13px; margin-bottom: 8px; }
+        .card .value { font-size: 28px; font-weight: bold; color: #38bdf8; }
+        .hospitals { display: grid; grid-template-columns: repeat(2, 1fr); gap: 20px; margin-bottom: 30px; }
+        .hospital { background: #1e293b; border-radius: 12px; padding: 20px; border: 1px solid #334155; }
+        .hospital h3 { font-size: 15px; margin-bottom: 8px; }
+        .badge { display: inline-block; padding: 4px 10px; border-radius: 20px; font-size: 12px; }
+        .badge.idle { background: #334155; color: #94a3b8; }
+        .badge.training { background: #0c4a6e; color: #38bdf8; }
+        .badge.done { background: #064e3b; color: #34d399; }
+        .btn { background: #38bdf8; color: #0f172a; border: none; padding: 14px 32px; border-radius: 8px; font-size: 15px; font-weight: bold; cursor: pointer; }
+        .btn:hover { background: #7dd3fc; }
+        .btn:disabled { background: #334155; color: #64748b; cursor: not-allowed; }
+        .upload-box { background: #1e293b; border-radius: 12px; padding: 24px; border: 2px dashed #334155; margin-bottom: 30px; text-align: center; }
+        .upload-box h3 { color: #94a3b8; font-size: 14px; margin-bottom: 12px; }
+        .upload-box input { display: none; }
+        .upload-label { display: inline-block; padding: 10px 24px; background: #334155; border-radius: 8px; cursor: pointer; font-size: 14px; color: #e2e8f0; }
+        .upload-label:hover { background: #475569; }
+        .file-name { margin-top: 10px; font-size: 13px; color: #38bdf8; }
+        .rounds { background: #1e293b; border-radius: 12px; padding: 24px; border: 1px solid #334155; margin-bottom: 30px; }
+        .rounds h2 { margin-bottom: 16px; font-size: 16px; color: #94a3b8; }
+        .round-row { display: flex; justify-content: space-between; padding: 12px 0; border-bottom: 1px solid #334155; font-size: 14px; }
+        .round-row:last-child { border-bottom: none; }
+        .status-bar { background: #1e293b; border-radius: 12px; padding: 20px 24px; border: 1px solid #334155; margin-bottom: 30px; display: flex; align-items: center; justify-content: space-between; }
+        .dot { width: 10px; height: 10px; border-radius: 50%; background: #334155; margin-right: 10px; display: inline-block; }
+        .dot.active { background: #38bdf8; animation: pulse 1s infinite; }
+        @keyframes pulse { 0%, 100% { opacity: 1; } 50% { opacity: 0.3; } }
+        .charts { display: grid; grid-template-columns: repeat(2, 1fr); gap: 20px; margin-bottom: 30px; }
+        .chart-box { background: #1e293b; border-radius: 12px; padding: 24px; border: 1px solid #334155; }
+        .chart-box h3 { color: #94a3b8; font-size: 14px; margin-bottom: 16px; }
+    </style>
+</head>
+<body>
+    <header>
+        <h1>PrivaMed</h1>
+        <div>
+            <a href="/predict">Predict Risk</a>
+            <a href="/">Back to Home</a>
+        </div>
+    </header>
+    <div class="container">
+        <div class="cards">
+            <div class="card">
+                <h3>HOSPITALS CONNECTED</h3>
+                <div class="value">2</div>
+            </div>
+            <div class="card">
+                <h3>BEST ACCURACY</h3>
+                <div class="value" id="bestAccuracy">—</div>
+            </div>
+            <div class="card">
+                <h3>ROUNDS COMPLETED</h3>
+                <div class="value" id="roundsCount">0</div>
+            </div>
+        </div>
+        <div class="upload-box">
+            <h3>Upload your hospital dataset (CSV) or use default Diabetes dataset</h3>
+            <label class="upload-label" for="csvFile">Choose CSV File</label>
+            <input type="file" id="csvFile" accept=".csv" onchange="showFileName()">
+            <div class="file-name" id="fileName">Using default: diabetes.csv</div>
+        </div>
+        <div class="status-bar">
+            <div style="display:flex; align-items:center;">
+                <span class="dot" id="statusDot"></span>
+                <span id="statusText" style="margin-left:10px;">Ready to train</span>
+            </div>
+            <button class="btn" id="trainBtn" onclick="startTraining()">Start Federated Training</button>
+        </div>
+        <div class="hospitals">
+            <div class="hospital">
+                <h3>Hospital A</h3>
+                <p style="color:#64748b; font-size:13px; margin-bottom:10px;">Local data — never shared</p>
+                <span class="badge idle" id="h0badge">Idle</span>
+            </div>
+            <div class="hospital">
+                <h3>Hospital B</h3>
+                <p style="color:#64748b; font-size:13px; margin-bottom:10px;">Local data — never shared</p>
+                <span class="badge idle" id="h1badge">Idle</span>
+            </div>
+        </div>
+        <div class="charts">
+            <div class="chart-box">
+                <h3>Loss per Round</h3>
+                <canvas id="lossChart"></canvas>
+            </div>
+            <div class="chart-box">
+                <h3>Accuracy per Round</h3>
+                <canvas id="accuracyChart"></canvas>
+            </div>
+        </div>
+        <div class="rounds">
+            <h2>Training Rounds</h2>
+            <div id="roundsTable">
+                <p style="color:#475569; font-size:14px;">No training data yet.</p>
+            </div>
+        </div>
+    </div>
+    <script>
+        let polling = null;
+        let lossChart = null;
+        let accuracyChart = null;
+
+        function showFileName() {
+            const file = document.getElementById('csvFile').files[0];
+            if (file) document.getElementById('fileName').textContent = 'Selected: ' + file.name;
+        }
+
+        function initCharts() {
+            lossChart = new Chart(document.getElementById('lossChart'), {
+                type: 'line',
+                data: { labels: [], datasets: [{ label: 'Loss', data: [], borderColor: '#38bdf8', backgroundColor: 'rgba(56,189,248,0.1)', tension: 0.3, fill: true }] },
+                options: { plugins: { legend: { display: false } }, scales: { x: { ticks: { color: '#94a3b8' }, grid: { color: '#1e293b' } }, y: { ticks: { color: '#94a3b8' }, grid: { color: '#334155' } } } }
+            });
+            accuracyChart = new Chart(document.getElementById('accuracyChart'), {
+                type: 'line',
+                data: { labels: [], datasets: [{ label: 'Accuracy', data: [], borderColor: '#34d399', backgroundColor: 'rgba(52,211,153,0.1)', tension: 0.3, fill: true }] },
+                options: { plugins: { legend: { display: false } }, scales: { x: { ticks: { color: '#94a3b8' }, grid: { color: '#1e293b' } }, y: { ticks: { color: '#94a3b8' }, grid: { color: '#334155' } } } }
+            });
+        }
+
+        function startTraining() {
+            const formData = new FormData();
+            const fileInput = document.getElementById('csvFile');
+            if (fileInput.files.length > 0) formData.append('file', fileInput.files[0]);
+            fetch('/start_training', { method: 'POST', body: formData }).then(() => {
+                document.getElementById('trainBtn').disabled = true;
+                document.getElementById('trainBtn').textContent = 'Training...';
+                document.getElementById('statusDot').classList.add('active');
+                document.getElementById('statusText').textContent = 'Training in progress...';
+                document.getElementById('h0badge').className = 'badge training';
+                document.getElementById('h0badge').textContent = 'Training';
+                document.getElementById('h1badge').className = 'badge training';
+                document.getElementById('h1badge').textContent = 'Training';
+                document.getElementById('roundsTable').innerHTML = '';
+                lossChart.data.labels = []; lossChart.data.datasets[0].data = []; lossChart.update();
+                accuracyChart.data.labels = []; accuracyChart.data.datasets[0].data = []; accuracyChart.update();
+                polling = setInterval(pollStatus, 3000);
+            });
+        }
+
+        function pollStatus() {
+            fetch('/status').then(r => r.json()).then(data => {
+                if (data.rounds.length > 0) {
+                    updateRoundsTable(data.rounds);
+                    updateCharts(data.rounds);
+                    const last = data.rounds[data.rounds.length - 1];
+                    document.getElementById('roundsCount').textContent = last.round;
+                    document.getElementById('bestAccuracy').textContent = (last.accuracy * 100).toFixed(1) + '%';
+                }
+                if (data.finished) {
+                    clearInterval(polling);
+                    document.getElementById('statusDot').classList.remove('active');
+                    document.getElementById('statusText').textContent = 'Training complete';
+                    document.getElementById('h0badge').className = 'badge done';
+                    document.getElementById('h0badge').textContent = 'Done';
+                    document.getElementById('h1badge').className = 'badge done';
+                    document.getElementById('h1badge').textContent = 'Done';
+                    document.getElementById('trainBtn').disabled = false;
+                    document.getElementById('trainBtn').textContent = 'Train Again';
+                }
+            });
+        }
+
+        function updateCharts(rounds) {
+            const labels = rounds.map(r => 'Round ' + r.round);
+            lossChart.data.labels = labels;
+            lossChart.data.datasets[0].data = rounds.map(r => r.loss);
+            lossChart.update();
+            accuracyChart.data.labels = labels;
+            accuracyChart.data.datasets[0].data = rounds.map(r => (r.accuracy * 100).toFixed(1));
+            accuracyChart.update();
+        }
+
+        function updateRoundsTable(rounds) {
+            const table = document.getElementById('roundsTable');
+            table.innerHTML = '';
+            rounds.forEach(r => {
+                const row = document.createElement('div');
+                row.className = 'round-row';
+                row.innerHTML = `<span>Round ${r.round}</span><span>Loss: ${r.loss}</span><span style="color:#34d399">Accuracy: ${(r.accuracy * 100).toFixed(1)}%</span>`;
+                table.appendChild(row);
+            });
+        }
+
+        initCharts();
+    </script>
+</body>
+</html>

templates/landing.html

@@ -0,0 +1,115 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>PrivaMed - Federated Learning for Hospitals</title>
+    <style>
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+        body { font-family: 'Segoe UI', sans-serif; background: #0f172a; color: #e2e8f0; }
+        nav { background: #1e293b; padding: 20px 60px; display: flex; justify-content: space-between; align-items: center; border-bottom: 1px solid #334155; }
+        nav h1 { color: #38bdf8; font-size: 22px; }
+        nav div { display: flex; gap: 10px; }
+        nav a { color: #0f172a; background: #38bdf8; padding: 10px 24px; border-radius: 8px; font-weight: bold; font-size: 14px; text-decoration: none; }
+        nav a.secondary { background: #334155; color: #e2e8f0; }
+        .hero { text-align: center; padding: 100px 20px 80px; }
+        .hero h2 { font-size: 48px; font-weight: bold; line-height: 1.2; margin-bottom: 20px; }
+        .hero h2 span { color: #38bdf8; }
+        .hero p { font-size: 18px; color: #94a3b8; max-width: 600px; margin: 0 auto 40px; line-height: 1.7; }
+        .hero a { background: #38bdf8; color: #0f172a; padding: 16px 40px; border-radius: 10px; font-size: 16px; font-weight: bold; text-decoration: none; }
+        .problem { background: #1e293b; padding: 80px 60px; text-align: center; }
+        .problem h2 { font-size: 32px; margin-bottom: 16px; }
+        .problem p { color: #94a3b8; font-size: 16px; max-width: 650px; margin: 0 auto; line-height: 1.7; }
+        .steps { padding: 80px 60px; max-width: 1000px; margin: 0 auto; }
+        .steps h2 { font-size: 32px; text-align: center; margin-bottom: 50px; }
+        .steps-grid { display: grid; grid-template-columns: repeat(3, 1fr); gap: 30px; }
+        .step { background: #1e293b; border-radius: 12px; padding: 30px; border: 1px solid #334155; text-align: center; }
+        .step .number { font-size: 36px; font-weight: bold; color: #38bdf8; margin-bottom: 16px; }
+        .step h3 { font-size: 18px; margin-bottom: 10px; }
+        .step p { color: #94a3b8; font-size: 14px; line-height: 1.6; }
+        .benefits { background: #1e293b; padding: 80px 60px; }
+        .benefits h2 { font-size: 32px; text-align: center; margin-bottom: 50px; }
+        .benefits-grid { display: grid; grid-template-columns: repeat(2, 1fr); gap: 24px; max-width: 900px; margin: 0 auto; }
+        .benefit { background: #0f172a; border-radius: 12px; padding: 24px; border: 1px solid #334155; }
+        .benefit h3 { color: #38bdf8; font-size: 16px; margin-bottom: 8px; }
+        .benefit p { color: #94a3b8; font-size: 14px; line-height: 1.6; }
+        .cta { text-align: center; padding: 80px 20px; }
+        .cta h2 { font-size: 36px; margin-bottom: 16px; }
+        .cta p { color: #94a3b8; font-size: 16px; margin-bottom: 40px; }
+        .cta a { background: #38bdf8; color: #0f172a; padding: 16px 40px; border-radius: 10px; font-size: 16px; font-weight: bold; text-decoration: none; }
+        footer { background: #1e293b; padding: 30px 60px; text-align: center; color: #475569; font-size: 13px; border-top: 1px solid #334155; line-height: 2; }
+        footer a { color: #38bdf8; text-decoration: none; }
+        footer a:hover { color: #7dd3fc; }
+    </style>
+</head>
+<body>
+    <nav>
+        <h1>PrivaMed</h1>
+        <div>
+            <a href="/predict" class="secondary">Predict Risk</a>
+            <a href="/dashboard">Try Demo</a>
+        </div>
+    </nav>
+    <div class="hero">
+        <h2>Train AI Together.<br><span>Share Nothing.</span></h2>
+        <p>PrivaMed lets hospitals collaborate to build powerful medical AI models without ever sharing patient data. Privacy guaranteed.</p>
+        <a href="/dashboard">See It In Action</a>
+    </div>
+    <div class="problem">
+        <h2>The Problem with Medical AI Today</h2>
+        <p>Building accurate AI models requires large datasets. But hospitals can't share patient data due to privacy laws like HIPAA and GDPR. This means each hospital trains on limited data — and gets limited results.</p>
+    </div>
+    <div class="steps">
+        <h2>How PrivaMed Works</h2>
+        <div class="steps-grid">
+            <div class="step">
+                <div class="number">1</div>
+                <h3>Each Hospital Trains Locally</h3>
+                <p>Your data never leaves your servers. The AI model trains directly on your local dataset.</p>
+            </div>
+            <div class="step">
+                <div class="number">2</div>
+                <h3>Only Weights Are Shared</h3>
+                <p>Instead of data, only the model's learned parameters are sent to the central server.</p>
+            </div>
+            <div class="step">
+                <div class="number">3</div>
+                <h3>Everyone Gets a Better Model</h3>
+                <p>The server combines all weights using FedAvg. Every hospital receives a stronger global model.</p>
+            </div>
+        </div>
+    </div>
+    <div class="benefits">
+        <h2>Why Hospitals Choose PrivaMed</h2>
+        <div class="benefits-grid">
+            <div class="benefit">
+                <h3>Patient Privacy Protected</h3>
+                <p>Raw patient data is never transmitted or stored outside your hospital's infrastructure.</p>
+            </div>
+            <div class="benefit">
+                <h3>Better AI with More Data</h3>
+                <p>Collaborate with other hospitals to train on larger, more diverse datasets without sharing them.</p>
+            </div>
+            <div class="benefit">
+                <h3>HIPAA & GDPR Compliant</h3>
+                <p>Designed from the ground up with privacy regulations in mind.</p>
+            </div>
+            <div class="benefit">
+                <h3>Works with Your Data</h3>
+                <p>Upload any CSV medical dataset and start training immediately. No complex setup required.</p>
+            </div>
+        </div>
+    </div>
+    <div class="cta">
+        <h2>Ready to See It Work?</h2>
+        <p>Upload your dataset and watch federated learning in action.</p>
+        <a href="/dashboard">Try the Demo</a>
+    </div>
+    <footer>
+        PrivaMed — Federated Learning System — Train Together, Share Nothing<br>
+        © 2026 Nour Maaita. All rights reserved.<br>
+        <a href="https://github.com/nmaaitallh">GitHub</a> &nbsp;|&nbsp;
+        <a href="https://www.linkedin.com/in/nour-maaita-197733243/">LinkedIn</a>
+    </footer>
+</body>
+</html>

templates/predict.html

@@ -0,0 +1,247 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>PrivaMed - Medical Risk Prediction</title>
+    <style>
+        * { margin: 0; padding: 0; box-sizing: border-box; }
+        body { font-family: 'Segoe UI', sans-serif; background: #0f172a; color: #e2e8f0; }
+        header { background: #1e293b; padding: 20px 40px; border-bottom: 1px solid #334155; display: flex; justify-content: space-between; align-items: center; }
+        header h1 { color: #38bdf8; font-size: 24px; }
+        header a { color: #94a3b8; font-size: 14px; text-decoration: none; }
+        header a:hover { color: #38bdf8; }
+        .container { max-width: 700px; margin: 40px auto; padding: 0 20px; }
+        h2 { font-size: 22px; margin-bottom: 8px; }
+        .subtitle { color: #94a3b8; font-size: 14px; margin-bottom: 30px; }
+        .diagnosis-select { background: #1e293b; border-radius: 12px; padding: 20px; border: 1px solid #334155; margin-bottom: 20px; }
+        .diagnosis-select label { display: block; font-size: 13px; color: #94a3b8; margin-bottom: 8px; }
+        .diagnosis-select select { width: 100%; background: #0f172a; border: 1px solid #334155; border-radius: 8px; padding: 12px 14px; color: #e2e8f0; font-size: 14px; }
+        .diagnosis-select select:focus { outline: none; border-color: #38bdf8; }
+        .form-box { background: #1e293b; border-radius: 12px; padding: 30px; border: 1px solid #334155; }
+        .form-grid { display: grid; grid-template-columns: repeat(2, 1fr); gap: 20px; }
+        .field label { display: block; font-size: 13px; color: #94a3b8; margin-bottom: 6px; }
+        .field input, .field select { width: 100%; background: #0f172a; border: 1px solid #334155; border-radius: 8px; padding: 10px 14px; color: #e2e8f0; font-size: 14px; }
+        .field input:focus, .field select:focus { outline: none; border-color: #38bdf8; }
+        .btn { width: 100%; margin-top: 24px; background: #38bdf8; color: #0f172a; border: none; padding: 14px; border-radius: 8px; font-size: 15px; font-weight: bold; cursor: pointer; }
+        .btn:hover { background: #7dd3fc; }
+        .result { margin-top: 30px; background: #1e293b; border-radius: 12px; padding: 30px; border: 1px solid #334155; text-align: center; display: none; }
+        .result .risk { font-size: 36px; font-weight: bold; margin-bottom: 8px; }
+        .result .risk.high { color: #f87171; }
+        .result .risk.low { color: #34d399; }
+        .result .prob { color: #94a3b8; font-size: 15px; }
+        .result .confidence { font-size: 18px; margin-top: 8px; }
+        .coming-soon { background: #1e293b; border-radius: 12px; padding: 60px 30px; border: 1px solid #334155; text-align: center; display: none; }
+        .coming-soon h3 { font-size: 24px; color: #94a3b8; margin-bottom: 12px; }
+        .coming-soon p { color: #64748b; font-size: 14px; }
+    </style>
+</head>
+<body>
+    <header>
+        <h1>PrivaMed</h1>
+        <a href="/dashboard">Back to Dashboard</a>
+    </header>
+
+    <div class="container">
+        <h2>Medical Risk Prediction</h2>
+        <p class="subtitle">Select diagnosis type and enter patient data to get an instant prediction.</p>
+
+        <div class="diagnosis-select">
+            <label>Select Diagnosis Type</label>
+            <select id="diagnosisType" onchange="switchDiagnosis()">
+                <option value="diabetes">Diabetes Risk Assessment</option>
+                <option value="heart">Heart Disease Risk Assessment</option>
+                <option value="cancer">Cancer Screening (Coming Soon)</option>
+            </select>
+        </div>
+
+        <div class="form-box" id="diabetesForm">
+            <div class="form-grid">
+                <div class="field">
+                    <label>Pregnancies</label>
+                    <input type="number" id="pregnancies" placeholder="e.g. 2" min="0">
+                </div>
+                <div class="field">
+                    <label>Glucose (mg/dL)</label>
+                    <input type="number" id="glucose" placeholder="e.g. 120" min="0">
+                </div>
+                <div class="field">
+                    <label>Blood Pressure (mmHg)</label>
+                    <input type="number" id="blood_pressure" placeholder="e.g. 70" min="0">
+                </div>
+                <div class="field">
+                    <label>Skin Thickness (mm)</label>
+                    <input type="number" id="skin_thickness" placeholder="e.g. 20" min="0">
+                </div>
+                <div class="field">
+                    <label>Insulin (μU/mL)</label>
+                    <input type="number" id="insulin" placeholder="e.g. 80" min="0">
+                </div>
+                <div class="field">
+                    <label>BMI</label>
+                    <input type="number" id="bmi" placeholder="e.g. 28.5" step="0.1" min="0">
+                </div>
+                <div class="field">
+                    <label>Diabetes Pedigree</label>
+                    <input type="number" id="diabetes_pedigree" placeholder="e.g. 0.5" step="0.01" min="0">
+                </div>
+                <div class="field">
+                    <label>Age</label>
+                    <input type="number" id="d_age" placeholder="e.g. 35" min="0">
+                </div>
+            </div>
+            <button class="btn" onclick="predict()">Predict Risk</button>
+        </div>
+
+        <div class="form-box" id="heartForm" style="display:none;">
+            <div class="form-grid">
+                <div class="field">
+                    <label>Age</label>
+                    <input type="number" id="h_age" placeholder="e.g. 55" min="0">
+                </div>
+                <div class="field">
+                    <label>Sex</label>
+                    <select id="sex">
+                        <option value="1">Male</option>
+                        <option value="0">Female</option>
+                    </select>
+                </div>
+                <div class="field">
+                    <label>Chest Pain Type (0-3)</label>
+                    <input type="number" id="cp" placeholder="0-3" min="0" max="3">
+                </div>
+                <div class="field">
+                    <label>Resting BP (mmHg)</label>
+                    <input type="number" id="trestbps" placeholder="e.g. 130" min="0">
+                </div>
+                <div class="field">
+                    <label>Cholesterol (mg/dL)</label>
+                    <input type="number" id="chol" placeholder="e.g. 250" min="0">
+                </div>
+                <div class="field">
+                    <label>Fasting Blood Sugar > 120</label>
+                    <select id="fbs">
+                        <option value="0">No</option>
+                        <option value="1">Yes</option>
+                    </select>
+                </div>
+                <div class="field">
+                    <label>Resting ECG (0-2)</label>
+                    <input type="number" id="restecg" placeholder="0-2" min="0" max="2">
+                </div>
+                <div class="field">
+                    <label>Max Heart Rate</label>
+                    <input type="number" id="thalach" placeholder="e.g. 150" min="0">
+                </div>
+                <div class="field">
+                    <label>Exercise Induced Angina</label>
+                    <select id="exang">
+                        <option value="0">No</option>
+                        <option value="1">Yes</option>
+                    </select>
+                </div>
+                <div class="field">
+                    <label>ST Depression</label>
+                    <input type="number" id="oldpeak" placeholder="e.g. 2.3" step="0.1" min="0">
+                </div>
+                <div class="field">
+                    <label>Slope (0-2)</label>
+                    <input type="number" id="slope" placeholder="0-2" min="0" max="2">
+                </div>
+                <div class="field">
+                    <label>Major Vessels (0-3)</label>
+                    <input type="number" id="ca" placeholder="0-3" min="0" max="3">
+                </div>
+                <div class="field">
+                    <label>Thalassemia (0-3)</label>
+                    <input type="number" id="thal" placeholder="0-3" min="0" max="3">
+                </div>
+            </div>
+            <button class="btn" onclick="predict()">Predict Risk</button>
+        </div>
+
+        <div class="coming-soon" id="comingSoon">
+            <h3>Coming Soon</h3>
+            <p>This diagnosis type will be available in future updates as we train more federated models.</p>
+        </div>
+
+        <div class="result" id="result">
+            <div class="risk" id="riskLabel"></div>
+            <div class="confidence" id="confidenceLabel"></div>
+            <div class="prob" id="probLabel"></div>
+        </div>
+    </div>
+
+    <script>
+        function switchDiagnosis() {
+            const type = document.getElementById("diagnosisType").value;
+            const diabetesForm = document.getElementById("diabetesForm");
+            const heartForm = document.getElementById("heartForm");
+            const comingSoon = document.getElementById("comingSoon");
+            const result = document.getElementById("result");
+
+            result.style.display = "none";
+
+            if (type === "diabetes") {
+                diabetesForm.style.display = "block";
+                heartForm.style.display = "none";
+                comingSoon.style.display = "none";
+            } else if (type === "heart") {
+                diabetesForm.style.display = "none";
+                heartForm.style.display = "block";
+                comingSoon.style.display = "none";
+            } else {
+                diabetesForm.style.display = "none";
+                heartForm.style.display = "none";
+                comingSoon.style.display = "block";
+            }
+        }
+
+        function predict() {
+            const diagnosisType = document.getElementById("diagnosisType").value;
+            const data = new FormData();
+            data.append("diagnosis_type", diagnosisType);
+
+            if (diagnosisType === "diabetes") {
+                data.append("pregnancies", document.getElementById("pregnancies").value || 0);
+                data.append("glucose", document.getElementById("glucose").value || 0);
+                data.append("blood_pressure", document.getElementById("blood_pressure").value || 0);
+                data.append("skin_thickness", document.getElementById("skin_thickness").value || 0);
+                data.append("insulin", document.getElementById("insulin").value || 0);
+                data.append("bmi", document.getElementById("bmi").value || 0);
+                data.append("diabetes_pedigree", document.getElementById("diabetes_pedigree").value || 0);
+                data.append("age", document.getElementById("d_age").value || 0);
+            } else if (diagnosisType === "heart") {
+                data.append("age", document.getElementById("h_age").value || 0);
+                data.append("sex", document.getElementById("sex").value || 0);
+                data.append("cp", document.getElementById("cp").value || 0);
+                data.append("trestbps", document.getElementById("trestbps").value || 0);
+                data.append("chol", document.getElementById("chol").value || 0);
+                data.append("fbs", document.getElementById("fbs").value || 0);
+                data.append("restecg", document.getElementById("restecg").value || 0);
+                data.append("thalach", document.getElementById("thalach").value || 0);
+                data.append("exang", document.getElementById("exang").value || 0);
+                data.append("oldpeak", document.getElementById("oldpeak").value || 0);
+                data.append("slope", document.getElementById("slope").value || 0);
+                data.append("ca", document.getElementById("ca").value || 0);
+                data.append("thal", document.getElementById("thal").value || 0);
+            }
+
+            fetch("/predict_result", { method: "POST", body: data })
+                .then(r => r.json())
+                .then(res => {
+                    const result = document.getElementById("result");
+                    const riskLabel = document.getElementById("riskLabel");
+                    const confidenceLabel = document.getElementById("confidenceLabel");
+                    const probLabel = document.getElementById("probLabel");
+
+                    riskLabel.textContent = res.risk;
+                    riskLabel.className = "risk " + (res.risk === "High Risk" ? "high" : "low");
+                    confidenceLabel.textContent = "Confidence: " + res.confidence + "%";
+                    probLabel.textContent = res.disease + " Probability: " + res.probability + "%";
+                    result.style.display = "block";
+                });
+        }
+    </script>
+</body>
+</html>

training_results.json

@@ -0,0 +1 @@
+[{"round": 0, "loss": 0.6939, "accuracy": 0.5779}, {"round": 1, "loss": 0.5329, "accuracy": 0.7532}, {"round": 2, "loss": 0.4894, "accuracy": 0.7792}, {"round": 3, "loss": 0.4807, "accuracy": 0.7857}]

uploads/diabetes.csv

@@ -0,0 +1,769 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
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