From 2e89aa3726a7f042e5b6f8cbe73841ad09159548 Mon Sep 17 00:00:00 2001 From: 1308100560 <1308100560@qq.com> Date: Mon, 29 Apr 2024 17:20:48 +0800 Subject: [PATCH 1/6] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E4=B8=89=E4=B8=AA?= =?UTF-8?q?=E7=95=8C=E9=9D=A2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 6_Speech_Recognition.py | 123 +++++++++++++++++++++++++++++++++++++ 7_Text_error_correction.py | 91 +++++++++++++++++++++++++++ 8_dialogue.py | 91 +++++++++++++++++++++++++++ 3 files changed, 305 insertions(+) create mode 100644 6_Speech_Recognition.py create mode 100644 7_Text_error_correction.py create mode 100644 8_dialogue.py diff --git a/6_Speech_Recognition.py b/6_Speech_Recognition.py new file mode 100644 index 000000000..ef2dacd24 --- /dev/null +++ b/6_Speech_Recognition.py @@ -0,0 +1,123 @@ +import streamlit as st +import anthropic +import ollama as ol +import streamlit as st +from streamlit_mic_recorder import speech_to_text +import datetime +import json + +def log_interaction(action, data): + timestamp = datetime.datetime.now().isoformat() + log = {"timestamp": timestamp, "action": action, "data": data} + with open("user_interactions_log.json", "a") as logfile: + logfile.write(json.dumps(log) + "\n") + +def language_selector(): + lang_options = ["ar", "de", "en", "es", "fr", "it", "ja", "nl", "pl", "pt", "ru", "zh"] + with st.sidebar: + return st.selectbox("Speech Language", ["en"] + lang_options) + +def print_txt(text): + if any("\u0600" <= c <= "\u06FF" for c in text): # check if text contains Arabic characters + text = f"

{text}

" + st.markdown(text, unsafe_allow_html=True) + +def print_chat_message(message): + text = message["content"] + if message["role"] == "user": + with st.chat_message("user", avatar="🎙️"): + print_txt(text) + elif message["role"] == "assistant": + with st.chat_message("assistant", avatar="🦙"): + print_txt(text) + +def get_chat_history(key): + return st.session_state.chat_history[key] + +def init_chat_history(key, system_prompt): + if "chat_history" not in st.session_state: + st.session_state.chat_history = {} + if key not in st.session_state.chat_history: + st.session_state.chat_history[key] = [{"role": "system", "content": system_prompt}] + +def system_prompt_input(default_prompt): + return st.sidebar.text_area("System Prompt", value=default_prompt, height=100) + +def record_voice(language="en"): + # https://github.com/B4PT0R/streamlit-mic-recorder?tab=readme-ov-file#example + + state = st.session_state + + if "text_received" not in state: + state.text_received = [] + + text = speech_to_text( + start_prompt="🎤 Click and speak to ask question", + stop_prompt="⚠️Stop recording🚨", + language=language, + use_container_width=True, + just_once=True, + ) + + if text: + state.text_received.append(text) + + result = "" + for text in state.text_received: + result += text + + state.text_received = [] + + return result if result else None + +def llm_selector(): + ollama_models = [m['name'] for m in ol.list()['models']] + with st.sidebar: + return st.selectbox("LLM", ollama_models) + + + +st.title("🎙 语音识别") + + +model = llm_selector() +chat_key = f"语音识别_chat_history_{model}" # Unique key for each mode and model +default_prompt = "你是一名语音识别助手，请把语音识别出的文字加上标点符号输出，不得改变原文。" + +system_prompt = system_prompt_input(default_prompt) +init_chat_history(chat_key, system_prompt) +chat_history = get_chat_history(chat_key) +for message in chat_history: + print_chat_message(message) + +question = record_voice(language=language_selector()) + +debug_mode = st.sidebar.checkbox("Debug Mode", value=True) +log_interaction("User input", {"mode": "语音识别", "question": question}) + +if question: + prompt = f"""{anthropic.HUMAN_PROMPT} Here's an article:\n\n
+ {question}\n\n
\n\n{question}{anthropic.AI_PROMPT}""" + + if question: + user_message = {"role": "user", "content": question} + # if app_mode == "语音识别": + print_chat_message(user_message) + chat_history.append(user_message) + response = ol.chat(model=model, messages=chat_history) + answer = response['message']['content'] + ai_message = {"role": "assistant", "content": answer} + print_chat_message(ai_message) + chat_history.append(ai_message) + debug_info = {"messages": chat_history, "response": response} + + if debug_mode: + st.write("Debug Info: Complete Prompt Interaction") + st.json(debug_info) + + # truncate chat history to keep 20 messages max + if len(chat_history) > 20: + chat_history = chat_history[-20:] + + # update chat history + st.session_state.chat_history[chat_key] = chat_history diff --git a/7_Text_error_correction.py b/7_Text_error_correction.py new file mode 100644 index 000000000..d9127afab --- /dev/null +++ b/7_Text_error_correction.py @@ -0,0 +1,91 @@ +import streamlit as st +import anthropic +import ollama as ol +import streamlit as st +from streamlit_mic_recorder import speech_to_text +import datetime +import json + +def log_interaction(action, data): + timestamp = datetime.datetime.now().isoformat() + log = {"timestamp": timestamp, "action": action, "data": data} + with open("user_interactions_log.json", "a") as logfile: + logfile.write(json.dumps(log) + "\n") + +def print_txt(text): + if any("\u0600" <= c <= "\u06FF" for c in text): # check if text contains Arabic characters + text = f"

{text}

" + st.markdown(text, unsafe_allow_html=True) + +def print_chat_message(message): + text = message["content"] + if message["role"] == "user": + with st.chat_message("user", avatar="🎙️"): + print_txt(text) + elif message["role"] == "assistant": + with st.chat_message("assistant", avatar="🦙"): + print_txt(text) + +def get_chat_history(key): + return st.session_state.chat_history[key] + +def init_chat_history(key, system_prompt): + if "chat_history" not in st.session_state: + st.session_state.chat_history = {} + if key not in st.session_state.chat_history: + st.session_state.chat_history[key] = [{"role": "system", "content": system_prompt}] + +def system_prompt_input(default_prompt): + return st.sidebar.text_area("System Prompt", value=default_prompt, height=100) + +def llm_selector(): + ollama_models = [m['name'] for m in ol.list()['models']] + with st.sidebar: + return st.selectbox("LLM", ollama_models) + + + +st.title("📝 文本纠错") + + +model = llm_selector() +chat_key = f"文本纠错_chat_history_{model}" # Unique key for each mode and model +default_prompt = "你是一名文本纠错助手，请修改文本中的错别字并输出结果。" + +system_prompt = system_prompt_input(default_prompt) +init_chat_history(chat_key, system_prompt) +chat_history = get_chat_history(chat_key) +for message in chat_history: + print_chat_message(message) + +question = st.text_input("Enter text for correction") + +debug_mode = st.sidebar.checkbox("Debug Mode", value=True) +log_interaction("User input", {"mode": "文本纠错", "question": question}) + +if question: + prompt = f"""{anthropic.HUMAN_PROMPT} Here's an article:\n\n
+ {question}\n\n
\n\n{question}{anthropic.AI_PROMPT}""" + + if question: + user_message = {"role": "user", "content": question} + # if app_mode == "语音识别": + print_chat_message(user_message) + chat_history.append(user_message) + response = ol.chat(model=model, messages=chat_history) + answer = response['message']['content'] + ai_message = {"role": "assistant", "content": answer} + print_chat_message(ai_message) + chat_history.append(ai_message) + debug_info = {"messages": chat_history, "response": response} + + if debug_mode: + st.write("Debug Info: Complete Prompt Interaction") + st.json(debug_info) + + # truncate chat history to keep 20 messages max + if len(chat_history) > 20: + chat_history = chat_history[-20:] + + # update chat history + st.session_state.chat_history[chat_key] = chat_history diff --git a/8_dialogue.py b/8_dialogue.py new file mode 100644 index 000000000..8cb8b0375 --- /dev/null +++ b/8_dialogue.py @@ -0,0 +1,91 @@ +import streamlit as st +import anthropic +import ollama as ol +import streamlit as st +from streamlit_mic_recorder import speech_to_text +import datetime +import json + +def log_interaction(action, data): + timestamp = datetime.datetime.now().isoformat() + log = {"timestamp": timestamp, "action": action, "data": data} + with open("user_interactions_log.json", "a") as logfile: + logfile.write(json.dumps(log) + "\n") + +def print_txt(text): + if any("\u0600" <= c <= "\u06FF" for c in text): # check if text contains Arabic characters + text = f"

{text}

" + st.markdown(text, unsafe_allow_html=True) + +def print_chat_message(message): + text = message["content"] + if message["role"] == "user": + with st.chat_message("user", avatar="🎙️"): + print_txt(text) + elif message["role"] == "assistant": + with st.chat_message("assistant", avatar="🦙"): + print_txt(text) + +def get_chat_history(key): + return st.session_state.chat_history[key] + +def init_chat_history(key, system_prompt): + if "chat_history" not in st.session_state: + st.session_state.chat_history = {} + if key not in st.session_state.chat_history: + st.session_state.chat_history[key] = [{"role": "system", "content": system_prompt}] + +def system_prompt_input(default_prompt): + return st.sidebar.text_area("System Prompt", value=default_prompt, height=100) + +def llm_selector(): + ollama_models = [m['name'] for m in ol.list()['models']] + with st.sidebar: + return st.selectbox("LLM", ollama_models) + + + +st.title("💬 对话模式") + + +model = llm_selector() +chat_key = f"对话_chat_history_{model}" # Unique key for each mode and model +default_prompt = "你是一位有用的助手。" + +system_prompt = system_prompt_input(default_prompt) +init_chat_history(chat_key, system_prompt) +chat_history = get_chat_history(chat_key) +for message in chat_history: + print_chat_message(message) + +question = st.chat_input() + +debug_mode = st.sidebar.checkbox("Debug Mode", value=True) +log_interaction("User input", {"mode": "对话", "question": question}) + +if question: + prompt = f"""{anthropic.HUMAN_PROMPT} Here's an article:\n\n
+ {question}\n\n
\n\n{question}{anthropic.AI_PROMPT}""" + + if question: + user_message = {"role": "user", "content": question} + # if app_mode == "语音识别": + print_chat_message(user_message) + chat_history.append(user_message) + response = ol.chat(model=model, messages=chat_history) + answer = response['message']['content'] + ai_message = {"role": "assistant", "content": answer} + print_chat_message(ai_message) + chat_history.append(ai_message) + debug_info = {"messages": chat_history, "response": response} + + if debug_mode: + st.write("Debug Info: Complete Prompt Interaction") + st.json(debug_info) + + # truncate chat history to keep 20 messages max + if len(chat_history) > 20: + chat_history = chat_history[-20:] + + # update chat history + st.session_state.chat_history[chat_key] = chat_history From 0cc45f9d43f8bcfc3767ff3a377b90bf1e190f98 Mon Sep 17 00:00:00 2001 From: XY <1308100560@qq.com> Date: Wed, 8 May 2024 11:18:25 +0800 Subject: [PATCH 2/6] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 7_Text_error_correction.py | 2 +- 9_Patent disclosure document.py | 101 ++++++++++++++++++++++++++++++++ 2 files changed, 102 insertions(+), 1 deletion(-) create mode 100644 9_Patent disclosure document.py diff --git a/7_Text_error_correction.py b/7_Text_error_correction.py index d9127afab..328c28bb1 100644 --- a/7_Text_error_correction.py +++ b/7_Text_error_correction.py @@ -50,7 +50,7 @@ def llm_selector(): model = llm_selector() chat_key = f"文本纠错_chat_history_{model}" # Unique key for each mode and model -default_prompt = "你是一名文本纠错助手，请修改文本中的错别字并输出结果。" +default_prompt = "你是一名文本纠错助手，请将下列语句中的错字、多字、少字修改正确,只输出修改后的句子,如果没有需要修改的地方，直接回复原句。不必做解释，直接输出即可。。" system_prompt = system_prompt_input(default_prompt) init_chat_history(chat_key, system_prompt) diff --git a/9_Patent disclosure document.py b/9_Patent disclosure document.py new file mode 100644 index 000000000..7b3fde084 --- /dev/null +++ b/9_Patent disclosure document.py @@ -0,0 +1,101 @@ +import streamlit as st +import anthropic +import ollama as ol +import streamlit as st +from streamlit_mic_recorder import speech_to_text +import datetime +import json + +def log_interaction(action, data): + timestamp = datetime.datetime.now().isoformat() + log = {"timestamp": timestamp, "action": action, "data": data} + with open("user_interactions_log.json", "a") as logfile: + logfile.write(json.dumps(log) + "\n") + +def print_txt(text): + if any("\u0600" <= c <= "\u06FF" for c in text): # check if text contains Arabic characters + text = f"

{text}

" + st.markdown(text, unsafe_allow_html=True) + +def print_chat_message(message): + text = message["content"] + if message["role"] == "user": + with st.chat_message("user", avatar="🎙️"): + print_txt(text) + elif message["role"] == "assistant": + with st.chat_message("assistant", avatar="🦙"): + print_txt(text) + +def get_chat_history(key): + return st.session_state.chat_history[key] + +def init_chat_history(key, system_prompt): + if "chat_history" not in st.session_state: + st.session_state.chat_history = {} + if key not in st.session_state.chat_history: + st.session_state.chat_history[key] = [{"role": "system", "content": system_prompt}] + +def system_prompt_input(default_prompt): + return st.sidebar.text_area("System Prompt", value=default_prompt, height=100) + +def llm_selector(): + ollama_models = [m['name'] for m in ol.list()['models']] + with st.sidebar: + return st.selectbox("LLM", ollama_models) + + + +st.title("📝 Patent disclosure document") +uploaded_file = st.file_uploader("Upload an article", type=("txt", "md", "docx")) + + +model = llm_selector() +chat_key = f"对话_chat_history_{model}" # Unique key for each mode and model +default_prompt = ("你是一位有用的中文助手，回答我的任何问题都要详细说明，并且用中文回答我。" + "我要升成一篇专利交底书，请用中文回答我." + "内容包括发明名称、技术领域、现有技术一的技术方案、现有技术一的缺点、" + "与本发明相关的现有技术二、本发明所要解决的技术问题、本发明提供的完整技术方案、" + "本发明技术方案带来的有益效果、针对本发明提供的完整技术方案中的技术方案，" + "是否还有别的替代方案同样能完成发明目的、本发明的技术关键点和欲保护点是什么。") + +system_prompt = system_prompt_input(default_prompt) +init_chat_history(chat_key, system_prompt) +chat_history = get_chat_history(chat_key) +for message in chat_history: + print_chat_message(message) + +question = st.chat_input() + +debug_mode = st.sidebar.checkbox("Debug Mode", value=True) +log_interaction("User input", {"mode": "对话", "question": question}) + +if question: + prompt = f"""{anthropic.HUMAN_PROMPT} Here's an article:\n\n
+ {question}\n\n
\n\n{question}{anthropic.AI_PROMPT}""" + + if question: + user_message = {"role": "user", "content": question} + + # if app_mode == "语音识别": + print_chat_message(user_message) + chat_history.append(user_message) + if uploaded_file: + article = uploaded_file.read().decode() + chat_history.append({"role": "user", "content": article}) # 添加用户上传的文件内容作为对话历史的一部分 + response = ol.chat(model=model, messages=chat_history) + answer = response['message']['content'] + ai_message = {"role": "assistant", "content": answer} + print_chat_message(ai_message) + chat_history.append(ai_message) + debug_info = {"messages": chat_history, "response": response} + + if debug_mode: + st.write("Debug Info: Complete Prompt Interaction") + st.json(debug_info) + + # truncate chat history to keep 20 messages max + if len(chat_history) > 20: + chat_history = chat_history[-20:] + + # update chat history + st.session_state.chat_history[chat_key] = chat_history From 922bc11677514c97df6c4f93d92887a205a619e4 Mon Sep 17 00:00:00 2001 From: 1308100560 Date: Thu, 9 May 2024 14:57:10 +0800 Subject: [PATCH 3/6] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 1_File_Q&A.py | 33 ++++++++++++++++++ 2_Chat_with_search.py | 48 ++++++++++++++++++++++++++ 3_Langchain_Quickstart.py | 22 ++++++++++++ 4_Langchain_PromptTemplate.py | 29 ++++++++++++++++ 5_Chat_with_user_feedback.py | 65 +++++++++++++++++++++++++++++++++++ 5 files changed, 197 insertions(+) create mode 100644 1_File_Q&A.py create mode 100644 2_Chat_with_search.py create mode 100644 3_Langchain_Quickstart.py create mode 100644 4_Langchain_PromptTemplate.py create mode 100644 5_Chat_with_user_feedback.py diff --git a/1_File_Q&A.py b/1_File_Q&A.py new file mode 100644 index 000000000..417474c4f --- /dev/null +++ b/1_File_Q&A.py @@ -0,0 +1,33 @@ +import streamlit as st +import anthropic + +with st.sidebar: + anthropic_api_key = st.text_input("Anthropic API Key", key="file_qa_api_key", type="password") + "[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/1_File_Q%26A.py)" + "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)" + +st.title("📝 File Q&A with Anthropic") +uploaded_file = st.file_uploader("Upload an article", type=("txt", "md")) +question = st.text_input( + "Ask something about the article", + placeholder="Can you give me a short summary?", + disabled=not uploaded_file, +) + +if uploaded_file and question and not anthropic_api_key: + st.info("Please add your Anthropic API key to continue.") + +if uploaded_file and question and anthropic_api_key: + article = uploaded_file.read().decode() + prompt = f"""{anthropic.HUMAN_PROMPT} Here's an article:\n\n
+ {article}\n\n
\n\n{question}{anthropic.AI_PROMPT}""" + + client = anthropic.Client(api_key=anthropic_api_key) + response = client.completions.create( + prompt=prompt, + stop_sequences=[anthropic.HUMAN_PROMPT], + model="claude-v1", # "claude-2" for Claude 2 model + max_tokens_to_sample=100, + ) + st.write("### Answer") + st.write(response.completion) diff --git a/2_Chat_with_search.py b/2_Chat_with_search.py new file mode 100644 index 000000000..399c58219 --- /dev/null +++ b/2_Chat_with_search.py @@ -0,0 +1,48 @@ +import streamlit as st + +from langchain.agents import initialize_agent, AgentType +from langchain.callbacks import StreamlitCallbackHandler +from langchain.chat_models import ChatOpenAI +from langchain.tools import DuckDuckGoSearchRun + +with st.sidebar: + openai_api_key = st.text_input( + "OpenAI API Key", key="langchain_search_api_key_openai", type="password" + ) + "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)" + "[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/2_Chat_with_search.py)" + "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)" + +st.title("🔎 LangChain - Chat with search") + +""" +In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app. +Try more LangChain 🤝 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent). +""" + +if "messages" not in st.session_state: + st.session_state["messages"] = [ + {"role": "assistant", "content": "Hi, I'm a chatbot who can search the web. How can I help you?"} + ] + +for msg in st.session_state.messages: + st.chat_message(msg["role"]).write(msg["content"]) + +if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?"): + st.session_state.messages.append({"role": "user", "content": prompt}) + st.chat_message("user").write(prompt) + + if not openai_api_key: + st.info("Please add your OpenAI API key to continue.") + st.stop() + + llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=openai_api_key, streaming=True) + search = DuckDuckGoSearchRun(name="Search") + search_agent = initialize_agent( + [search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True + ) + with st.chat_message("assistant"): + st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False) + response = search_agent.run(st.session_state.messages, callbacks=[st_cb]) + st.session_state.messages.append({"role": "assistant", "content": response}) + st.write(response) diff --git a/3_Langchain_Quickstart.py b/3_Langchain_Quickstart.py new file mode 100644 index 000000000..38c820f24 --- /dev/null +++ b/3_Langchain_Quickstart.py @@ -0,0 +1,22 @@ +import streamlit as st +from langchain.llms import OpenAI + +st.title("🦜🔗 Langchain Quickstart App") + +with st.sidebar: + openai_api_key = st.text_input("OpenAI API Key", type="password") + "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)" + + +def generate_response(input_text): + llm = OpenAI(temperature=0.7, openai_api_key=openai_api_key) + st.info(llm(input_text)) + + +with st.form("my_form"): + text = st.text_area("Enter text:", "What are 3 key advice for learning how to code?") + submitted = st.form_submit_button("Submit") + if not openai_api_key: + st.info("Please add your OpenAI API key to continue.") + elif submitted: + generate_response(text) diff --git a/4_Langchain_PromptTemplate.py b/4_Langchain_PromptTemplate.py new file mode 100644 index 000000000..3755419ea --- /dev/null +++ b/4_Langchain_PromptTemplate.py @@ -0,0 +1,29 @@ +import streamlit as st +from langchain.llms import OpenAI +from langchain.prompts import PromptTemplate + +st.title("🦜🔗 Langchain - Blog Outline Generator App") + +openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password") + + +def blog_outline(topic): + # Instantiate LLM model + llm = OpenAI(model_name="text-davinci-003", openai_api_key=openai_api_key) + # Prompt + template = "As an experienced data scientist and technical writer, generate an outline for a blog about {topic}." + prompt = PromptTemplate(input_variables=["topic"], template=template) + prompt_query = prompt.format(topic=topic) + # Run LLM model + response = llm(prompt_query) + # Print results + return st.info(response) + + +with st.form("myform"): + topic_text = st.text_input("Enter prompt:", "") + submitted = st.form_submit_button("Submit") + if not openai_api_key: + st.info("Please add your OpenAI API key to continue.") + elif submitted: + blog_outline(topic_text) diff --git a/5_Chat_with_user_feedback.py b/5_Chat_with_user_feedback.py new file mode 100644 index 000000000..5f58f139c --- /dev/null +++ b/5_Chat_with_user_feedback.py @@ -0,0 +1,65 @@ +from openai import OpenAI +import streamlit as st +from streamlit_feedback import streamlit_feedback +import trubrics + +with st.sidebar: + openai_api_key = st.text_input("OpenAI API Key", key="feedback_api_key", type="password") + "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)" + "[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/5_Chat_with_user_feedback.py)" + "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)" + +st.title("📝 Chat with feedback (Trubrics)") + +""" +In this example, we're using [streamlit-feedback](https://github.com/trubrics/streamlit-feedback) and Trubrics to collect and store feedback +from the user about the LLM responses. +""" + +if "messages" not in st.session_state: + st.session_state.messages = [ + {"role": "assistant", "content": "How can I help you? Leave feedback to help me improve!"} + ] +if "response" not in st.session_state: + st.session_state["response"] = None + +messages = st.session_state.messages +for msg in messages: + st.chat_message(msg["role"]).write(msg["content"]) + +if prompt := st.chat_input(placeholder="Tell me a joke about sharks"): + messages.append({"role": "user", "content": prompt}) + st.chat_message("user").write(prompt) + + if not openai_api_key: + st.info("Please add your OpenAI API key to continue.") + st.stop() + client = OpenAI(api_key=openai_api_key) + response = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages) + st.session_state["response"] = response.choices[0].message.content + with st.chat_message("assistant"): + messages.append({"role": "assistant", "content": st.session_state["response"]}) + st.write(st.session_state["response"]) + +if st.session_state["response"]: + feedback = streamlit_feedback( + feedback_type="thumbs", + optional_text_label="[Optional] Please provide an explanation", + key=f"feedback_{len(messages)}", + ) + # This app is logging feedback to Trubrics backend, but you can send it anywhere. + # The return value of streamlit_feedback() is just a dict. + # Configure your own account at https://trubrics.streamlit.app/ + if feedback and "TRUBRICS_EMAIL" in st.secrets: + config = trubrics.init( + email=st.secrets.TRUBRICS_EMAIL, + password=st.secrets.TRUBRICS_PASSWORD, + ) + collection = trubrics.collect( + component_name="default", + model="gpt", + response=feedback, + metadata={"chat": messages}, + ) + trubrics.save(config, collection) + st.toast("Feedback recorded!", icon="📝") From fa485b3d87beed070468e4390be49ea458138fc9 Mon Sep 17 00:00:00 2001 From: 1308100560 Date: Sat, 11 May 2024 17:07:19 +0800 Subject: [PATCH 4/6] =?UTF-8?q?=E6=96=B0=E5=A2=9E=E5=8E=86=E5=8F=B2?= =?UTF-8?q?=E6=95=B0=E6=8D=AE=E6=94=B6=E9=9B=86=E3=80=81=E7=BD=91=E6=83=85?= =?UTF-8?q?=E6=95=B0=E6=8D=AE=E6=94=B6=E9=9B=86=E3=80=81=E4=B8=8A=E7=BA=A7?= =?UTF-8?q?=E4=BB=BB=E5=8A=A1=E6=95=B0=E6=8D=AE=E6=94=B6=E9=9B=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 10_Historical_data_collection.py | 96 ++++++++++++++++++++++++++ 11_Internet_data_collection.py | 96 ++++++++++++++++++++++++++ 12_Collection_of_superior_task_data.py | 96 ++++++++++++++++++++++++++ 9_Patent disclosure document.py | 2 +- 4 files changed, 289 insertions(+), 1 deletion(-) create mode 100644 10_Historical_data_collection.py create mode 100644 11_Internet_data_collection.py create mode 100644 12_Collection_of_superior_task_data.py diff --git a/10_Historical_data_collection.py b/10_Historical_data_collection.py new file mode 100644 index 000000000..1402ce9af --- /dev/null +++ b/10_Historical_data_collection.py @@ -0,0 +1,96 @@ +import streamlit as st +import anthropic +import ollama as ol +import streamlit as st +from streamlit_mic_recorder import speech_to_text +import datetime +import json + +def log_interaction(action, data): + timestamp = datetime.datetime.now().isoformat() + log = {"timestamp": timestamp, "action": action, "data": data} + with open("user_interactions_log.json", "a") as logfile: + logfile.write(json.dumps(log) + "\n") + +def print_txt(text): + if any("\u0600" <= c <= "\u06FF" for c in text): # check if text contains Arabic characters + text = f"

{text}

" + st.markdown(text, unsafe_allow_html=True) + +def print_chat_message(message): + text = message["content"] + if message["role"] == "user": + with st.chat_message("user", avatar="🎙️"): + print_txt(text) + elif message["role"] == "assistant": + with st.chat_message("assistant", avatar="🦙"): + print_txt(text) + +def get_chat_history(key): + return st.session_state.chat_history[key] + +def init_chat_history(key, system_prompt): + if "chat_history" not in st.session_state: + st.session_state.chat_history = {} + if key not in st.session_state.chat_history: + st.session_state.chat_history[key] = [{"role": "system", "content": system_prompt}] + +def system_prompt_input(default_prompt): + return st.sidebar.text_area("System Prompt", value=default_prompt, height=100) + +def llm_selector(): + ollama_models = [m['name'] for m in ol.list()['models']] + with st.sidebar: + return st.selectbox("LLM", ollama_models) + + + +st.title("🕔 Historical data collection") +uploaded_file = st.file_uploader("Upload an article", type=("txt")) + + +model = llm_selector() +chat_key = f"对话_chat_history_{model}" # Unique key for each mode and model +default_prompt = ("我现在将要给你传送历史数据，你需要整理这个数据然后给我发送清洗后的历史数据。要求格式：json格式。") + +system_prompt = system_prompt_input(default_prompt) +init_chat_history(chat_key, system_prompt) +chat_history = get_chat_history(chat_key) +for message in chat_history: + print_chat_message(message) + +question = st.chat_input() + +debug_mode = st.sidebar.checkbox("Debug Mode", value=True) +log_interaction("User input", {"mode": "对话", "question": question}) + +if question: + prompt = f"""{anthropic.HUMAN_PROMPT} Here's an article:\n\n
+ {question}\n\n
\n\n{question}{anthropic.AI_PROMPT}""" + + if question: + user_message = {"role": "user", "content": question} + + # if app_mode == "语音识别": + print_chat_message(user_message) + chat_history.append(user_message) + if uploaded_file: + article = uploaded_file.read().decode() + chat_history.append({"role": "user", "content": article}) # 添加用户上传的文件内容作为对话历史的一部分 + response = ol.chat(model=model, messages=chat_history) + answer = response['message']['content'] + ai_message = {"role": "assistant", "content": answer} + print_chat_message(ai_message) + chat_history.append(ai_message) + debug_info = {"messages": chat_history, "response": response} + + if debug_mode: + st.write("Debug Info: Complete Prompt Interaction") + st.json(debug_info) + + # truncate chat history to keep 20 messages max + if len(chat_history) > 20: + chat_history = chat_history[-20:] + + # update chat history + st.session_state.chat_history[chat_key] = chat_history diff --git a/11_Internet_data_collection.py b/11_Internet_data_collection.py new file mode 100644 index 000000000..1a05d5504 --- /dev/null +++ b/11_Internet_data_collection.py @@ -0,0 +1,96 @@ +import streamlit as st +import anthropic +import ollama as ol +import streamlit as st +from streamlit_mic_recorder import speech_to_text +import datetime +import json + +def log_interaction(action, data): + timestamp = datetime.datetime.now().isoformat() + log = {"timestamp": timestamp, "action": action, "data": data} + with open("user_interactions_log.json", "a") as logfile: + logfile.write(json.dumps(log) + "\n") + +def print_txt(text): + if any("\u0600" <= c <= "\u06FF" for c in text): # check if text contains Arabic characters + text = f"

{text}

" + st.markdown(text, unsafe_allow_html=True) + +def print_chat_message(message): + text = message["content"] + if message["role"] == "user": + with st.chat_message("user", avatar="🎙️"): + print_txt(text) + elif message["role"] == "assistant": + with st.chat_message("assistant", avatar="🦙"): + print_txt(text) + +def get_chat_history(key): + return st.session_state.chat_history[key] + +def init_chat_history(key, system_prompt): + if "chat_history" not in st.session_state: + st.session_state.chat_history = {} + if key not in st.session_state.chat_history: + st.session_state.chat_history[key] = [{"role": "system", "content": system_prompt}] + +def system_prompt_input(default_prompt): + return st.sidebar.text_area("System Prompt", value=default_prompt, height=100) + +def llm_selector(): + ollama_models = [m['name'] for m in ol.list()['models']] + with st.sidebar: + return st.selectbox("LLM", ollama_models) + + + +st.title("🖥️ Internet data collection") +uploaded_file = st.file_uploader("Upload an article", type=("txt")) + + +model = llm_selector() +chat_key = f"对话_chat_history_{model}" # Unique key for each mode and model +default_prompt = ("我现在将要给你传送网情数据,你需要提取这个数据特征之后发给我，要求格式：json格式。") + +system_prompt = system_prompt_input(default_prompt) +init_chat_history(chat_key, system_prompt) +chat_history = get_chat_history(chat_key) +for message in chat_history: + print_chat_message(message) + +question = st.chat_input() + +debug_mode = st.sidebar.checkbox("Debug Mode", value=True) +log_interaction("User input", {"mode": "对话", "question": question}) + +if question: + prompt = f"""{anthropic.HUMAN_PROMPT} Here's an article:\n\n
+ {question}\n\n
\n\n{question}{anthropic.AI_PROMPT}""" + + if question: + user_message = {"role": "user", "content": question} + + # if app_mode == "语音识别": + print_chat_message(user_message) + chat_history.append(user_message) + if uploaded_file: + article = uploaded_file.read().decode() + chat_history.append({"role": "user", "content": article}) # 添加用户上传的文件内容作为对话历史的一部分 + response = ol.chat(model=model, messages=chat_history) + answer = response['message']['content'] + ai_message = {"role": "assistant", "content": answer} + print_chat_message(ai_message) + chat_history.append(ai_message) + debug_info = {"messages": chat_history, "response": response} + + if debug_mode: + st.write("Debug Info: Complete Prompt Interaction") + st.json(debug_info) + + # truncate chat history to keep 20 messages max + if len(chat_history) > 20: + chat_history = chat_history[-20:] + + # update chat history + st.session_state.chat_history[chat_key] = chat_history diff --git a/12_Collection_of_superior_task_data.py b/12_Collection_of_superior_task_data.py new file mode 100644 index 000000000..35ab8680c --- /dev/null +++ b/12_Collection_of_superior_task_data.py @@ -0,0 +1,96 @@ +import streamlit as st +import anthropic +import ollama as ol +import streamlit as st +from streamlit_mic_recorder import speech_to_text +import datetime +import json + +def log_interaction(action, data): + timestamp = datetime.datetime.now().isoformat() + log = {"timestamp": timestamp, "action": action, "data": data} + with open("user_interactions_log.json", "a") as logfile: + logfile.write(json.dumps(log) + "\n") + +def print_txt(text): + if any("\u0600" <= c <= "\u06FF" for c in text): # check if text contains Arabic characters + text = f"

{text}

" + st.markdown(text, unsafe_allow_html=True) + +def print_chat_message(message): + text = message["content"] + if message["role"] == "user": + with st.chat_message("user", avatar="🎙️"): + print_txt(text) + elif message["role"] == "assistant": + with st.chat_message("assistant", avatar="🦙"): + print_txt(text) + +def get_chat_history(key): + return st.session_state.chat_history[key] + +def init_chat_history(key, system_prompt): + if "chat_history" not in st.session_state: + st.session_state.chat_history = {} + if key not in st.session_state.chat_history: + st.session_state.chat_history[key] = [{"role": "system", "content": system_prompt}] + +def system_prompt_input(default_prompt): + return st.sidebar.text_area("System Prompt", value=default_prompt, height=100) + +def llm_selector(): + ollama_models = [m['name'] for m in ol.list()['models']] + with st.sidebar: + return st.selectbox("LLM", ollama_models) + + + +st.title("🪪 Collection of superior task data") +uploaded_file = st.file_uploader("Upload an article", type=("txt")) + + +model = llm_selector() +chat_key = f"对话_chat_history_{model}" # Unique key for each mode and model +default_prompt = ("我现在将要给你传送上级任务数据，你需要整理这个数据然后给我发送清洗后的任务数据。要求格式：json格式。") + +system_prompt = system_prompt_input(default_prompt) +init_chat_history(chat_key, system_prompt) +chat_history = get_chat_history(chat_key) +for message in chat_history: + print_chat_message(message) + +question = st.chat_input() + +debug_mode = st.sidebar.checkbox("Debug Mode", value=True) +log_interaction("User input", {"mode": "对话", "question": question}) + +if question: + prompt = f"""{anthropic.HUMAN_PROMPT} Here's an article:\n\n
+ {question}\n\n
\n\n{question}{anthropic.AI_PROMPT}""" + + if question: + user_message = {"role": "user", "content": question} + + # if app_mode == "语音识别": + print_chat_message(user_message) + chat_history.append(user_message) + if uploaded_file: + article = uploaded_file.read().decode() + chat_history.append({"role": "user", "content": article}) # 添加用户上传的文件内容作为对话历史的一部分 + response = ol.chat(model=model, messages=chat_history) + answer = response['message']['content'] + ai_message = {"role": "assistant", "content": answer} + print_chat_message(ai_message) + chat_history.append(ai_message) + debug_info = {"messages": chat_history, "response": response} + + if debug_mode: + st.write("Debug Info: Complete Prompt Interaction") + st.json(debug_info) + + # truncate chat history to keep 20 messages max + if len(chat_history) > 20: + chat_history = chat_history[-20:] + + # update chat history + st.session_state.chat_history[chat_key] = chat_history diff --git a/9_Patent disclosure document.py b/9_Patent disclosure document.py index 7b3fde084..eb8814c5b 100644 --- a/9_Patent disclosure document.py +++ b/9_Patent disclosure document.py @@ -46,7 +46,7 @@ def llm_selector(): st.title("📝 Patent disclosure document") -uploaded_file = st.file_uploader("Upload an article", type=("txt", "md", "docx")) +uploaded_file = st.file_uploader("Upload an article", type=("txt")) model = llm_selector() From f16469c9774365d380e88ae572b855c09dcb48d5 Mon Sep 17 00:00:00 2001 From: 1308100560 Date: Sat, 11 May 2024 17:42:50 +0800 Subject: [PATCH 5/6] =?UTF-8?q?=E6=96=B0=E5=A2=9E=E5=8E=86=E5=8F=B2?= =?UTF-8?q?=E6=95=B0=E6=8D=AE=E6=94=B6=E9=9B=86=E3=80=81=E7=BD=91=E6=83=85?= =?UTF-8?q?=E6=95=B0=E6=8D=AE=E6=94=B6=E9=9B=86=E3=80=81=E4=B8=8A=E7=BA=A7?= =?UTF-8?q?=E4=BB=BB=E5=8A=A1=E6=95=B0=E6=8D=AE=E6=94=B6=E9=9B=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 12_Collection_of_superior_task_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/12_Collection_of_superior_task_data.py b/12_Collection_of_superior_task_data.py index 35ab8680c..ebc2133b1 100644 --- a/12_Collection_of_superior_task_data.py +++ b/12_Collection_of_superior_task_data.py @@ -45,7 +45,7 @@ def llm_selector(): -st.title("🪪 Collection of superior task data") +st.title("🪪 ollection of superior task data") uploaded_file = st.file_uploader("Upload an article", type=("txt")) From 06453396126f2b6278d318aa0708b2d763e055b7 Mon Sep 17 00:00:00 2001 From: 1308100560 Date: Sat, 11 May 2024 17:43:54 +0800 Subject: [PATCH 6/6] =?UTF-8?q?=E6=9B=B4=E6=96=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 12_Collection_of_superior_task_data.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/12_Collection_of_superior_task_data.py b/12_Collection_of_superior_task_data.py index ebc2133b1..35ab8680c 100644 --- a/12_Collection_of_superior_task_data.py +++ b/12_Collection_of_superior_task_data.py @@ -45,7 +45,7 @@ def llm_selector(): -st.title("🪪 ollection of superior task data") +st.title("🪪 Collection of superior task data") uploaded_file = st.file_uploader("Upload an article", type=("txt"))