import flask

app = Flask(name)

encryption_key = "supersecretkey"

def anonymize_and_protect_data(data): # Implement data anonymization and protection techniques anonymized_data = anonymize_data(data) encrypted_data = encrypt_data(anonymized_data) ensure_data_encryption_and_compliance(encrypted_data) return encrypted_data

def anonymize_data(data): # Implement data anonymization techniques anonymized_data = data.copy() # Anonymize age, symptoms, etc. return anonymized_data

def use_ai_model(data): # Use AI model to perform diagnosis diagnosis_result = ai_model.predict(data) return diagnosis_result

def explain_diagnosis(diagnosis_result, user_data): # Generate an explanation for the diagnosis decision explanation = generate_explanation(diagnosis_result, user_data) return explanation

@app.route('/', methods=['GET', 'POST']) def collect_user_input(): if request.method == 'POST': age = int(request.form['age']) symptoms = request.form['symptoms'] # Collect other relevant user input

    user_data = {
        'age': age,
        'symptoms': symptoms,
        # Include other fields as needed
    }

    # Ensure proper data anonymization and protection
    user_data = anonymize_and_protect_data(user_data)

    # Perform diagnosis
    diagnosis_result = use_ai_model(user_data)

    # Generate explanation
    explanation = explain_diagnosis(diagnosis_result, user_data)

    return render_template('result.html', diagnosis=diagnosis_result, explanation=explanation)

return render_template('input_form.html')

def train_model_on_diverse_data(): # Train AI model on diverse and representative medical data model = train_model(train_data) return model

def evaluate_fairness_and_apply_post_processing(model, test_data): # Evaluate model's fairness across demographic groups fairness_metrics = evaluate_fairness(model, test_data)

# Apply fairness-aware post-processing if needed
if fairness_metrics.requires_correction():
    corrected_predictions = apply_fairness_post_processing(model, test_data)
else:
    corrected_predictions = model.predict(test_data)

return corrected_predictions, fairness_metrics

def implement_differential_privacy(data): # Implement differential privacy mechanisms for sensitive data protected_data = apply_differential_privacy(data) return protected_data

if name == 'main': app.run(debug=True)