Update app.py

app.py

@@ -1,64 +1,50 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+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(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+    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。"
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()