UPDATE

app/streamlit/pages/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>
+    {article}\n\n</article>\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)

app/streamlit/pages/2_Chat_with_search.py

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+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)

app/streamlit/pages/3_Langchain_Quickstart.py

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+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)

app/streamlit/pages/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)

app/streamlit/pages/5_Chat_with_user_feedback.py

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+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="📝")