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| # -*- coding:utf-8 -*- | |
| from __future__ import annotations | |
| from typing import TYPE_CHECKING, List | |
| import logging | |
| import json | |
| import os | |
| import requests | |
| from tqdm import tqdm | |
| from utils import * | |
| if TYPE_CHECKING: | |
| from typing import TypedDict | |
| class DataframeData(TypedDict): | |
| headers: List[str] | |
| data: List[List[str | int | bool]] | |
| initial_prompt = "You are a helpful assistant." | |
| API_URL = "https://api.openai.com/v1/chat/completions" | |
| def get_response( | |
| openai_api_key, system_prompt, history, stream, selected_model | |
| ): | |
| headers = { | |
| "Content-Type": "application/json", | |
| "Authorization": f"Bearer {openai_api_key}", | |
| } | |
| history = [construct_system(system_prompt), *history] | |
| payload = { | |
| "model": selected_model, | |
| "messages": history, # [{"role": "user", "content": f"{inputs}"}], | |
| "temperature": 1.0, # 1.0, | |
| "top_p": 1.0, # 1.0, | |
| "n": 1, | |
| "stream": stream, | |
| "presence_penalty": 0, | |
| "frequency_penalty": 0, | |
| } | |
| if stream: | |
| timeout = timeout_streaming | |
| else: | |
| timeout = timeout_all | |
| # 获取环境变量中的代理设置 | |
| http_proxy = os.environ.get("HTTP_PROXY") or os.environ.get("http_proxy") | |
| https_proxy = os.environ.get("HTTPS_PROXY") or os.environ.get("https_proxy") | |
| # 如果存在代理设置，使用它们 | |
| proxies = {} | |
| if http_proxy: | |
| logging.info(f"Using HTTP proxy: {http_proxy}") | |
| proxies["http"] = http_proxy | |
| if https_proxy: | |
| logging.info(f"Using HTTPS proxy: {https_proxy}") | |
| proxies["https"] = https_proxy | |
| # 如果有代理，使用代理发送请求，否则使用默认设置发送请求 | |
| if proxies: | |
| response = requests.post( | |
| API_URL, | |
| headers=headers, | |
| json=payload, | |
| stream=True, | |
| timeout=timeout, | |
| proxies=proxies, | |
| ) | |
| else: | |
| response = requests.post( | |
| API_URL, | |
| headers=headers, | |
| json=payload, | |
| stream=True, | |
| timeout=timeout, | |
| ) | |
| return response | |
| def stream_predict( | |
| openai_api_key, | |
| system_prompt, | |
| history, | |
| inputs, | |
| chatbot, | |
| all_token_counts, | |
| selected_model, | |
| fake_input=None, | |
| display_append="" | |
| ): | |
| def get_return_value(): | |
| return chatbot, history, status_text, all_token_counts | |
| # logging.info("实时回答模式") | |
| partial_words = "" | |
| counter = 0 | |
| status_text = "answering……" | |
| history.append(construct_user(inputs)) | |
| history.append(construct_assistant("")) | |
| if fake_input: | |
| chatbot.append((fake_input, "")) | |
| else: | |
| chatbot.append((inputs, "")) | |
| user_token_count = 0 | |
| if len(all_token_counts) == 0: | |
| system_prompt_token_count = count_token(construct_system(system_prompt)) | |
| user_token_count = ( | |
| count_token(construct_user(inputs)) + system_prompt_token_count | |
| ) | |
| else: | |
| user_token_count = count_token(construct_user(inputs)) | |
| all_token_counts.append(user_token_count) | |
| logging.info(f"input token count: {user_token_count}") | |
| yield get_return_value() | |
| try: | |
| response = get_response( | |
| openai_api_key, | |
| system_prompt, | |
| history, | |
| True, | |
| selected_model, | |
| ) | |
| except requests.exceptions.ConnectTimeout: | |
| status_text = ( | |
| standard_error_msg + connection_timeout_prompt + error_retrieve_prompt | |
| ) | |
| yield get_return_value() | |
| return | |
| except requests.exceptions.ReadTimeout: | |
| status_text = standard_error_msg + read_timeout_prompt + error_retrieve_prompt | |
| yield get_return_value() | |
| return | |
| yield get_return_value() | |
| error_json_str = "" | |
| for chunk in tqdm(response.iter_lines()): | |
| if counter == 0: | |
| counter += 1 | |
| continue | |
| counter += 1 | |
| # check whether each line is non-empty | |
| if chunk: | |
| chunk = chunk.decode() | |
| chunklength = len(chunk) | |
| try: | |
| chunk = json.loads(chunk[6:]) | |
| except json.JSONDecodeError: | |
| logging.info(chunk) | |
| error_json_str += chunk | |
| status_text = f"JSON file parsing error. Please reset the conversation. received content: {error_json_str}" | |
| yield get_return_value() | |
| continue | |
| # decode each line as response data is in bytes | |
| if chunklength > 6 and "delta" in chunk["choices"][0]: | |
| finish_reason = chunk["choices"][0]["finish_reason"] | |
| status_text = construct_token_message( | |
| sum(all_token_counts), stream=True | |
| ) | |
| if finish_reason == "stop": | |
| yield get_return_value() | |
| break | |
| try: | |
| partial_words = ( | |
| partial_words + chunk["choices"][0]["delta"]["content"] | |
| ) | |
| except KeyError: | |
| status_text = ( | |
| standard_error_msg | |
| + "Token count has reached the maxtoken limit. Please reset the conversation. Current Token Count: " | |
| + str(sum(all_token_counts)) | |
| ) | |
| yield get_return_value() | |
| break | |
| history[-1] = construct_assistant(partial_words) | |
| chatbot[-1] = (chatbot[-1][0], partial_words+display_append) | |
| all_token_counts[-1] += 1 | |
| yield get_return_value() | |
| def predict_all( | |
| openai_api_key, | |
| system_prompt, | |
| history, | |
| inputs, | |
| chatbot, | |
| all_token_counts, | |
| selected_model, | |
| fake_input=None, | |
| display_append="" | |
| ): | |
| # logging.info("一次性回答模式") | |
| history.append(construct_user(inputs)) | |
| history.append(construct_assistant("")) | |
| if fake_input: | |
| chatbot.append((fake_input, "")) | |
| else: | |
| chatbot.append((inputs, "")) | |
| all_token_counts.append(count_token(construct_user(inputs))) | |
| try: | |
| response = get_response( | |
| openai_api_key, | |
| system_prompt, | |
| history, | |
| False, | |
| selected_model, | |
| ) | |
| except requests.exceptions.ConnectTimeout: | |
| status_text = ( | |
| standard_error_msg + connection_timeout_prompt + error_retrieve_prompt | |
| ) | |
| return chatbot, history, status_text, all_token_counts | |
| except requests.exceptions.ProxyError: | |
| status_text = standard_error_msg + proxy_error_prompt + error_retrieve_prompt | |
| return chatbot, history, status_text, all_token_counts | |
| except requests.exceptions.SSLError: | |
| status_text = standard_error_msg + ssl_error_prompt + error_retrieve_prompt | |
| return chatbot, history, status_text, all_token_counts | |
| response = json.loads(response.text) | |
| content = response["choices"][0]["message"]["content"] | |
| history[-1] = construct_assistant(content) | |
| chatbot[-1] = (chatbot[-1][0], content+display_append) | |
| total_token_count = response["usage"]["total_tokens"] | |
| all_token_counts[-1] = total_token_count - sum(all_token_counts) | |
| status_text = construct_token_message(total_token_count) | |
| return chatbot, history, status_text, all_token_counts | |
| def predict( | |
| openai_api_key, | |
| system_prompt, | |
| history, | |
| inputs, | |
| chatbot, | |
| all_token_counts, | |
| stream=True, | |
| selected_model=MODELS[0], | |
| use_websearch=False, | |
| files = None, | |
| should_check_token_count=True, | |
| ): # repetition_penalty, top_k | |
| old_inputs = "" | |
| link_references = "" | |
| if len(openai_api_key) != 51: | |
| status_text = standard_error_msg + no_apikey_msg | |
| logging.info(status_text) | |
| chatbot.append((inputs, "")) | |
| if len(history) == 0: | |
| history.append(construct_user(inputs)) | |
| history.append("") | |
| all_token_counts.append(0) | |
| else: | |
| history[-2] = construct_user(inputs) | |
| yield chatbot, history, status_text, all_token_counts | |
| return | |
| yield chatbot, history, "answering……", all_token_counts | |
| if stream: | |
| # logging.info("使用流式传输") | |
| iter = stream_predict( | |
| openai_api_key, | |
| system_prompt, | |
| history, | |
| inputs, | |
| chatbot, | |
| all_token_counts, | |
| selected_model, | |
| fake_input=old_inputs, | |
| display_append=link_references | |
| ) | |
| for chatbot, history, status_text, all_token_counts in iter: | |
| yield chatbot, history, status_text, all_token_counts | |
| else: | |
| # logging.info("不使用流式传输") | |
| chatbot, history, status_text, all_token_counts = predict_all( | |
| openai_api_key, | |
| system_prompt, | |
| history, | |
| inputs, | |
| chatbot, | |
| all_token_counts, | |
| selected_model, | |
| fake_input=old_inputs, | |
| display_append=link_references | |
| ) | |
| yield chatbot, history, status_text, all_token_counts | |
| logging.info(f"The current token count is{all_token_counts}") | |