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'''
Fintelligence (C) 2024
Intelligent Finance
Released in Apache 2.0 license
'''
import streamlit as st
import os
from openai import OpenAI
import json
# Initialize the client
client = OpenAI(
base_url="https://api-inference.huggingface.co/v1",
api_key=os.environ.get('HUGGINGFACE_API_TOKEN')
)
# Model configuration
MODEL = "HuggingFaceH4/zephyr-7b-beta"
# Define a custom function
def get_current_weather(location, unit="celsius"):
"""Get the current weather in a given location"""
weather_info = {
"location": location,
"temperature": "22",
"unit": unit,
"forecast": ["sunny", "windy"],
}
return weather_info
# Set up the Streamlit app
st.title("Chatbot with Hugging Face and Zephyr-7B-Beta")
# 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"])
# Accept user input
if prompt := st.chat_input("What is up?"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Prepare the messages for the API call
messages = [
{"role": msg["role"], "content": msg["content"]}
for msg in st.session_state.messages
]
# Call the Hugging Face API
try:
response = client.chat.completions.create(
model=MODEL,
messages=messages,
functions=[
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
}
],
function_call="auto",
)
# Process the response
assistant_response = response.choices[0].message.content
function_call = response.choices[0].message.function_call
if function_call:
function_name = function_call.name
function_args = json.loads(function_call.arguments)
if function_name == "get_current_weather":
function_response = get_current_weather(**function_args)
# Call the API again with the function response
messages.append({"role": "function", "name": function_name, "content": json.dumps(function_response)})
final_response = client.chat.completions.create(
model=MODEL,
messages=messages
)
assistant_response = final_response.choices[0].message.content
# Display assistant response in chat message container
with st.chat_message("assistant"):
st.markdown(assistant_response)
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": assistant_response})
except Exception as e:
st.error(f"An error occurred: {str(e)}")
# Note: Token usage information is not available with the Hugging Face API
st.sidebar.write("Note: Token usage information is not available with the Hugging Face API.") |