Update app.py

app.py

@@ -1,56 +1,188 @@
-# 在 Colab 中安装必要的包（通常 Colab 已经安装 transformers，但为了确保版本推荐运行以下代码）
+!pip install huggingface_hub
 !pip install --upgrade transformers
-!pip install soundfile  # 有些 TTS 模型可能依赖 soundfile
+!pip install datasets soundfile
 
-# 导入必要的库
-from transformers import pipeline
+##########################################
+# Step 0: 导入必需的库
+##########################################
+
+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
+
 
 ##########################################
 # Step 1：情感分析 - 分析用户评论的情感倾向
 ##########################################
-# 本示例中选择 distilbert-base-uncased-finetuned-sst-2-english 模型用于英文情感分类
-sentiment_classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+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."
 
-# 进行情感分析
-sentiment_result = sentiment_classifier(user_review)[0]
-print("情感分析结果：", sentiment_result)
+# 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}")
+
+# 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
 ##########################################
-# 使用 microsoft/DialoGPT-medium 模型生成回复
-reply_generator = pipeline("text-generation", model="microsoft/DialoGPT-medium")
-
-# 根据情感构造 Prompt，本次修改在 prompt 中加入明确的“回复：”提示，
-# 以引导模型产生新内容而不是简单重复输入内容
-label = sentiment_result['label'].upper()
-if "POSITIVE" in label or "LABEL_1" in label:
-    prompt = (f"用户评论表达了正面情感：\n评论内容： {user_review}\n"
-              f"请给出一段贴心温暖、表达感谢和认可，并让用户感到愉快的回复：\n回复：")
-elif "NEGATIVE" in label or "LABEL_0" in label:
-    prompt = (f"用户评论表达了负面情感：\n评论内容： {user_review}\n"
-              f"请给出一段温柔体贴、安抚用户并表示歉意的回复：\n回复：")
-else:
-    prompt = f"用户评论： {user_review}\n请给出一段中肯的回复：\n回复："
-
-print("\n生成回复的提示词 (Prompt):")
+
+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)
 
-# 生成回复文本，设置 truncation=True 避免长度问题，并适当提高采样温度与 top_p 值
-generated = reply_generator(prompt, max_length=150, do_sample=True, top_p=0.95, temperature=0.9, truncation=True)
 
-generated_text = generated[0]['generated_text']
 
-# 如果生成结果包含完整 prompt，则剔除
-if generated_text.startswith(prompt):
-    final_reply = generated_text[len(prompt):].strip()
-else:
-    final_reply = generated_text.strip()
 
-print("\n生成的回复文本：")
-print(final_reply)
+# 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")