# import gradio as gr # from huggingface_hub import InferenceClient # import json # import random # import re # from load_data import load_data # from openai import OpenAI # from transformers import AutoTokenizer, AutoModel # import weaviate # import os # import torch # from tqdm import tqdm # import numpy as np # import time # # 设置缓存目录 # os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib' # os.environ['TRANSFORMERS_CACHE'] = '/tmp/huggingface_cache' # os.makedirs(os.environ['MPLCONFIGDIR'], exist_ok=True) # os.makedirs(os.environ['TRANSFORMERS_CACHE'], exist_ok=True) # # Weaviate 连接配置 # WEAVIATE_API_KEY = "Y7c8DRmcxZ4nP5IJLwkznIsK84l6EdwfXwcH" # WEAVIATE_URL = "https://39nlafviqvard82k6y8btq.c0.asia-southeast1.gcp.weaviate.cloud" # weaviate_auth_config = weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY) # weaviate_client = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate_auth_config) # # 预训练模型配置 # MODEL_NAME = "bert-base-chinese" # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) # model = AutoModel.from_pretrained(MODEL_NAME) # # OpenAI 客户端 # openai_client = None # def initialize_openai_client(api_key): # global openai_client # openai_client = OpenAI(api_key=api_key) # def extract_keywords(text): # prompt = """ # 你是一个关键词提取机器人。提取用户输入中的关键词，特别是名词和形容词，关键词之间用空格分隔。例如：苹果 电脑 裤子 蓝色 裙。 # """ # messages = [ # {"role": "system", "content": prompt}, # {"role": "user", "content": f"从下面的文本中提取五个关键词，以空格分隔：{text}"} # ] # response = openai_client.chat.completions.create( # model="gpt-3.5-turbo", # messages=messages, # max_tokens=100, # temperature=0.7, # top_p=0.9, # ) # keywords = response.choices[0].message.content.split(' ') # return ','.join(keywords) # def match_keywords(query_keywords, ad_keywords_list, triggered_keywords, current_turn, window_size, threshold): # best_match_distance = 0 # best_match_index = -1 # for i, ad_keywords in enumerate(ad_keywords_list): # match_count = sum( # any( # ad_keyword in keyword and # (keyword not in triggered_keywords or current_turn - triggered_keywords[keyword] > window_size) # ) for keyword in query_keywords # ) # if match_count > best_match_distance: # best_match_distance = match_count # best_match_index = i # if best_match_distance >= threshold: # for keyword in query_keywords: # if any(ad_keyword in keyword for ad_keyword in ad_keywords_list[best_match_index]): # triggered_keywords[keyword] = current_turn # return best_match_distance, best_match_index # def encode_keywords_to_avg(keywords, model, tokenizer, device): # embeddings = [] # for keyword in tqdm(keywords): # inputs = tokenizer(keyword, return_tensors='pt', padding=True, truncation=True, max_length=512) # inputs.to(device) # with torch.no_grad(): # outputs = model(**inputs) # embeddings.append(outputs.last_hidden_state.mean(dim=1)) # avg_embedding = sum(embeddings) / len(embeddings) # return avg_embedding # def get_response_from_db(keywords_dict, class_name): # avg_vec = encode_keywords_to_avg(keywords_dict.keys(), model, tokenizer, device).numpy() # response = ( # weaviate_client.query # .get(class_name, ['keywords', 'summary']) # .with_near_vector({'vector': avg_vec}) # .with_limit(1) # .with_additional(['distance']) # .do() # ) # if class_name.capitalize() in response['data']['Get']: # result = response['data']['Get'][class_name.capitalize()][0] # return result['_additional']['distance'], result['summary'], result['keywords'] # else: # return None, None, None # def chatbot_response(message, max_tokens, temperature, top_p, window_size, threshold, user_weight, triggered_weight, api_key, state): # initialize_openai_client(api_key) # history = state.get('history', []) # triggered_keywords = state.get('triggered_keywords', {}) # current_turn = len(history) + 1 # combined_user_message = " ".join([h[0] for h in history[-window_size:]] + [message]) # combined_assistant_message = " ".join([h[1] for h in history[-window_size:]]) # user_keywords = extract_keywords(combined_user_message).split(',') # assistant_keywords = extract_keywords(combined_assistant_message).split(',') # keywords_dict = {keyword: user_weight for keyword in user_keywords} # for keyword in assistant_keywords: # keywords_dict[keyword] = keywords_dict.get(keyword, 0) + 1 # for keyword in list(keywords_dict.keys()): # if keyword in triggered_keywords and current_turn - triggered_keywords[keyword] < window_size: # keywords_dict[keyword] = triggered_weight # distance, ad_summary, ad_keywords = get_response_from_db(keywords_dict, class_name="ad_DB02") # if distance and distance < threshold: # ad_message = f"{message} 品牌{ad_summary}" # messages = [{"role": "system", "content": "你是一个热情的聊天机器人，应微妙地嵌入广告内容。"}] # for msg in history: # messages.extend([{"role": "user", "content": msg[0]}, {"role": "assistant", "content": msg[1]}]) # messages.append({"role": "user", "content": ad_message}) # for keyword in keywords_dict.keys(): # if any(ad_keyword in keyword for ad_keyword in ad_keywords.split(',')): # triggered_keywords[keyword] = current_turn # else: # messages = [{"role": "system", "content": "你是一个热情的聊天机器人。"}] # for msg in history: # messages.extend([{"role": "user", "content": msg[0]}, {"role": "assistant", "content": msg[1]}]) # messages.append({"role": "user", "content": message}) # response = openai_client.chat.completions.create( # model="gpt-3.5-turbo", # messages=messages, # max_tokens=max_tokens, # temperature=temperature, # top_p=top_p, # ) # history.append((message, response.choices[0].message.content)) # state['history'] = history # state['triggered_keywords'] = triggered_keywords # return response.choices[0].message.content, state # # Gradio UI # demo = gr.Interface( # fn=chatbot_response, # inputs=[ # gr.Textbox(label="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)"), # gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Window size"), # gr.Slider(minimum=0.01, maximum=0.20, value=0.08, step=0.01, label="Distance threshold"), # gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Weight of keywords from users"), # gr.Slider(minimum=0, maximum=2, value=0.5, step=0.5, label="Weight of triggered keywords"), # gr.Textbox(label="API Key"), # gr.State(value={'history': [], 'triggered_keywords': {}}) # Combined state # ], # outputs=[ # gr.Textbox(label="Response"), # gr.State() # Return the updated state # ] # ) # if __name__ == "__main__": # demo.launch(share=True) import gradio as gr from huggingface_hub import InferenceClient import json import random import re from load_data import load_data from openai import OpenAI from transformers import AutoTokenizer, AutoModel import weaviate import os import torch from tqdm import tqdm import numpy as np import time # 设置缓存目录 os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib' os.environ['TRANSFORMERS_CACHE'] = '/tmp/huggingface_cache' os.makedirs(os.environ['MPLCONFIGDIR'], exist_ok=True) os.makedirs(os.environ['TRANSFORMERS_CACHE'], exist_ok=True) # Weaviate 连接配置 WEAVIATE_API_KEY = "Y7c8DRmcxZ4nP5IJLwkznIsK84l6EdwfXwcH" WEAVIATE_URL = "https://39nlafviqvard82k6y8btq.c0.asia-southeast1.gcp.weaviate.cloud" weaviate_auth_config = weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY) weaviate_client = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate_auth_config) # 预训练模型配置 MODEL_NAME = "bert-base-chinese" device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model = AutoModel.from_pretrained(MODEL_NAME) # OpenAI 客户端 openai_client = None def initialize_openai_client(api_key): global openai_client openai_client = OpenAI(api_key=api_key) def extract_keywords(text): prompt = """ 你是一个关键词提取机器人。提取用户输入中的关键词，特别是名词和形容词，关键词之间用空格分隔。例如：苹果 电脑 裤子 蓝色 裙。 """ messages = [ {"role": "system", "content": prompt}, {"role": "user", "content": f"从下面的文本中提取五个关键词，以空格分隔：{text}"} ] response = openai_client.chat.completions.create( model="gpt-3.5-turbo", messages=messages, max_tokens=100, temperature=0.7, top_p=0.9, ) keywords = response.choices[0].message.content.split(' ') return ','.join(keywords) # def match_keywords(query_keywords, ad_keywords_list, triggered_keywords, current_turn, window_size, threshold): # best_match_distance = 0 # best_match_index = -1 # for i, ad_keywords in enumerate(ad_keywords_list): # match_count = sum( # any( # ad_keyword in keyword and # (keyword not in triggered_keywords or current_turn - triggered_keywords[keyword] > window_size) # ) for keyword in query_keywords # ) # if match_count > best_match_distance: # best_match_distance = match_count # best_match_index = i # if best_match_distance >= threshold: # for keyword in query_keywords: # if any(ad_keyword in keyword for ad_keyword in ad_keywords_list[best_match_index]): # triggered_keywords[keyword] = current_turn # return best_match_distance, best_match_index def encode_keywords_to_avg(keywords, model, tokenizer, device): embeddings = [] for keyword in tqdm(keywords): inputs = tokenizer(keyword, return_tensors='pt', padding=True, truncation=True, max_length=512) inputs.to(device) with torch.no_grad(): outputs = model(**inputs) embeddings.append(outputs.last_hidden_state.mean(dim=1)) avg_embedding = sum(embeddings) / len(embeddings) return avg_embedding def encode_keywords_to_list(keywords, model, tokenizer, device): embeddings = [] for keyword in tqdm(keywords): inputs = tokenizer(keyword, return_tensors='pt', padding=True, truncation=True, max_length=512) inputs.to(device) with torch.no_grad(): outputs = model(**inputs) embeddings.append(outputs.last_hidden_state.mean(dim=1).squeeze().tolist()) return embeddings def get_response_from_db(keywords_dict, class_name): avg_vec = encode_keywords_to_avg(keywords_dict.keys(), model, tokenizer, device).numpy() response = ( weaviate_client.query .get(class_name, ['keywords', 'summary']) .with_near_vector({'vector': avg_vec}) .with_limit(1) .with_additional(['distance']) .do() ) if class_name.capitalize() in response['data']['Get']: result = response['data']['Get'][class_name.capitalize()][0] return result['_additional']['distance'], result['summary'], result['keywords'] else: return None, None, None def get_candidates_from_db(keywords_dict, class_name,limit=3): embeddings= encode_keywords_to_list(keywords_dict.keys(), model, tokenizer, device) candidate_list=[] for embedding in embeddings: response = ( weaviate_client.query .get(class_name, ['group_id','keyword_list','keyword', 'summary']) .with_near_vector({'vector': embedding}) .with_limit(limit) .with_additional(['distance']) .do() ) class_name=class_name[0].upper()+class_name[1:] if class_name in response['data']['Get']: print(111) results = response['data']['Get'][class_name] for result in results: candidate_list.append({ 'distance': result['_additional']['distance'], 'group_id': result['group_id'], 'keyword_list':result['keyword_list'], 'summary': result['summary'], 'keyword': result['keyword'] }) return candidate_list triggered_keywords = {} def keyword_match(keywords_dict,candidates): for candidate in candidates: keywords=candidate['keywords'].split('*') candidate_keywords_list=[keyword.split('#')[1] for keyword in keywords if '#' in keyword] # print(keywords_dict.keys()) print(f"nowdebug candidatekeywordslist{candidate_keywords_list}") for keyword in keywords_dict.keys(): if any(candidate_keyword in keyword for candidate_keyword in candidate_keywords_list): # triggered_keywords[keyword]=True print(f"candidate_keyword{candidate_keywords_list},,,,,,,keyword{keyword}") return candidate['distance'],candidate['summary'],candidate['keywords'] return 1000,None,None def chatbot_response(message, history, window_size, threshold, score_threshold,user_weight, triggered_weight,candidate_length,api_key): #初始化openai client initialize_openai_client(api_key) #更新轮次，获取窗口历史 current_turn = len(history) + 1 combined_user_message = " ".join([h[0] for h in history[-window_size:]] + [message]) combined_assistant_message = " ".join([h[1] for h in history[-window_size:]]) #提取关键词 user_keywords = extract_keywords(combined_user_message).split(',') assistant_keywords = extract_keywords(combined_assistant_message).split(',') #获取关键词字典 keywords_dict = {keyword: user_weight for keyword in user_keywords} for keyword in assistant_keywords: keywords_dict[keyword] = keywords_dict.get(keyword, 0) + 1 for keyword in list(keywords_dict.keys()): if keyword in triggered_keywords and current_turn - triggered_keywords[keyword] < window_size: keywords_dict[keyword] = triggered_weight #数据库检索，双方平均方式 # distance, ad_summary, ad_keywords = get_response_from_db(keywords_dict, class_name="ad_DB02") #数据库索引，数据库关键词平均方式 candidates=get_candidates_from_db(keywords_dict, class_name="Ad_DB05",limit=candidate_length) # #对类别进行判断加权 # for candidate in candidates: # if candidate['keyword']!= candidate['keyword_list'].split(',')[0]: # candidate['distance']*=2 #先对候选集的distance进行筛选，保留小于threshold的 candidates.sort(key=lambda x:x['distance']) candidates=[candidate for candidate in candidates if candidate['distance']=score_threshold): distance,ad_summary,ad_keywords=[candidate['distance'],candidate['summary'],candidate['keyword_list'] for candidate in candidates if candidate['group_id']==max_group_id][0] else: distance=1000 # if(candidates): # # distance, ad_summary, ad_keywords=keyword_match(keywords_dict,candidates) # distance,ad_summary,ad_keywords=candidates[0]['distance'],candidates[0]['summary'],candidates[0]['keyword_list'] # else: # distance=1000 if distance and distance < 1000: brands=['腾讯','阿里巴巴','百度','京东','华为','小米','苹果','微软','谷歌','亚马逊'] brand=random.choice(brands) ad_message = f"{message} 品牌{brand}{ad_summary}" messages = [{"role": "system", "content": "你是一个热情的聊天机器人，应微妙地嵌入广告内容。"}] 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": ad_message}) for keyword in keywords_dict.keys(): if any(ad_keyword in keyword for ad_keyword in ad_keywords.split(',')): triggered_keywords[keyword] = current_turn else: messages = [{"role": "system", "content": "你是一个热情的聊天机器人。"}] 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}) #获取回复 response = openai_client.chat.completions.create( model="gpt-3.5-turbo", messages=messages, # max_tokens=max_tokens, # temperature=temperature, # top_p=top_p, ) print(f"triggered_keywords: {triggered_keywords}") return response.choices[0].message.content # Gradio UI demo = gr.ChatInterface( chatbot_response, additional_inputs=[ # 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)"), gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Window size"), gr.Slider(minimum=0.01, maximum=0.25, value=0.10, step=0.01, label="Distance threshold"), gr.Slider(minimum=1, maximum=10, value=3, step=1, label="Score threshold"), gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Weight of keywords from users"), gr.Slider(minimum=0, maximum=2, value=0.5, step=0.5, label="Weight of triggered keywords"), gr.Slider(minimum=1, maximum=5, value=3, step=1, label="Number of candidates"), gr.Textbox(label="API Key"), ], ) if __name__ == "__main__": demo.launch(share=True) print("happyhappyhappy") # import gradio as gr # from huggingface_hub import InferenceClient # import json # import random # import re # from load_data import load_data # from openai import OpenAI # from transformers import AutoTokenizer, AutoModel # import weaviate # import os # import subprocess # import torch # from tqdm import tqdm # import numpy as np # import time # # 设置 Matplotlib 的缓存目录 # os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib' # # 设置 Hugging Face Transformers 的缓存目录 # os.environ['TRANSFORMERS_CACHE'] = '/tmp/huggingface_cache' # # 确保这些目录存在 # os.makedirs(os.environ['MPLCONFIGDIR'], exist_ok=True) # os.makedirs(os.environ['TRANSFORMERS_CACHE'], exist_ok=True) # auth_config = weaviate.AuthApiKey(api_key="Y7c8DRmcxZ4nP5IJLwkznIsK84l6EdwfXwcH") # URL = "https://39nlafviqvard82k6y8btq.c0.asia-southeast1.gcp.weaviate.cloud" # # Connect to a WCS instance # db_client = weaviate.Client( # url=URL, # auth_client_secret=auth_config # ) # class_name="ad_DB02" # device = torch.device(device='cuda' if torch.cuda.is_available() else 'cpu') # tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") # model = AutoModel.from_pretrained("bert-base-chinese") # global_api_key = None # client = None # def initialize_clients(api_key): # global client # client = OpenAI(api_key=api_key) # def get_keywords(message): # system_message = """ # # 角色 # 你是一个关键词提取机器人 # # 指令 # 你的目标是从用户的输入中提取关键词，这些关键词应该尽可能是购买意图相关的。关键词中应该尽可能注意那些名词和形容词 # # 输出格式 # 你应该直接输出关键词，关键词之间用空格分隔。例如：苹果 电脑 裤子 蓝色 裙 # # 注意：如果输入文本过短可以重复输出关键词，例如对输入“你好”可以输出：你好 你好 你好 你好 你好 # """ # messages = [{"role": "system", "content": system_message}] # messages.append({"role": "user", "content": f"从下面的文本中给我提取五个关键词，只输出这五个关键词，以空格分隔{message}"}) # response = client.chat.completions.create( # model="gpt-3.5-turbo", # messages=messages, # max_tokens=100, # temperature=0.7, # top_p=0.9, # ) # keywords = response.choices[0].message.content.split(' ') # return ','.join(keywords) # #字符串匹配模块 # def keyword_match(query_keywords_dict, ad_keywords_lists, triggered_keywords, current_turn, window_size,distance_threshold): # distance = 0 # most_matching_list = None # index = 0 # # query_keywords = query_keywords.split(',') # # query_keywords = [keyword for keyword in query_keywords if keyword] # #匹配模块 # query_keywords= list(query_keywords_dict.keys()) # for i, lst in enumerate(ad_keywords_lists): # lst = lst.split(',') # matches = sum( # any( # ad_keyword in keyword and # ( # keyword not in triggered_keywords or # triggered_keywords.get(keyword) is None or # current_turn - triggered_keywords.get(keyword, 0) > window_size # ) * query_keywords_dict.get(keyword, 1) #计数乘以权重 # for keyword in query_keywords # ) # for ad_keyword in lst # ) # if matches > distance: # distance = matches # most_matching_list = lst # index = i # #更新对distance 有贡献的关键词 # if distance >= distance_threshold: # for keyword in query_keywords: # if any( # ad_keyword in keyword for ad_keyword in most_matching_list # ): # triggered_keywords[keyword] = current_turn # return distance, index # def encode_list_to_avg(keywords_list_list, model, tokenizer, device): # if torch.cuda.is_available(): # print('Using GPU') # print(device) # else: # print('Using CPU') # print(device) # avg_embeddings = [] # for keywords in tqdm(keywords_list_list): # keywords_lst=[] # # keywords.split(',') # for keyword in keywords: # inputs = tokenizer(keyword, return_tensors='pt', padding=True, truncation=True, max_length=512) # inputs.to(device) # with torch.no_grad(): # outputs = model(**inputs) # embeddings = outputs.last_hidden_state.mean(dim=1) # keywords_lst.append(embeddings) # avg_embedding = sum(keywords_lst) / len(keywords_lst) # avg_embeddings.append(avg_embedding) # return avg_embeddings # def encode_to_avg(keywords_dict, model, tokenizer, device): # if torch.cuda.is_available(): # print('Using GPU') # print(device) # else: # print('Using CPU') # print(device) # keyword_embeddings=[] # for keyword, weight in keywords_dict.items(): # inputs = tokenizer(keyword, return_tensors='pt', padding=True, truncation=True, max_length=512) # inputs.to(device) # with torch.no_grad(): # outputs = model(**inputs) # embedding = outputs.last_hidden_state.mean(dim=1) # keyword_embedding=embedding * weight # keyword_embeddings.append(keyword_embedding * weight) # avg_embedding = sum(keyword_embeddings) / sum(keywords_dict.values()) # return avg_embedding.tolist() # def fetch_response_from_db(query_keywords_dict,class_name): # start_time = time.time() # avg_vec=np.array(encode_to_avg(query_keywords_dict, model, tokenizer, device)) # end_time = time.time() # print(f"Time taken to encode to avg: {end_time - start_time}") # nearVector = { # 'vector': avg_vec # } # start_time = time.time() # response = ( # db_client.query # .get(class_name, ['keywords', 'summary']) # .with_near_vector(nearVector) # .with_limit(1) # .with_additional(['distance']) # .do() # ) # end_time = time.time() # print(f"Time taken to search in DB: {end_time - start_time}") # print(response) # class_name=class_name[0].upper()+class_name[1:] # if class_name in response['data']['Get']: # results = response['data']['Get'][class_name] # print(results[0]['keywords']) # return results[0]['_additional']['distance'],results[0]['summary'], results[0]['keywords'] # else: # print(f"Class name {class_name} not found in response") # return None # def wrapper(message, history, max_tokens, temperature, top_p, window_size, distance_threshold, weight_keywords_users, weight_keywords_triggered, api_key): # initialize_clients(api_key) # return respond(message, history, max_tokens, temperature, top_p, window_size, distance_threshold, weight_keywords_users, weight_keywords_triggered) # #触发词及触发回合字典 # triggered_keywords = {} # def respond( # message, # history: list[tuple[str, str]], # max_tokens, # temperature, # top_p, # window_size, # distance_threshold, # weight_keywords_users, # weight_keywords_triggered, # ): # system_message_with_ad = """ # # 角色 # 你是一个热情的聊天机器人 # # 指令 # 你的目标是以一种微妙的方式将给定的广告内容嵌入对话中。插入广告时应尽量自然，避免突兀。优先正常回答用户问题，然后逐步以推荐方式引入广告内容。 # 注意要在商品的描述前面加上是来自哪个品牌的广告。 # 注意在推荐中不要脑补用户的身份，只是进行简单推荐。 # 注意要热情但是语气只要适度热情 # # 输入格式 # 用户查询后跟随广告品牌，用分隔，广告品牌后跟随广告描述，再用分隔。 # 例如：我想买一条阔腿裤 腾讯 宽松的阔腿裤这两年真的吸粉不少，明星时尚达人的心头爱。毕竟好穿时尚，谁都能穿出腿长2米的效果宽松的裤腿，当然是遮肉小能手啊。上身随性自然不拘束，面料亲肤舒适贴身体验感棒棒哒。系带部分增加设计看点，还让单品的设计感更强。腿部线条若隐若现的，性感撩人。颜色敲温柔的，与裤子本身所呈现的风格有点反差萌。 # 注意: 当没有时，正常回复用户，不插入广告。 # # 输出格式 # 始终使用中文，只输出聊天内容，不输出任何自我分析的信息 # """ # system_message_without_ad = """ # 你是一个热情的聊天机器人 # """ # print(f"triggered_keywords{triggered_keywords}") # # 更新当前轮次 # current_turn = len(history) + 1 # print(f"\ncurrent_turn: {current_turn}\n") # # 检查历史记录的长度 # if len(history) >= window_size: # combined_message_user = " ".join([h[0] for h in history[-window_size:] if h[0]] + [message]) # combined_message_assistant=" ".join(h[1] for h in history[-window_size:] if h[1]) # else: # combined_message_user = message # combined_message_assistant = "" # start_time = time.time() # key_words_users=get_keywords(combined_message_user).split(',') # key_words_assistant=get_keywords(combined_message_assistant).split(',') # end_time = time.time() # print(f"Time taken to get keywords: {end_time - start_time}") # print(f"Initial keywords_users: {key_words_users}") # print(f"Initial keywords_assistant: {key_words_assistant}") # keywords_dict = {} # added_keywords = set() # for keywords in key_words_users: # if keywords not in added_keywords: # if keywords in keywords_dict: # keywords_dict[keywords] += weight_keywords_users # else: # keywords_dict[keywords] = weight_keywords_users # added_keywords.add(keywords) # for keywords in key_words_assistant: # if keywords not in added_keywords: # if keywords in keywords_dict: # keywords_dict[keywords] += 1 # else: # keywords_dict[keywords] = 1 # added_keywords.add(keywords) # #窗口内触发过的关键词权重下调为0.5 # for keyword in list(keywords_dict.keys()): # if keyword in triggered_keywords: # if current_turn - triggered_keywords[keyword] < window_size: # keywords_dict[keyword] = weight_keywords_triggered # query_keywords = list(keywords_dict.keys()) # print(keywords_dict) # start_time = time.time() # distance,top_keywords_list,top_summary = fetch_response_from_db(keywords_dict,class_name) # end_time = time.time() # print(f"Time taken to fetch response from db: {end_time - start_time}") # print(f"distance: {distance}") # if distance{brand}的 {ad}"}) # #更新触发词 # for keyword in query_keywords: # if any( # ad_keyword in keyword for ad_keyword in top_keywords_list # ): # triggered_keywords[keyword] = current_turn # else: # messages = [{"role": "system", "content": system_message_without_ad}] # 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}) # start_time = time.time() # response = client.chat.completions.create( # model="gpt-3.5-turbo", # messages=messages, # max_tokens=max_tokens, # temperature=temperature, # top_p=top_p, # ) # end_time = time.time() # print(f"Time taken to get response from GPT: {end_time - start_time}") # return response.choices[0].message.content # # def chat_interface(message, history, max_tokens, temperature, top_p, window_size, distance_threshold): # # global triggered_keywords # # response, triggered_keywords = respond( # # message, # # history, # # max_tokens, # # temperature, # # top_p, # # window_size, # # distance_threshold, # # triggered_keywords # # ) # # return response, history + [(message, response)] # demo = gr.ChatInterface( # wrapper, # additional_inputs=[ # 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)", # ), # gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Window size"), # gr.Slider(minimum=0.01, maximum=0.20, value=0.08, step=0.01, label="Distance threshold"), # gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Weight of keywords from users"), # gr.Slider(minimum=0, maximum=2, value=0.5, step=0.5, label="Weight of triggered keywords"), # gr.Textbox(label="api_key"), # ], # ) # if __name__ == "__main__": # demo.launch(share=True) # import gradio as gr # from huggingface_hub import InferenceClient # import json # import random # import re # from load_data import load_data # from openai import OpenAI # from transformers import AutoTokenizer, AutoModel # import weaviate # import os # import subprocess # import torch # from tqdm import tqdm # import numpy as np # # 设置 Matplotlib 和 Hugging Face Transformers 的缓存目录 # os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib' # os.environ['TRANSFORMERS_CACHE'] = '/tmp/huggingface_cache' # os.makedirs(os.environ['MPLCONFIGDIR'], exist_ok=True) # os.makedirs(os.environ['TRANSFORMERS_CACHE'], exist_ok=True) # auth_config = weaviate.AuthApiKey(api_key="Y7c8DRmcxZ4nP5IJLwkznIsK84l6EdwfXwcH") # URL = "https://39nlafviqvard82k6y8btq.c0.asia-southeast1.gcp.weaviate.cloud" # # Connect to a WCS instance # db_client = weaviate.Client( # url=URL, # auth_client_secret=auth_config # ) # class_name = "ad_DB02" # device = torch.device(device='cuda' if torch.cuda.is_available() else 'cpu') # tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese") # model = AutoModel.from_pretrained("bert-base-chinese") # global_api_key = None # client = None # def initialize_clients(api_key): # global client # client = OpenAI(api_key=api_key) # def get_keywords(message): # system_message = """ # # 角色 # 你是一个关键词提取机器人 # # 指令 # 你的目标是从用户的输入中提取关键词，这些关键词应该尽可能是购买意图相关的。关键词中应该尽可能注意那些名词和形容词 # # 输出格式 # 你应该直接输出关键词，关键词之间用空格分隔。例如：苹果 电脑 裤子 蓝色 裙 # # 注意：如果输入文本过短可以重复输出关键词，例如对输入“你好”可以输出：你好 你好 你好 你好 你好 # """ # messages = [{"role": "system", "content": system_message}] # messages.append({"role": "user", "content": f"从下面的文本中给我提取五个关键词，只输出这五个关键词，以空格分隔{message}"}) # response = client.chat.completions.create( # model="gpt-3.5-turbo", # messages=messages, # max_tokens=100, # temperature=0.7, # top_p=0.9, # ) # keywords = response.choices[0].message.content.split(' ') # return ','.join(keywords) # def fetch_response_from_db(query_keywords_dict, class_name): # avg_vec = np.array(encode_to_avg(query_keywords_dict, model, tokenizer, device)) # nearVector = {'vector': avg_vec} # response = ( # db_client.query # .get(class_name, ['keywords', 'summary']) # .with_near_vector(nearVector) # .with_limit(1) # .with_additional(['distance']) # .do() # ) # class_name = class_name[0].upper() + class_name[1:] # if class_name in response['data']['Get']: # results = response['data']['Get'][class_name] # return results[0]['_additional']['distance'], results[0]['summary'], results[0]['keywords'] # else: # print(f"Class name {class_name} not found in response") # return None # def encode_to_avg(keywords_dict, model, tokenizer, device): # if torch.cuda.is_available(): # print('Using GPU') # print(device) # else: # print('Using CPU') # print(device) # keyword_embeddings = [] # for keyword, weight in keywords_dict.items(): # inputs = tokenizer(keyword, return_tensors='pt', padding=True, truncation=True, max_length=512) # inputs.to(device) # with torch.no_grad(): # outputs = model(**inputs) # embedding = outputs.last_hidden_state.mean(dim=1) # keyword_embedding = embedding * weight # keyword_embeddings.append(keyword_embedding) # avg_embedding = sum(keyword_embeddings) / sum(keywords_dict.values()) # return avg_embedding.tolist() # def wrapper(message, history, max_tokens, temperature, top_p, window_size, distance_threshold, weight_keywords_users, weight_keywords_triggered, api_key, state): # initialize_clients(api_key) # return respond(message, history, max_tokens, temperature, top_p, window_size, distance_threshold, weight_keywords_users, weight_keywords_triggered, state) # def respond( # message, # history, # max_tokens, # temperature, # top_p, # window_size, # distance_threshold, # weight_keywords_users, # weight_keywords_triggered, # state # ): # triggered_keywords = state.get('triggered_keywords', {}) # current_turn = len(history) + 1 # if len(history) >= window_size: # combined_message_user = " ".join([h[0] for h in history[-window_size:] if h[0]] + [message]) # combined_message_assistant = " ".join(h[1] for h in history[-window_size:] if h[1]) # else: # combined_message_user = message # combined_message_assistant = "" # key_words_users = get_keywords(combined_message_user).split(',') # key_words_assistant = get_keywords(combined_message_assistant).split(',') # keywords_dict = {} # for keyword in key_words_users: # if keyword in keywords_dict: # keywords_dict[keyword] += weight_keywords_users # else: # keywords_dict[keyword] = weight_keywords_users # for keyword in key_words_assistant: # if keyword in keywords_dict: # keywords_dict[keyword] += 1 # else: # keywords_dict[keyword] = 1 # for keyword in list(keywords_dict.keys()): # if keyword in triggered_keywords: # if current_turn - triggered_keywords[keyword] < window_size: # keywords_dict[keyword] = weight_keywords_triggered # query_keywords = list(keywords_dict.keys()) # distance, top_keywords_list, top_summary = fetch_response_from_db(keywords_dict, class_name) # if distance < distance_threshold: # ad = top_summary # messages = [{"role": "system", "content": system_message_with_ad}] # for val in history: # if val[0]: # messages.append({"role": "user", "content": val[0]}) # if val[1]: # messages.append({"role": "assistant", "content": val[1]}) # brands = ['腾讯', '百度', '京东', '华为', '小米', '苹果', '微软', '谷歌', '亚马逊'] # brand = random.choice(brands) # messages.append({"role": "user", "content": f"{message} {brand}的 {ad}"}) # for keyword in query_keywords: # if any(ad_keyword in keyword for ad_keyword in top_keywords_list): # triggered_keywords[keyword] = current_turn # else: # messages = [{"role": "system", "content": system_message_without_ad}] # 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 = client.chat.completions.create( # model="gpt-3.5-turbo", # messages=messages, # max_tokens=max_tokens, # temperature=temperature, # top_p=top_p, # ) # state['triggered_keywords'] = triggered_keywords # return response.choices[0].message.content, state # demo = gr.ChatInterface( # wrapper, # additional_inputs=[ # 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)"), # gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Window size"), # gr.Slider(minimum=0.01, maximum=0.20, value=0.08, step=0.01, label="Distance threshold"), # gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Weight of keywords from users"), # gr.Slider(minimum=0, maximum=2, value=0.5, step=0.5, label="Weight of triggered keywords"), # gr.Textbox(label="api_key"), # gr.State(value="state") # ], # ) # if __name__ == "__main__": # demo.launch(share=True)