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import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
# 初始化模型
emotion_analyzer = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
safety_checker = pipeline("text-classification", model="meta-llama/Meta-Llama-Guard-2-8B")
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
# 预定义安全回复模板
SAFE_RESPONSES = {
"crisis": "我注意到您可能需要专业帮助，建议立即联系心理咨询师或拨打心理援助热线。",
"sadness": "听起来您最近压力很大，要不要试试深呼吸或听舒缓音乐？",
"anger": "情绪波动很正常，我们可以一起分析问题的根源。"
}
def generate_response(user_input, history):
# 安全检查
safety_result = safety_checker(user_input)[0]
if safety_result["label"] == "UNSAFE":
return SAFE_RESPONSES["crisis"]
# 情绪分析
emotion = emotion_analyzer(user_input)[0]["label"]
# 根据情绪选择生成策略
if emotion in ["sadness", "fear"]:
return SAFE_RESPONSES.get(emotion, "我理解您的感受，可以多聊聊吗？")
# 生成对话
inputs = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
reply_ids = model.generate(
inputs,
max_length=1000,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3 # 避免重复
)
response = tokenizer.decode(reply_ids[:, inputs.shape[-1]:][0], skip_special_tokens=True)
return response
# 创建Gradio界面
demo = gr.ChatInterface(
fn=generate_response,
examples=["最近总是失眠", "感觉没有人理解我", "考试成绩让我很焦虑"],
title="青少年心理健康助手",
description="请随时倾诉您的感受，我会尽力帮助您调整情绪。"
)
demo.launch()
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