Playing with streamlit

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  import streamlit as st
  import numpy as np
  import pandas as pd

  if "counter" not in st.session_state:
      st.session_state.counter = 0

  st.session_state.counter += 1

  st.header(f"This page has run {st.session_state.counter} times.")
  st.button("Run it again")

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  import streamlit as st
  import pandas as pd
  import numpy as np

  st.title('Uber pickups in NYC')

  DATE_COLUMN = 'date/time'
  DATA_URL = ('https://s3-us-west-2.amazonaws.com/'
              'streamlit-demo-data/uber-raw-data-sep14.csv.gz')

  @st.cache_data
  def load_data(nrows):
      data = pd.read_csv(DATA_URL, nrows=nrows)
      lowercase = lambda x: str(x).lower()
      data.rename(lowercase, axis='columns', inplace=True)
      data[DATE_COLUMN] = pd.to_datetime(data[DATE_COLUMN])
      return data

  data_load_state = st.text('Loading data...')
  data = load_data(10000)
  data_load_state.text("Done! (using st.cache_data)")

  if st.checkbox('Show raw data'):
      st.subheader('Raw data')
      st.write(data)

  st.subheader('Number of pickups by hour')
  hist_values = np.histogram(data[DATE_COLUMN].dt.hour, bins=24, range=(0,24))[0]
  st.bar_chart(hist_values)

  # Some number in the range 0-23
  hour_to_filter = st.slider('hour', 0, 23, 17)
  filtered_data = data[data[DATE_COLUMN].dt.hour == hour_to_filter]

  st.subheader('Map of all pickups at %s:00' % hour_to_filter)
  st.map(filtered_data)

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  import streamlit as st

  st.title("Echo Bot")

  # Initialize chat history
  if "messages" not in st.session_state:
      st.session_state.messages = []

  # Display chat messages from history on app rerun
  for message in st.session_state.messages:
      with st.chat_message(message["role"]):
          st.markdown(message["content"])

  # React to user input
  if prompt := st.chat_input("What is up?"):
      # Display user message in chat message container
      with st.chat_message("user"):
          st.markdown(prompt)
      # Add user message to chat history
      st.session_state.messages.append({"role": "user", "content": prompt})

  response = f"Echo: {prompt}"
  # Display assistant response in chat message container
  with st.chat_message("assistant"):
      st.markdown(response)
  # Add assistant response to chat history
  st.session_state.messages.append({"role": "assistant", "content": response})

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  import streamlit as st
  from langchain_openai.chat_models import ChatOpenAI

  st.title("🦜🔗 Quickstart App")

  openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password")


  def generate_response(input_text):
      model = ChatOpenAI(temperature=0.7, api_key=openai_api_key)
      st.info(model.invoke(input_text))


  with st.form("my_form"):
      text = st.text_area(
          "Enter text:",
          "What are the three key pieces of advice for learning how to code?",
      )
      submitted = st.form_submit_button("Submit")
      if not openai_api_key.startswith("sk-"):
          st.warning("Please enter your OpenAI API key!", icon="⚠")
      if submitted and openai_api_key.startswith("sk-"):
          generate_response(text)