app.py

##########################################
# Step 0: 导入必需的库
##########################################
import streamlit as st
from transformers import pipeline, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, AutoModelForCausalLM, AutoTokenizer, pipeline
from datasets import load_dataset
from IPython.display import Audio, display
import torch
import soundfile as sf

from google.colab import drive

from huggingface_hub import login

# Streamlit application title
st.title("Comment reply for you")
st.write("automative reply")

# Text input for user to enter the comment
text = st.text_area("Enter your comment", "")


##########################################
# Step 1：情感分析 - 分析用户评论的情感倾向
##########################################
# Perform tasks when the user clicks the "Comment" button
if st.button("Comment"):

pipe = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")

# 使用 "j-hartmann/emotion-english-distilroberta-base" 模型进行多维度情感分类
emotion_classifier = pipeline(
    "text-classification",
    model="j-hartmann/emotion-english-distilroberta-base",
    return_all_scores=True
)

# 示例用户评论（可替换为实际评论）
user_review = "I love the fast delivery, but the product quality could be better."

# 对评论进行情感分析
emotion_results = emotion_classifier(user_review)[0]  # 返回列表中的第一个结果（单条输入）

# 打印所有情感维度及其分数

print("情感分析结果（多维度）：")
for emotion in emotion_results:
    print(f"{emotion['label']}: {emotion['score']:.4f}")
    
    st.write("Text:", text)
    st.write("Label:", max_label)
    st.write("Score:", max_score)
    
# 提取置信度最高的情感标签（可选）
dominant_emotion = max(emotion_results, key=lambda x: x['score'])
print("\n主导情感：", dominant_emotion['label'], f"(置信度: {dominant_emotion['score']:.2f})")



##########################################
# Step 2：回复生成 - 根据情感生成自动回复
##########################################

emotion_strategies = {
    "anger": {
        "prompt": (
            "Customer complaint: '{review}'\n\n"
            "As a customer service representative, craft a professional response that:\n"
            "- Begins with sincere apology and acknowledgment\n"
            "- Clearly explains solution process with concrete steps\n"
            "- Offers appropriate compensation/redemption\n"
            "- Keeps tone humble and solution-focused (3-4 sentences)\n\n"
            "Response:"
        )
    },
    "disgust": {
        "prompt": (
            "Customer quality concern: '{review}'\n\n"
            "As a customer service representative, craft a response that:\n"
            "- Immediately acknowledges the product issue\n"
            "- Explains quality control measures being taken\n"
            "- Provides clear return/replacement instructions\n"
            "- Offers goodwill gesture (3-4 sentences)\n\n"
            "Response:"
        )
    },
    "fear": {
        "prompt": (
            "Customer safety concern: '{review}'\n\n"
            "As a customer service representative, craft a reassuring response that:\n"
            "- Directly addresses the safety worries\n"
            "- References relevant certifications/standards\n"
            "- Offers dedicated support contact\n"
            "- Provides satisfaction guarantee (3-4 sentences)\n\n"
            "Response:"
        )
    },
    "joy": {
        "prompt": (
            "Customer review: '{review}'\n\n"
            "As a customer service representative, craft a concise response that:\n"
            "- Specifically acknowledges both positive and constructive feedback\n"
            "- Briefly mentions loyalty/referral programs\n"
            "- Ends with shopping invitation (3-4 sentences)\n\n"
            "Response:"
        )
    },
    "neutral": {
        "prompt": (
            "Customer feedback: '{review}'\n\n"
            "As a customer service representative, craft a balanced response that:\n"
            "- Provides additional relevant product information\n"
            "- Highlights key service features\n"
            "- Politely requests more detailed feedback\n"
            "- Maintains professional tone (3-4 sentences)\n\n"
            "Response:"
        )
    },
    "sadness": {
        "prompt": (
            "Customer disappointment: '{review}'\n\n"
            "As a customer service representative, craft an empathetic response that:\n"
            "- Shows genuine understanding of the issue\n"
            "- Proposes personalized recovery solution\n"
            "- Offers extended support options\n"
            "- Maintains positive outlook (3-4 sentences)\n\n"
            "Response:"
        )
    },
    "surprise": {
        "prompt": (
            "Customer enthusiastic feedback: '{review}'\n\n"
            "As a customer service representative, craft a response that:\n"
            "- Matches customer's positive energy appropriately\n"
            "- Highlights unexpected product benefits\n"
            "- Invites to user community/events\n"
            "- Maintains brand voice (3-4 sentences)\n\n"
            "Response:"
        )
    }
}



# 生成回复Prompt
template = emotion_strategies[dominant_emotion['label'].lower()]["prompt"]
prompt = template.format(review=user_review)
print(prompt)




# 加载Llama-3作为text generation模型
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")




inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)

input_length = inputs.input_ids.shape[1]
response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)
print(response)




##########################################
# Step 3：语音生成 - 根据回复合成语音
##########################################

# 加载模型和处理器
#processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
#speech_model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
#vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

# 创建默认的说话人嵌入
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)  # 女性中性语音

# 文本预处理和语音合成
inputs = processor(text=response, return_tensors="pt")
spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)

# 使用声码器生成波形音频
with torch.no_grad():
    speech = vocoder(spectrogram)

# 保存为WAV文件(16kHz采样率)
sf.write("customer_service_response.wav", speech.numpy(), samplerate=16000)

print("语音生成完成，已保存为 customer_service_response.wav")