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Merge pull request #13 from aimingmed/feature/status-batch
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Hong Kai LEE
GitHub
commit
29d82e7cef
@ -1,4 +1,4 @@
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[](https://github.com/aimingmed/aimingmed-ai/actions/workflows/app-testing.yml)
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## Important note:
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## Important note:
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@ -3,8 +3,8 @@ version: "3.9"
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services:
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services:
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streamlit:
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streamlit:
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build: ./streamlit
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build: ./streamlit
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platform: linux/amd64
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ports:
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ports:
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- "8501:8501"
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- "8501:8501"
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volumes:
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volumes:
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- ./llmops/src/rag_cot/chroma_db:/app/llmops/src/rag_cot/chroma_db
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- ./llmops/src/rag_cot/chroma_db:/app/llmops/src/rag_cot/chroma_db
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@ -2,9 +2,9 @@ FROM python:3.11-slim
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WORKDIR /app/streamlit
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WORKDIR /app/streamlit
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COPY Pipfile ./
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COPY requirements.txt ./
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RUN pip install pipenv && pipenv install --system --deploy
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RUN pip install --no-cache-dir -r requirements.txt
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COPY Chatbot.py .
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COPY Chatbot.py .
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COPY .env .
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COPY .env .
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@ -25,20 +25,25 @@ def create_chat_completion(response: str, role: str = "assistant") -> ChatComple
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)
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)
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# @patch("openai.resources.chat.Completions.create")
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# @patch("langchain_deepseek.ChatDeepSeek.__call__")
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# def test_Chatbot(openai_create):
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# @patch("langchain_google_genai.ChatGoogleGenerativeAI.invoke")
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# @patch("langchain_community.llms.moonshot.Moonshot.__call__")
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# def test_Chatbot(moonshot_llm, gemini_llm, deepseek_llm):
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# at = AppTest.from_file("Chatbot.py").run()
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# at = AppTest.from_file("Chatbot.py").run()
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# assert not at.exception
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# assert not at.exception
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# at.chat_input[0].set_value("Do you know any jokes?").run()
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# assert at.info[0].value == "Please add your OpenAI API key to continue."
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# QUERY = "What is the best treatment for hypertension?"
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# RESPONSE = "The best treatment for hypertension is..."
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# JOKE = "Why did the chicken cross the road? To get to the other side."
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# openai_create.return_value = create_chat_completion(JOKE)
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# deepseek_llm.return_value.content = RESPONSE
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# at.text_input(key="chatbot_api_key").set_value("sk-...")
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# gemini_llm.return_value.content = RESPONSE
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# at.chat_input[0].set_value("Do you know any jokes?").run()
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# moonshot_llm.return_value = RESPONSE
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# print(at)
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# assert at.chat_message[1].markdown[0].value == "Do you know any jokes?"
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# at.chat_input[0].set_value(QUERY).run()
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# assert at.chat_message[2].markdown[0].value == JOKE
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# assert any(mock.called for mock in [deepseek_llm, gemini_llm, moonshot_llm])
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# assert at.chat_message[1].markdown[0].value == QUERY
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# assert at.chat_message[2].markdown[0].value == RESPONSE
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# assert at.chat_message[2].avatar == "assistant"
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# assert at.chat_message[2].avatar == "assistant"
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# assert not at.exception
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# assert not at.exception
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@ -1,33 +0,0 @@
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import streamlit as st
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import anthropic
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with st.sidebar:
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anthropic_api_key = st.text_input("Anthropic API Key", key="file_qa_api_key", type="password")
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"[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/1_File_Q%26A.py)"
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"[](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
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st.title("📝 File Q&A with Anthropic")
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uploaded_file = st.file_uploader("Upload an article", type=("txt", "md"))
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question = st.text_input(
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"Ask something about the article",
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placeholder="Can you give me a short summary?",
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disabled=not uploaded_file,
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)
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if uploaded_file and question and not anthropic_api_key:
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st.info("Please add your Anthropic API key to continue.")
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if uploaded_file and question and anthropic_api_key:
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article = uploaded_file.read().decode()
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prompt = f"""{anthropic.HUMAN_PROMPT} Here's an article:\n\n<article>
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{article}\n\n</article>\n\n{question}{anthropic.AI_PROMPT}"""
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client = anthropic.Client(api_key=anthropic_api_key)
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response = client.completions.create(
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prompt=prompt,
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stop_sequences=[anthropic.HUMAN_PROMPT],
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model="claude-v1", # "claude-2" for Claude 2 model
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max_tokens_to_sample=100,
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)
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st.write("### Answer")
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st.write(response.completion)
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@ -1,48 +0,0 @@
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import streamlit as st
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from langchain.agents import initialize_agent, AgentType
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from langchain.callbacks import StreamlitCallbackHandler
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from langchain.chat_models import ChatOpenAI
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from langchain.tools import DuckDuckGoSearchRun
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with st.sidebar:
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openai_api_key = st.text_input(
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"OpenAI API Key", key="langchain_search_api_key_openai", type="password"
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)
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"[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
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"[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/2_Chat_with_search.py)"
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"[](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
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st.title("🔎 LangChain - Chat with search")
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"""
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In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app.
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Try more LangChain 🤝 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).
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"""
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if "messages" not in st.session_state:
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st.session_state["messages"] = [
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{"role": "assistant", "content": "Hi, I'm a chatbot who can search the web. How can I help you?"}
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]
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for msg in st.session_state.messages:
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st.chat_message(msg["role"]).write(msg["content"])
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if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?"):
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st.session_state.messages.append({"role": "user", "content": prompt})
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st.chat_message("user").write(prompt)
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if not openai_api_key:
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st.info("Please add your OpenAI API key to continue.")
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st.stop()
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llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=openai_api_key, streaming=True)
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search = DuckDuckGoSearchRun(name="Search")
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search_agent = initialize_agent(
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[search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True
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)
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with st.chat_message("assistant"):
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st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
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response = search_agent.run(st.session_state.messages, callbacks=[st_cb])
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st.session_state.messages.append({"role": "assistant", "content": response})
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st.write(response)
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import streamlit as st
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from langchain.llms import OpenAI
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from langchain.prompts import PromptTemplate
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st.title("🦜🔗 Langchain - Blog Outline Generator App")
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openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password")
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def blog_outline(topic):
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# Instantiate LLM model
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llm = OpenAI(model_name="text-davinci-003", openai_api_key=openai_api_key)
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# Prompt
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template = "As an experienced data scientist and technical writer, generate an outline for a blog about {topic}."
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prompt = PromptTemplate(input_variables=["topic"], template=template)
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prompt_query = prompt.format(topic=topic)
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# Run LLM model
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response = llm(prompt_query)
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# Print results
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return st.info(response)
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with st.form("myform"):
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topic_text = st.text_input("Enter prompt:", "")
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submitted = st.form_submit_button("Submit")
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if not openai_api_key:
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st.info("Please add your OpenAI API key to continue.")
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elif submitted:
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blog_outline(topic_text)
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from openai import OpenAI
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import streamlit as st
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from streamlit_feedback import streamlit_feedback
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import trubrics
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with st.sidebar:
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openai_api_key = st.text_input("OpenAI API Key", key="feedback_api_key", type="password")
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"[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
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"[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/5_Chat_with_user_feedback.py)"
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"[](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
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st.title("📝 Chat with feedback (Trubrics)")
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"""
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In this example, we're using [streamlit-feedback](https://github.com/trubrics/streamlit-feedback) and Trubrics to collect and store feedback
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from the user about the LLM responses.
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"""
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if "messages" not in st.session_state:
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st.session_state.messages = [
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{"role": "assistant", "content": "How can I help you? Leave feedback to help me improve!"}
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]
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if "response" not in st.session_state:
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st.session_state["response"] = None
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messages = st.session_state.messages
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for msg in messages:
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st.chat_message(msg["role"]).write(msg["content"])
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if prompt := st.chat_input(placeholder="Tell me a joke about sharks"):
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messages.append({"role": "user", "content": prompt})
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st.chat_message("user").write(prompt)
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if not openai_api_key:
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st.info("Please add your OpenAI API key to continue.")
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st.stop()
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client = OpenAI(api_key=openai_api_key)
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response = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
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st.session_state["response"] = response.choices[0].message.content
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with st.chat_message("assistant"):
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messages.append({"role": "assistant", "content": st.session_state["response"]})
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st.write(st.session_state["response"])
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if st.session_state["response"]:
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feedback = streamlit_feedback(
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feedback_type="thumbs",
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optional_text_label="[Optional] Please provide an explanation",
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key=f"feedback_{len(messages)}",
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)
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# This app is logging feedback to Trubrics backend, but you can send it anywhere.
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# The return value of streamlit_feedback() is just a dict.
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# Configure your own account at https://trubrics.streamlit.app/
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if feedback and "TRUBRICS_EMAIL" in st.secrets:
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config = trubrics.init(
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email=st.secrets.TRUBRICS_EMAIL,
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password=st.secrets.TRUBRICS_PASSWORD,
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)
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collection = trubrics.collect(
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component_name="default",
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model="gpt",
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response=feedback,
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metadata={"chat": messages},
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)
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trubrics.save(config, collection)
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st.toast("Feedback recorded!", icon="📝")
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@ -11,4 +11,4 @@ python-decouple
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langchain_google_genai
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langchain_google_genai
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langchain-deepseek
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langchain-deepseek
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sentence_transformers
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sentence_transformers
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mlflow
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watchdog
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