354 lines
12 KiB
Markdown
354 lines
12 KiB
Markdown
import pandas as pd
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import numpy as np
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import hashlib
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from cryptography.fernet import Fernet
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import logging
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import time
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from flask import Flask, render_template, request
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import tensorflow as tf
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.metrics import accuracy_score, f1_score
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from opacus import PrivacyEngine
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import torch
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import torch.nn as nn
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import torch.optim as optim
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app = Flask(__name__)
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# Initialize logging
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logging.basicConfig(filename='app_monitoring.log', level=logging.INFO)
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# Step 1: Collect User Input and Medical Data
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# Collect data from patients using an app
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data = {‘name’:[], 'age': [], 'gender': [], 'symptoms': [], 'diagnosis': []}
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while True:
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name = input(‘Enter full name:’)
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age = input('Enter age: ')
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gender = input('Enter gender (M/F): ')
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symptoms = input('Enter symptoms (comma-separated): ')
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diagnosis = input('Enter diagnosis (Y/N): ')
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data['age'].append(age)
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data['gender'].append(gender)
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data['symptoms'].append(symptoms)
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data['diagnosis'].append(diagnosis)
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again = input('Enter another patient? (Y/N): ')
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if again.lower() != 'y':
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break
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# Convert data to a pandas dataframe
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data_df = pd.DataFrame(data)
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# Split dataset into training and testing sets
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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)
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# Train decision tree classifier
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clf = DecisionTreeClassifier()
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clf.fit(X_train, y_train)
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# Predict on test set
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y_pred = clf.predict(X_test)
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# Calculate accuracy score
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accuracy = accuracy_score(y_test, y_pred)
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print('Accuracy:', accuracy)
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# Step 2: Ensure Proper Data Anonymization and Protection
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# Implement data anonymization and protection mechanisms
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def anonymize_and_protect_data(user_data):
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anonymized_data = user_data.copy()
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age_ranges = {'18-25', '26-35', '36-45', '46-55', '56+'}
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anonymized_data['age'] = find_age_range(user_data['age'], age_ranges)
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anonymized_data['symptoms'] = perturb_symptoms(user_data['symptoms'])
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return anonymized_data
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# Step 3: Use AI Model to Perform Diagnosis
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def use_ai_model(anonymized_data):
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X_train, y_train = load_medical_records()
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model = Sequential()
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model.add(LSTM(64, input_shape=(X_train.shape[1], X_train.shape[2]), return_sequences=True))
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model.add(Dropout(0.2))
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model.add(LSTM(32, return_sequences=False))
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model.add(Dropout(0.2))
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model.add(Dense(y_train.shape[1], activation='softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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# Train the model
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model.fit(X_train, y_train, epochs=50, batch_size=32)
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symptoms = load_symptoms()
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diagnosis = model.predict(symptoms)
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diagnosis_result = model.predict(anonymized_data)
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return diagnosis_result
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# Step 4: Generate Explanation for Diagnosis Decision
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Step 4: Generate Explanation for Diagnosis Decision
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def generate_explanation(diagnosis_result, anonymized_data):
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explanation = "Example explanation: Diagnosis based on AI analysis of user input."
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return explanation
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# Step 5: Train the AI Model on Diverse Data
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def train_model(train_data):
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model = define_rnn_model()
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model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
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model.fit(train_data['X_train'], train_data['y_train'], epochs=10, batch_size=32)
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return model
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# Step 6: Evaluate Model's Fairness Across Demographic Groups
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def evaluate_fairness(model, test_data):
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fairness_metrics = {}
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demographics = ['gender', 'age_group', 'race']
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predicted_labels = model.predict(test_data['X_test'])
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for demo in demographics:
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group_indices = test_data[demo] == 'group_1'
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group_labels = predicted_labels[group_indices]
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group_positive_rate = np.mean(group_labels)
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fairness_metrics[demo] = {
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'group_positive_rate': group_positive_rate,
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'overall_positive_rate': np.mean(predicted_labels),
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'disparity': np.abs(group_positive_rate - np.mean(predicted_labels))
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}
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return fairness_metrics
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test_data = {
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'X_test': np.random.randn(100, 10),
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'gender': ['group_1'] * 50 + ['group_2'] * 50,
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'age_group': ['group_1'] * 30 + ['group_2'] * 70,
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'race': ['group_1'] * 40 + ['group_2'] * 60
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}
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def load_test_data():
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return test_data
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# Step 7: Apply Fairness-Aware Post-Processing If Needed
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def apply_fairness_post_processing(model, test_data, fairness_metrics):
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corrected_predictions = model.predict(test_data['X_test'])
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for demo, metrics in fairness_metrics.items():
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group_indices = test_data[demo] == 'group_1'
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group_positive_rate = metrics['group_positive_rate']
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overall_positive_rate = metrics['overall_positive_rate']
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disparity = metrics['disparity']
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if disparity > 0.1:
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correction_factor = group_positive_rate / overall_positive_rate
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corrected_predictions[group_indices] *= correction_factor
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return corrected_predictions
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# Step 8: Implement Differential Privacy Mechanisms If Dealing with Sensitive Data
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def implement_differential_privacy(data):
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class RNNModel(nn.Module):
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def __init__(self, input_size, hidden_size, num_classes):
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super(RNNModel, self).__init__()
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self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
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self.fc = nn.Linear(hidden_size, num_classes)
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def forward(self, x):
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rnn_output, _ = self.rnn(x)
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return rnn_output
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# Load and preprocess your training data
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train_data = pd.read_csv('train_data.csv') # Adjust the filename as
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train_data['age'].fillna(train_data['age'].median(), inplace=True)
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train_data['gender'] = train_data['gender'].map({'M': 0, 'F': 1})
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train_data['symptoms'] = train_data['symptoms'].apply(lambda x: x.split(','))
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input_size = input_tensor.shape[1]
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hidden_size = 64
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num_classes = 2
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learning_rate = 0.001
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model = RNNModel(input_size, hidden_size, num_classes)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=learning_rate)
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privacy_engine = PrivacyEngine(
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model,
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sample_rate=0.1,
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alphas=[1, 10, 100],
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noise_multiplier=1.3,
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max_grad_norm=1.0
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)
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privacy_engine.attach(optimizer)
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for epoch in range(num_epochs):
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for inputs, labels in train_data:
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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def perform_diagnosis(user_data):
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with torch.no_grad():
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age = user_data['age']
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symptoms = user_data['symptoms']
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output = model(input_tensor)
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diagnosis_result = torch.argmax(output).item()
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return diagnosis_result
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protected_data = apply_differential_privacy(data)
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return protected_data
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# Step 9: Ensure Data Encryption and Compliance with Regulations
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def ensure_data_encryption_and_compliance(protected_data):
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encryption_key = b'supersecretkey'
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def encrypt_data(data):
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fernet = Fernet(encryption_key)
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encrypted_data = {}
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for field, value in data.items():
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encrypted_value = fernet.encrypt(str(value).encode())
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encrypted_data[field] = encrypted_value.decode()
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return encrypted_data
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def decrypt_data(encrypted_data):
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fernet = Fernet(encryption_key)
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decrypted_data = {}
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for field, encrypted_value in encrypted_data.items():
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decrypted_value = fernet.decrypt(encrypted_value.encode()).decode()
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decrypted_data[field] = decrypted_value
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return decrypted_data
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def comply_with_regulations(encrypted_data):
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hashed_data = {}
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for field, encrypted_value in encrypted_data.items():
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hash_value = hashlib.sha256(encrypted_value.encode()).hexdigest()
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hashed_data[field] = hash_value
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return hashed_data
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if __name__ == '__main__':
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user_data = {
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'age': 30,
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'symptoms': 'cough, fever, fatigue'
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# Include other fields
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}
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# Encrypt user data
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encrypted_data = encrypt_data(user_data)
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# Comply with data protection regulations
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compliant_data = comply_with_regulations(encrypted_data)
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print('Original user data:', user_data)
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print('Encrypted user data:', encrypted_data)
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print('Compliant data:', compliant_data)
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encrypted_data = encrypt_data(protected_data)
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comply_with_regulations(encrypted_data)
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# Step 10: Monitor App Usage, Performance, and Accuracy
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def monitor_app_and_handle_exceptions(user_data):
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try:
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start_time = time.time()
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user_data = collect_user_input()
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anonymized_data = anonymize_and_protect_data(user_data)
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diagnosis_result = use_ai_model(anonymized_data)
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explanation = generate_explanation(diagnosis_result, anonymized_data)
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test_data = load_test_data()
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fairness_metrics = evaluate_fairness(trained_model, test_data)
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corrected_predictions = apply_fairness_post_processing(trained_model, test_data)
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protected_data = implement_differential_privacy(user_data)
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ensure_data_encryption_and_compliance(protected_data)
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end_time = time.time()
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processing_time = end_time - start_time
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log_app_usage(user_data, anonymized_data, explanation, fairness_metrics, processing_time)
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except Exception as e:
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handle_exceptions(e)
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# Step 11: Implement Update Mechanisms to Keep the App's Ethical Practices Up-to-Date
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def implement_update_mechanisms():
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def check_for_updates():
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current_version = get_app_version()
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latest_version = fetch_latest_version()
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if current_version < latest_version:
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update_app_ethical_practices()
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update_app_version(latest_version)
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print("App's ethical practices have been updated to the latest version.")
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else:
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print("App's ethical practices are up-to-date.")
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# Step 12: Create User Interfaces for Interaction and Display Terms, Policies, and Feedback Options
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def create_user_interfaces():
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def index():
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if request.method == 'POST':
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age = int(request.form['age'])
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symptoms = request.form['symptoms']
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# Process other user input and perform diagnosis
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# Render diagnosis result page
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return render_template('diagnosis_result.html', diagnosis=diagnosis_result)
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return render_template('index.html')
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# Step 13: Continuous Improvement
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def continuous_improvement():
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# Implement continuous improvement strategies
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pass
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# Step 14: End
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def end():
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import atexit
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# Define cleanup or finalization functions here
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def cleanup_resources():
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# Perform cleanup tasks such as closing database connections, releasing resources, etc.
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print("Cleaning up resources...")
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def save_logs():
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# Save logs or other important data before exiting
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print("Saving logs...")
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# Register cleanup functions to be called on app exit
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atexit.register(cleanup_resources)
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atexit.register(save_logs)
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if __name__ == '__main__':
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# Load and preprocess your training and test data
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train_data = load_train_data()
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test_data = load_test_data()
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# Train your model
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trained_model = train_model(train_data)
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fairness_metrics = evaluate_fairness(trained_model, test_data)
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# Apply fairness-aware post-processing
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corrected_predictions = apply_fairness_post_processing(trained_model, test_data, fairness_metrics)
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# Encrypt and protect data
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user_data = {
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'age': 30,
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'symptoms': 'cough, fever, fatigue'
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# Include other fields
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}
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protected_data = apply_differential_privacy(user_data)
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encrypted_data = encrypt_data(protected_data)
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compliant_data = comply_with_regulations(encrypted_data)
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print('Fairness Metrics:', fairness_metrics)
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print('Corrected Predictions:', corrected_predictions)
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print('Compliant Data:', compliant_data)
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app.run()
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