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import os
import streamlit as st
import yfinance as yf
import pandas as pd
from langchain.agents import create_csv_agent, AgentType
from langchain.chat_models import ChatOpenAI
from htmlTemplates import css, user_template, bot_template
from langchain.llms.base import LLM
from typing import Optional, List
import g4f
class FinLLM(LLM):
@property
def _llm_type(self) -> str:
return "custom"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
out = g4f.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}],
temperature=0.5, # You can adjust parameters as needed
max_tokens=350 # Adjust the token limit as needed
) #
if stop:
stop_indexes = (out.find(s) for s in stop if s in out)
min_stop = min(stop_indexes, default=-1)
if min_stop > -1:
out = out[:min_stop]
return out
llm = FinLLM()
def init_ses_states():
st.session_state.setdefault('chat_history', [])
def relative_returns(df):
rel = df.pct_change()
cumret = ((1 + rel).cumprod() - 1).fillna(0)
return cumret
def display_convo():
with st.container():
for i, message in enumerate(reversed(st.session_state.chat_history)):
if i % 2 == 0:
st.markdown(bot_template.replace("{{MSG}}", message), unsafe_allow_html=True)
else:
st.markdown(user_template.replace("{{MSG}}", message), unsafe_allow_html=True)
def main():
st.set_page_config(page_title="Stock Price AI Bot", page_icon=":chart:")
st.write(css, unsafe_allow_html=True)
init_ses_states()
st.title("Stock Price AI Bot")
st.caption("Visualizations and OpenAI Chatbot for Multiple Stocks Over A Specified Period")
with st.sidebar:
asset_tickers = sorted(['DOW', 'NVDA', 'TSL', 'GOOGL', 'AMZN', 'AI', 'NIO', 'LCID', 'F', 'LYFY', 'AAPL', 'MSFT', 'BTC-USD', 'ETH-USD'])
asset_dropdown = st.multiselect('Pick Assets:', asset_tickers)
metric_tickers = ['Adj. Close', 'Relative Returns']
metric_dropdown = st.selectbox("Metric", metric_tickers)
viz_tickers = ['Line Chart', 'Area Chart']
viz_dropdown = st.multiselect("Pick Charts:", viz_tickers)
start = st.date_input('Start', value=pd.to_datetime('2023-01-01'))
end = st.date_input('End', value=pd.to_datetime('today'))
if len(asset_dropdown) > 0:
df = yf.download(asset_dropdown, start, end)['Adj Close']
if metric_dropdown == 'Relative Returns':
df = relative_returns(df)
if len(viz_dropdown) > 0:
with st.expander("Data Visualizations", expanded=True):
if "Line Chart" in viz_dropdown:
st.line_chart(df)
if "Area Chart" in viz_dropdown:
st.area_chart(df)
st.header("Chat with your Data")
query = st.text_input("Enter a query:")
chat_prompt = f'''
You are an AI ChatBot intended to help with user stock data.
\nDATA MODE: {metric_dropdown}
\nSTOCKS: {asset_dropdown}
\nTIME PERIOD: {start} to {end}
\nCHAT HISTORY: {st.session_state.chat_history}
\nUSER MESSAGE: {query}
\nAI RESPONSE HERE:
'''
if st.button("Execute") and query:
with st.spinner('Generating response...'):
try:
DF = pd.DataFrame(df)
DF.to_csv('data.csv')
agent = create_csv_agent(
llm,
'data.csv',
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
)
answer = agent.run(chat_prompt)
st.session_state.chat_history.append(f"USER: {query}\n")
st.session_state.chat_history.append(f"AI: {answer}\n")
display_convo()
except Exception as e:
st.error(f"An error occurred: {str(e)}")
if __name__ == '__main__':
main()
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