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import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Conv2D, MaxPooling2D, Flatten, Conv1D, MaxPooling1D, Bidirectional
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
import gym
from gym import spaces
from flask import Flask, jsonify

# 文本数据诊断模型（LSTM）
def build_text_model(texts, labels):
    tokenizer = Tokenizer(num_words=1000)
    tokenizer.fit_on_texts(texts)
    sequences = tokenizer.texts_to_sequences(texts)
    padded_sequences = pad_sequences(sequences, maxlen=20)

    model = Sequential([
        Embedding(input_dim=1000, output_dim=16, input_length=20),
        LSTM(16),
        Dense(1, activation='sigmoid')
    ])

    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    model.fit(padded_sequences, labels, epochs=10)
    return model

# 图像诊断模型（CNN）
def build_image_model(images, labels):
    model = Sequential([
        Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)),
        MaxPooling2D((2, 2)),
        Conv2D(64, (3, 3), activation='relu'),
        MaxPooling2D((2, 2)),
        Flatten(),
        Dense(64, activation='relu'),
        Dense(1, activation='sigmoid')
    ])

    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    model.fit(images, labels, epochs=10)
    return model

# 声音诊断模型（双向 LSTM）
def build_sound_model(sounds, labels):
    model = Sequential([
        Bidirectional(LSTM(64), input_shape=(100, 10)),
        Dense(1, activation='sigmoid')
    ])

    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    model.fit(sounds, labels, epochs=10)
    return model

# 呼吸参数诊断模型（CNN）
def build_breathing_model(breathing_data, labels):
    model = Sequential([
        Conv1D(32, 3, activation='relu', input_shape=(100, 5)),
        MaxPooling1D(2),
        Conv1D(64, 3, activation='relu'),
        MaxPooling1D(2),
        Flatten(),
        Dense(64, activation='relu'),
        Dense(1, activation='sigmoid')
    ])

    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    model.fit(breathing_data, labels, epochs=10)
    return model

# 构建诊疗数据模型库
def build_diagnosis_model(data):
    X = data.drop('diagnosis', axis=1)
    y = data['diagnosis']

    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)

    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

    model = RandomForestClassifier()
    model.fit(X_train, y_train)
    return model

# 定义强化学习环境
class RespiratoryEnv(gym.Env):
    def __init__(self):
        self.action_space = spaces.Discrete(2)
        self.observation_space = spaces.Box(low=0, high=1, shape=(1,))
        self.state = np.random.rand(1)

    def step(self, action):
        reward = 1 if action == 0 else -1
        done = True
        info = {}
        return self.state, reward, done, info

    def reset(self):
        self.state = np.random.rand(1)
        return self.state

# 主函数
def main():
    # 示例数据
    texts = ["Patient has cough and difficulty breathing", "No obvious symptoms"]
    text_labels = [1, 0]
    images = np.random.rand(2, 100, 100, 3)
    image_labels = [1, 0]
    sounds = np.random.rand(2, 100, 10)
    sound_labels = [1, 0]
    breathing_data = np.random.rand(100, 100, 5)
    breathing_labels = np.random.randint(0, 2, 100)
    patient_data = pd.DataFrame({
        'feature1': np.random.rand(100),
        'feature2': np.random.rand(100),
        'diagnosis': np.random.randint(0, 2, 100)
    })

    # 构建各个模型
    text_model = build_text_model(texts, text_labels)
    image_model = build_image_model(images, image_labels)
    sound_model = build_sound_model(sounds, sound_labels)
    breathing_model = build_breathing_model(breathing_data, breathing_labels)
    diagnosis_model = build_diagnosis_model(patient_data)

    # 加权整合预测结果
    weights = [0.2, 0.3, 0.2, 0.3]
    predictions_text = text_model.predict(pad_sequences(Tokenizer(num_words=1000).texts_to_sequences(texts), maxlen=20))
    predictions_image = image_model.predict(images)
    predictions_sound = sound_model.predict(sounds)
    predictions_breathing = breathing_model.predict(breathing_data)

    final_predictions = (weights[0] * predictions_text +
                         weights[1] * predictions_image +
                         weights[2] * predictions_sound +
                         weights[3] * predictions_breathing)

    # 生成治疗方案数据
    treatment_input = final_predictions
    treatment_prediction = diagnosis_model.predict(treatment_input)

    treatment_scheme = {
        'diagnosis': treatment_prediction[0],
        'medicine_type': 'Type A',
        'medicine_amount': 10,
        'frequency': 3,
        'duration': 7
    }

    # 雾化治疗和疗效监测及模型优化
    env = make_vec_env(RespiratoryEnv, n_envs=1)
    model_rl = PPO('MlpPolicy', env, verbose=1)
    model_rl.learn(total_timesteps=1000)

    treatment_effect = 0.8  # 疗效指标

    # Flask 服务
    app = Flask(__name__)

    @app.route('/diagnose', methods=['GET'])
    def diagnose():
        result = {
            'diagnosis': treatment_prediction[0],
            'treatment_scheme': treatment_scheme
        }
        return jsonify(result)

    app.run(debug=True)

if __name__ == "__main__":
    main()