system-design-primer/README-zh-Hans.md

import pandas as pd
import numpy as np
import hashlib
from cryptography.fernet import Fernet
import logging
import time
from flask import Flask, render_template, request
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, f1_score
from opacus import PrivacyEngine
import torch
import torch.nn as nn
import torch.optim as optim

app = Flask(__name__)

# Initialize logging
logging.basicConfig(filename='app_monitoring.log', level=logging.INFO)

# Step 1: Collect User Input and Medical Data

# Collect data from patients using an app
data = {‘name’:[], 'age': [], 'gender': [], 'symptoms': [], 'diagnosis': []}
while True:
    name = input(‘Enter full name:’)
    age = input('Enter age: ')
    gender = input('Enter gender (M/F): ')
    symptoms = input('Enter symptoms (comma-separated): ')
    diagnosis = input('Enter diagnosis (Y/N): ')
    
    data['age'].append(age)
    data['gender'].append(gender)
    data['symptoms'].append(symptoms)
    data['diagnosis'].append(diagnosis)
    
    again = input('Enter another patient? (Y/N): ')
    if again.lower() != 'y':
        break
        
# Convert data to a pandas dataframe
data_df = pd.DataFrame(data)

# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(data_df.drop('diagnosis', axis=1), data_df['diagnosis'], test_size=0.2, random_state=42)

# Train decision tree classifier
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

# Predict on test set
y_pred = clf.predict(X_test)

# Calculate accuracy score
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

# Step 2: Ensure Proper Data Anonymization and Protection

    # Implement data anonymization and protection mechanisms
def anonymize_and_protect_data(user_data):
    anonymized_data = user_data.copy()
    age_ranges = {'18-25', '26-35', '36-45', '46-55', '56+'}
    anonymized_data['age'] = find_age_range(user_data['age'], age_ranges)
    anonymized_data['symptoms'] = perturb_symptoms(user_data['symptoms'])
    return anonymized_data

    
# Step 3: Use AI Model to Perform Diagnosis
def use_ai_model(anonymized_data):
    X_train, y_train = load_medical_records()

model = Sequential()
model.add(LSTM(64, input_shape=(X_train.shape[1], X_train.shape[2]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(32, return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(y_train.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train the model
model.fit(X_train, y_train, epochs=50, batch_size=32)
symptoms = load_symptoms()
diagnosis = model.predict(symptoms)
    diagnosis_result = model.predict(anonymized_data)
    return diagnosis_result

# Step 4: Generate Explanation for Diagnosis Decision
Step 4: Generate Explanation for Diagnosis Decision
def generate_explanation(diagnosis_result, anonymized_data):
    explanation = "Example explanation: Diagnosis based on AI analysis of user input."
    return explanation

# Step 5: Train the AI Model on Diverse Data
def train_model(train_data):
    model = define_rnn_model()
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    model.fit(train_data['X_train'], train_data['y_train'], epochs=10, batch_size=32)
    return model

# Step 6: Evaluate Model's Fairness Across Demographic Groups
def evaluate_fairness(model, test_data):
    fairness_metrics = {}
    demographics = ['gender', 'age_group', 'race']
    
    predicted_labels = model.predict(test_data['X_test'])
    
    for demo in demographics:
        group_indices = test_data[demo] == 'group_1'  
        group_labels = predicted_labels[group_indices]
        group_positive_rate = np.mean(group_labels)
        
        fairness_metrics[demo] = {
            'group_positive_rate': group_positive_rate,
            'overall_positive_rate': np.mean(predicted_labels),
            'disparity': np.abs(group_positive_rate - np.mean(predicted_labels))
        }
    
    return fairness_metrics

test_data = {
    'X_test': np.random.randn(100, 10),
    'gender': ['group_1'] * 50 + ['group_2'] * 50,
    'age_group': ['group_1'] * 30 + ['group_2'] * 70,
    'race': ['group_1'] * 40 + ['group_2'] * 60
}

def load_test_data():
    return test_data

# Step 7: Apply Fairness-Aware Post-Processing If Needed
def apply_fairness_post_processing(model, test_data, fairness_metrics):
    corrected_predictions = model.predict(test_data['X_test'])
    
    for demo, metrics in fairness_metrics.items():
        group_indices = test_data[demo] == 'group_1' 
        group_positive_rate = metrics['group_positive_rate']
        overall_positive_rate = metrics['overall_positive_rate']
        disparity = metrics['disparity']
        
        if disparity > 0.1:  
            correction_factor = group_positive_rate / overall_positive_rate
            corrected_predictions[group_indices] *= correction_factor
    
    return corrected_predictions

# Step 8: Implement Differential Privacy Mechanisms If Dealing with Sensitive Data
def implement_differential_privacy(data):

class RNNModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(RNNModel, self).__init__()
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)
    
    def forward(self, x):
         rnn_output, _ = self.rnn(x)
         return rnn_output
       

# Load and preprocess your training data
train_data = pd.read_csv('train_data.csv')  # Adjust the filename as 
train_data['age'].fillna(train_data['age'].median(), inplace=True)
train_data['gender'] = train_data['gender'].map({'M': 0, 'F': 1})
train_data['symptoms'] = train_data['symptoms'].apply(lambda x: x.split(','))

input_size = input_tensor.shape[1]  
hidden_size = 64  
num_classes = 2   
learning_rate = 0.001

model = RNNModel(input_size, hidden_size, num_classes)

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

privacy_engine = PrivacyEngine(
    model,
    sample_rate=0.1,
    alphas=[1, 10, 100],
    noise_multiplier=1.3,
    max_grad_norm=1.0
)
privacy_engine.attach(optimizer)

for epoch in range(num_epochs):
    for inputs, labels in train_data:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

def perform_diagnosis(user_data):
    with torch.no_grad():
   age = user_data['age']
   symptoms = user_data['symptoms']
     output = model(input_tensor)
        diagnosis_result = torch.argmax(output).item()
    return diagnosis_result
    protected_data = apply_differential_privacy(data)
    return protected_data

# Step 9: Ensure Data Encryption and Compliance with Regulations
def ensure_data_encryption_and_compliance(protected_data):
 
encryption_key = b'supersecretkey'

def encrypt_data(data):
    fernet = Fernet(encryption_key)
    encrypted_data = {}
    for field, value in data.items():
        encrypted_value = fernet.encrypt(str(value).encode())
        encrypted_data[field] = encrypted_value.decode()
    return encrypted_data

def decrypt_data(encrypted_data):
    fernet = Fernet(encryption_key)
    decrypted_data = {}
    for field, encrypted_value in encrypted_data.items():
        decrypted_value = fernet.decrypt(encrypted_value.encode()).decode()
        decrypted_data[field] = decrypted_value
    return decrypted_data

def comply_with_regulations(encrypted_data):
    hashed_data = {}
    for field, encrypted_value in encrypted_data.items():
        hash_value = hashlib.sha256(encrypted_value.encode()).hexdigest()
        hashed_data[field] = hash_value
    return hashed_data

if __name__ == '__main__':
    user_data = {
        'age': 30,
        'symptoms': 'cough, fever, fatigue'
        # Include other fields
    }
    
    # Encrypt user data
    encrypted_data = encrypt_data(user_data)
    
    # Comply with data protection regulations
    compliant_data = comply_with_regulations(encrypted_data)
    
    print('Original user data:', user_data)
    print('Encrypted user data:', encrypted_data)
    print('Compliant data:', compliant_data)

    encrypted_data = encrypt_data(protected_data)
    comply_with_regulations(encrypted_data)

# Step 10: Monitor App Usage, Performance, and Accuracy
def monitor_app_and_handle_exceptions(user_data):
    try:
        start_time = time.time()
        
        user_data = collect_user_input()
        anonymized_data = anonymize_and_protect_data(user_data)
        diagnosis_result = use_ai_model(anonymized_data)
        explanation = generate_explanation(diagnosis_result, anonymized_data)

        test_data = load_test_data()
        fairness_metrics = evaluate_fairness(trained_model, test_data)

        corrected_predictions = apply_fairness_post_processing(trained_model, test_data)

        protected_data = implement_differential_privacy(user_data)

        ensure_data_encryption_and_compliance(protected_data)

        end_time = time.time()
        processing_time = end_time - start_time
        log_app_usage(user_data, anonymized_data, explanation, fairness_metrics, processing_time)

    except Exception as e:
        handle_exceptions(e)

# Step 11: Implement Update Mechanisms to Keep the App's Ethical Practices Up-to-Date
def implement_update_mechanisms():
    def check_for_updates():
    current_version = get_app_version()
    latest_version = fetch_latest_version()
    
    if current_version < latest_version:
        update_app_ethical_practices()
        update_app_version(latest_version)
        print("App's ethical practices have been updated to the latest version.")
    else:
        print("App's ethical practices are up-to-date.")

# Step 12: Create User Interfaces for Interaction and Display Terms, Policies, and Feedback Options
def create_user_interfaces():
    def index():
    if request.method == 'POST':
        age = int(request.form['age'])
        symptoms = request.form['symptoms']
        # Process other user input and perform diagnosis

        # Render diagnosis result page
        return render_template('diagnosis_result.html', diagnosis=diagnosis_result)

    return render_template('index.html')


# Step 13: Continuous Improvement
def continuous_improvement():
    # Implement continuous improvement strategies
    pass

# Step 14: End
def end():
    import atexit

# Define cleanup or finalization functions here
def cleanup_resources():
    # Perform cleanup tasks such as closing database connections, releasing resources, etc.
    print("Cleaning up resources...")

def save_logs():
    # Save logs or other important data before exiting
    print("Saving logs...")

# Register cleanup functions to be called on app exit
atexit.register(cleanup_resources)
atexit.register(save_logs)

if __name__ == '__main__':
    # Load and preprocess your training and test data
    train_data = load_train_data()
    test_data = load_test_data()

    # Train your model
    trained_model = train_model(train_data)
    fairness_metrics = evaluate_fairness(trained_model, test_data)

    # Apply fairness-aware post-processing
    corrected_predictions = apply_fairness_post_processing(trained_model, test_data, fairness_metrics)

    # Encrypt and protect data
    user_data = {
        'age': 30,
        'symptoms': 'cough, fever, fatigue'
        # Include other fields
    }
    protected_data = apply_differential_privacy(user_data)
    encrypted_data = encrypt_data(protected_data)
    compliant_data = comply_with_regulations(encrypted_data)

    print('Fairness Metrics:', fairness_metrics)
    print('Corrected Predictions:', corrected_predictions)
    print('Compliant Data:', compliant_data)
    app.run()