from helper import * import streamlit as st import uuid import copy import pandas as pd import openai from requests.models import ChunkedEncodingError from streamlit.components import v1 from custom import css_code, js_code, set_context_all from st_audiorec import st_audiorec import numpy as np import wave device = "cpu" # STT import whisper WHISPER_LANG = "en" # detecting language if None #warnings.filterwarnings("ignore") WHISPER_MODEL = whisper.load_model("base") WHISPER_MODEL.to(device) def transcribe(aud_inp): if aud_inp is None: return "" aud = whisper.load_audio(aud_inp) aud = whisper.pad_or_trim(aud) mel = whisper.log_mel_spectrogram(aud).to(device) _, probs = WHISPER_MODEL.detect_language(mel) print(f"spectrogram.shape = {mel}") if device == "cpu": options = whisper.DecodingOptions(fp16 = False, language=WHISPER_LANG) else: options = whisper.DecodingOptions(language=WHISPER_LANG) result = whisper.decode(WHISPER_MODEL, mel, options) print("result.text", result.text) result_text = "" if result and result.text: result_text = result.text return result_text def bytes_to_wav(wav_bytes, output_wav_file = "output.wav", sample_width = 1, # 1 byte per sample sample_rate = 44100, # Sample rate in Hz num_channels = 1, # Mono audio ): # Create a WAV file in pcm_s16le format using the wave module with wave.open(output_wav_file, 'wb') as wav_file: wav_file.setnchannels(num_channels) wav_file.setsampwidth(sample_width) wav_file.setframerate(sample_rate) wav_file.setcomptype('NONE', 'not compressed') # Write the audio data from wav_bytes to the WAV file wav_file.writeframes(wav_bytes) ###################################################################################### st.set_page_config(page_title='ChatGPT Assistant', layout='wide', page_icon='🤖') # 自定义元素样式 st.markdown(css_code, unsafe_allow_html=True) if "initial_settings" not in st.session_state: # 历史聊天窗口 st.session_state["path"] = set_chats_path() st.session_state['history_chats'] = get_history_chats(st.session_state["path"]) # ss参数初始化 st.session_state['pre_chat'] = None st.session_state['if_chat_change'] = False st.session_state['error_info'] = '' st.session_state["current_chat_index"] = 0 st.session_state['user_input_content'] = '' # 设置完成 st.session_state["initial_settings"] = True with st.sidebar: st.markdown("# 🤖 聊天窗口") current_chat = st.radio( label='历史聊天窗口', format_func=lambda x: x.split('_')[0] if '_' in x else x, options=st.session_state['history_chats'], label_visibility='collapsed', index=st.session_state["current_chat_index"], key='current_chat' + st.session_state['history_chats'][st.session_state["current_chat_index"]], # on_change=current_chat_callback # 此处不适合用回调，无法识别到窗口增减的变动 ) if st.session_state['pre_chat'] != current_chat: st.session_state['pre_chat'] = current_chat st.session_state['if_chat_change'] = True st.write("---") c1, c2 = st.columns(2) create_chat_button = c1.button('新建', use_container_width=True, key='create_chat_button') if create_chat_button: st.session_state['history_chats'] = ['New Chat_' + str(uuid.uuid4())] + st.session_state['history_chats'] st.session_state["current_chat_index"] = 0 st.experimental_rerun() delete_chat_button = c2.button('删除', use_container_width=True, key='delete_chat_button') if delete_chat_button: if len(st.session_state['history_chats']) == 1: chat_init = 'New Chat_' + str(uuid.uuid4()) st.session_state['history_chats'].append(chat_init) st.session_state['current_chat'] = chat_init pre_chat_index = st.session_state['history_chats'].index(current_chat) if pre_chat_index > 0: st.session_state["current_chat_index"] = st.session_state['history_chats'].index(current_chat) - 1 else: st.session_state["current_chat_index"] = 0 st.session_state['history_chats'].remove(current_chat) remove_data(st.session_state["path"], current_chat) st.experimental_rerun() for i in range(5): st.write("\n") st.markdown("OpenAI API Key") st.text_input("OpenAI API Key", type='password', key='apikey_input', label_visibility='collapsed') st.caption( "此Key仅在当前网页有效，且优先级高于Secrets中的配置，仅自己可用，他人无法共享。[官网获取](https://platform.openai.com/account/api-keys)") st.caption(""" - 双击页面可直接定位输入栏 - Ctrl + Enter 可快捷提交问题 """) # 加载数据 if ("history" + current_chat not in st.session_state) or (st.session_state['if_chat_change']): for key, value in load_data(st.session_state["path"], current_chat).items(): if key == 'history': st.session_state[key + current_chat] = value else: for k, v in value.items(): st.session_state[k + current_chat + 'default'] = v st.session_state['if_chat_change'] = False # 对话展示 show_messages(st.session_state["history" + current_chat]) # 数据写入文件 def write_data(new_chat_name=current_chat): # 防止高频创建时组件尚未渲染完成，不影响正常写入 if "frequency_penalty" + current_chat in st.session_state: st.session_state["paras"] = { "model": st.session_state["model" + current_chat], "temperature": st.session_state["temperature" + current_chat], "top_p": st.session_state["top_p" + current_chat], "presence_penalty": st.session_state["presence_penalty" + current_chat], "frequency_penalty": st.session_state["frequency_penalty" + current_chat], } st.session_state["contexts"] = { "context_select": st.session_state["context_select" + current_chat], "context_input": st.session_state["context_input" + current_chat], "context_level": st.session_state["context_level" + current_chat], } save_data(st.session_state["path"], new_chat_name, st.session_state["history" + current_chat], st.session_state["paras"], st.session_state["contexts"]) # 输入内容展示 area_user_svg = st.empty() area_user_content = st.empty() # 回复展示 area_gpt_svg = st.empty() area_gpt_content = st.empty() # 报错展示 area_error = st.empty() st.header('ChatGPT Assistant') tap_input, tap_context, tap_set = st.tabs(['💬 聊天', '🗒️ 预设', '⚙️ 设置']) with tap_context: set_context_list = list(set_context_all.keys()) context_select_index = set_context_list.index(st.session_state['context_select' + current_chat + "default"]) st.selectbox(label='选择上下文', options=set_context_list, key='context_select' + current_chat, index=context_select_index, on_change=write_data) st.caption(set_context_all[st.session_state['context_select' + current_chat]]) context_input = st.text_area(label='补充或自定义上下文：', key="context_input" + current_chat, value=st.session_state['context_input' + current_chat + "default"], on_change=write_data) st.caption(context_input) with tap_set: def clear_button_callback(): st.session_state['history' + current_chat] = copy.deepcopy(initial_content_history) write_data() st.button("清空聊天记录", use_container_width=True, on_click=clear_button_callback) st.markdown("包含对话次数：") st.slider("Context Level", 0, 10, st.session_state['context_level' + current_chat + "default"], 1, on_change=write_data, key='context_level' + current_chat, help="表示每次会话中包含的历史对话次数，预设内容不计算在内。") st.markdown("模型参数：") st.selectbox("Model", ["gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4", "gpt-4-32k"], index=0, help="[模型选择参考](https://platform.openai.com/docs/models)", on_change=write_data, key='model' + current_chat) st.slider("Temperature", 0.0, 2.0, st.session_state["temperature" + current_chat + "default"], 0.1, help="""在0和2之间，应该使用什么样的采样温度？较高的值（如0.8）会使输出更随机，而较低的值（如0.2）则会使其更加集中和确定性。 我们一般建议只更改这个参数或top_p参数中的一个，而不要同时更改两个。""", on_change=write_data, key='temperature' + current_chat) st.slider("Top P", 0.1, 1.0, st.session_state["top_p" + current_chat + "default"], 0.1, help="""一种替代采用温度进行采样的方法，称为“基于核心概率”的采样。在该方法中，模型会考虑概率最高的top_p个标记的预测结果。 因此，当该参数为0.1时，只有包括前10%概率质量的标记将被考虑。我们一般建议只更改这个参数或采样温度参数中的一个，而不要同时更改两个。""", on_change=write_data, key='top_p' + current_chat) st.slider("Presence Penalty", -2.0, 2.0, st.session_state["presence_penalty" + current_chat + "default"], 0.1, help="""该参数的取值范围为-2.0到2.0。正值会根据新标记是否出现在当前生成的文本中对其进行惩罚，从而增加模型谈论新话题的可能性。""", on_change=write_data, key='presence_penalty' + current_chat) st.slider("Frequency Penalty", -2.0, 2.0, st.session_state["frequency_penalty" + current_chat + "default"], 0.1, help="""该参数的取值范围为-2.0到2.0。正值会根据新标记在当前生成的文本中的已有频率对其进行惩罚，从而减少模型直接重复相同语句的可能性。""", on_change=write_data, key='frequency_penalty' + current_chat) st.caption("[官网参数说明](https://platform.openai.com/docs/api-reference/completions/create)") with tap_input: def input_callback(): if st.session_state['user_input_area'] != "": # 修改窗口名称 user_input_content = st.session_state['user_input_area'] elif st.session_state['audio_input'] != "": user_input_content = st.session_state['audio_input'] else: return df_history = pd.DataFrame(st.session_state["history" + current_chat]) if len(df_history.query('role!="system"')) == 0: remove_data(st.session_state["path"], current_chat) current_chat_index = st.session_state['history_chats'].index(current_chat) new_name = extract_chars(user_input_content, 18) + '_' + str(uuid.uuid4()) st.session_state['history_chats'][current_chat_index] = new_name st.session_state["current_chat_index"] = current_chat_index # 写入新文件 write_data(new_name) with st.form("input_form", clear_on_submit=True): user_input = st.text_area("**输入：**", key="user_input_area") submitted = st.form_submit_button("确认提交", use_container_width=True, on_click=input_callback) if submitted: st.session_state['user_input_content'] = user_input wav_audio_data = st_audiorec() if wav_audio_data is not None: st.audio(wav_audio_data, format='audio/wav') bytes_to_wav(wav_audio_data, output_wav_file='output.wav', sample_rate=16000) user_input = WHISPER_MODEL.transcribe('output.wav', language=WHISPER_LANG)['text'] st.session_state['audio_input'] = user_input st.session_state['user_input_content'] = user_input input_callback() if submitted or wav_audio_data is not None: st.write("User input: ", user_input) if st.session_state['user_input_content'] != '': if 'r' in st.session_state: st.session_state.pop("r") st.session_state[current_chat + 'report'] = "" st.session_state['pre_user_input_content'] = (remove_hashtag_right__space(st.session_state['user_input_content'] .replace('\n', '\n\n'))) st.session_state['user_input_content'] = '' show_each_message(st.session_state['pre_user_input_content'], 'user', [area_user_svg.markdown, area_user_content.markdown]) context_level_tem = st.session_state['context_level' + current_chat] history_tem = get_history_input(st.session_state["history" + current_chat], context_level_tem) + \ [{"role": "user", "content": st.session_state['pre_user_input_content']}] history_need_input = ([{"role": "system", "content": set_context_all[st.session_state['context_select' + current_chat]]}] + [{"role": "system", "content": st.session_state['context_input' + current_chat]}] + history_tem) paras_need_input = { "model": st.session_state["model" + current_chat], "temperature": st.session_state["temperature" + current_chat], "top_p": st.session_state["top_p" + current_chat], "presence_penalty": st.session_state["presence_penalty" + current_chat], "frequency_penalty": st.session_state["frequency_penalty" + current_chat], } with st.spinner("🤔"): try: if apikey := st.session_state['apikey_input']: openai.api_key = apikey else: openai.api_key = st.secrets["apikey"] r = openai.ChatCompletion.create(messages=history_need_input, stream=True, **paras_need_input) except (FileNotFoundError, KeyError): area_error.error("缺失 OpenAI API Key，请在复制项目后配置Secrets，或者在设置中进行临时配置。" "详情见[项目仓库](https://github.com/PierXuY/ChatGPT-Assistant)。") except openai.error.AuthenticationError: area_error.error("无效的 OpenAI API Key。") except openai.error.APIConnectionError as e: area_error.error("连接超时，请重试。报错： \n" + str(e.args[0])) except openai.error.InvalidRequestError as e: area_error.error("无效的请求，请重试。报错： \n" + str(e.args[0])) except openai.error.RateLimitError as e: area_error.error("请求速率过快，请重试。报错： \n" + str(e.args[0])) else: st.session_state["chat_of_r"] = current_chat st.session_state["r"] = r st.experimental_rerun() if ("r" in st.session_state) and (current_chat == st.session_state["chat_of_r"]): if current_chat + 'report' not in st.session_state: st.session_state[current_chat + 'report'] = "" try: for e in st.session_state["r"]: if "content" in e["choices"][0]["delta"]: st.session_state[current_chat + 'report'] += e["choices"][0]["delta"]["content"] show_each_message(st.session_state['pre_user_input_content'], 'user', [area_user_svg.markdown, area_user_content.markdown]) show_each_message(st.session_state[current_chat + 'report'], 'assistant', [area_gpt_svg.markdown, area_gpt_content.markdown]) except ChunkedEncodingError: area_error.error("网络状况不佳，请刷新页面重试。") # 应对stop情形 except Exception: pass else: # 保存内容 st.session_state["history" + current_chat].append( {"role": "user", "content": st.session_state['pre_user_input_content']}) st.session_state["history" + current_chat].append( {"role": "assistant", "content": st.session_state[current_chat + 'report']}) write_data() # 用户在网页点击stop时，ss某些情形下会暂时为空 if current_chat + 'report' in st.session_state: st.session_state.pop(current_chat + 'report') if 'r' in st.session_state: st.session_state.pop("r") # 添加事件监听 v1.html(js_code, height=0)