test_demo / src /streamlit_app.py
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Update src/streamlit_app.py
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# import altair as alt
# import numpy as np
# import pandas as pd
# import streamlit as st
# """
# # Welcome to Streamlit!
# Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
# If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
# forums](https://discuss.streamlit.io).
# In the meantime, below is an example of what you can do with just a few lines of code:
# """
# num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
# num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
# indices = np.linspace(0, 1, num_points)
# theta = 2 * np.pi * num_turns * indices
# radius = indices
# x = radius * np.cos(theta)
# y = radius * np.sin(theta)
# df = pd.DataFrame({
# "x": x,
# "y": y,
# "idx": indices,
# "rand": np.random.randn(num_points),
# })
# st.altair_chart(alt.Chart(df, height=700, width=700)
# .mark_point(filled=True)
# .encode(
# x=alt.X("x", axis=None),
# y=alt.Y("y", axis=None),
# color=alt.Color("idx", legend=None, scale=alt.Scale()),
# size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
# ))
import streamlit as st
import asyncio
import nest_asyncio
from your_chatbot_module import MCP_ChatBot # Assuming you put your chatbot code in a module
nest_asyncio.apply()
@st.cache_resource
def get_chatbot_instance():
api_key = st.secrets["LLAMA_API_KEY"] # Use Hugging Face Secrets for API key
return MCP_ChatBot(api_key=api_key)
chatbot = get_chatbot_instance()
st.title("MCP Chatbot on Hugging Face Spaces")
user_input = st.text_input("Enter your query:")
if st.button("Send") and user_input:
# Run the async chatbot query in the event loop
response_steps = asyncio.run(chatbot.connect_and_process(user_input))
# Extract final answer from steps
final_answer = ""
for step in response_steps:
if step.get("type") == "final_answer":
final_answer = step.get("content")
break
st.markdown("### Response:")
st.write(final_answer)