########################################## # 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")