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import streamlit as st
import pandas as pd
import yfinance as yf
import seaborn as sns
import matplotlib.pyplot as plt
# import openai
def load_and_display_pl(ticker1, ticker2):
try:
# Fetch P&L data
ticker1_data = yf.Ticker(ticker1).financials
ticker2_data = yf.Ticker(ticker2).financials
# Transpose for better visualization
ticker1_df = ticker1_data.transpose()
ticker2_df = ticker2_data.transpose()
# Basic data cleaning (adjust as needed)
ticker1_df = ticker1_df.fillna(0) # Fill missing values with 0
ticker2_df = ticker2_df.fillna(0)
ticker1_df = ticker1_df[['Total Revenue', 'Cost Of Revenue', 'Gross Profit', 'Operating Income', 'Operating Expense', 'EBIT', 'EBITDA', 'Pretax Income','Tax Provision','Net Interest Income','Net Income', 'Basic EPS']]
ticker2_df = ticker2_df[['Total Revenue', 'Cost Of Revenue', 'Gross Profit', 'Operating Income', 'Operating Expense', 'EBIT', 'EBITDA', 'Pretax Income','Tax Provision','Net Interest Income','Net Income', 'Basic EPS']]
# Calculate and visualize horizontal analysis
ticker1_pct_change = ticker1_df.pct_change(axis=1) * 100
ticker2_pct_change = ticker2_df.pct_change(axis=1) * 100
ticker1_pct_change_1 = ticker1_pct_change[['Gross Profit', 'Pretax Income', 'Net Income']]
ticker2_pct_change_2 = ticker2_pct_change[['Gross Profit', 'Pretax Income', 'Net Income']]
#calculate the vertical analysis
ticker_1_vertical = ticker1_df.T.div(ticker1_df['Total Revenue'], axis=1).round(2) * 100
ticker_2_vertical = ticker2_df.T.div(ticker2_df['Total Revenue'], axis=1).round(2) * 100
#MARGINS & Ratios
#ticker one
ticker1_df['Operating_profit_Margin'] = (ticker1_df['Operating Income'] /ticker1_df['Total Revenue']) *100
ticker1_df['Operating_Expense_Ratio'] = (ticker1_df['Operating Expense'] /ticker1_df['Total Revenue']) *100
ticker1_df['Interest_Coverage_Ratio'] = ticker1_df['EBIT'] / abs(ticker1_df['Net Interest Income'])
ticker1_df['Effective_Tax_Rate'] = (ticker1_df['Tax Provision'] / ticker1_df['Pretax Income']) * 100
ticker1_df['Net_profit_Margin'] = (ticker1_df['Net Income'] /ticker1_df['Total Revenue']) *100
ticker2_df['Operating_profit_Margin'] = (ticker2_df['Operating Income'] /ticker2_df['Total Revenue']) *100
ticker2_df['Operating_Expense_Ratio'] = (ticker2_df['Operating Expense'] /ticker2_df['Total Revenue']) *100
ticker2_df['Interest_Coverage_Ratio'] = ticker2_df['EBIT'] / abs(ticker2_df['Net Interest Income'])
ticker2_df['Effective_Tax_Rate'] = (ticker2_df['Tax Provision'] / ticker2_df['Pretax Income']) * 100
ticker2_df['Net_profit_Margin'] = (ticker2_df['Net Income'] /ticker2_df['Total Revenue']) *100
Profit_Margins_1 = ticker1_df[['Operating_profit_Margin', 'Operating_Expense_Ratio', 'Interest_Coverage_Ratio', 'Effective_Tax_Rate', 'Net_profit_Margin']]
Profit_Margins_2 = ticker2_df[['Operating_profit_Margin', 'Operating_Expense_Ratio', 'Interest_Coverage_Ratio', 'Effective_Tax_Rate', 'Net_profit_Margin']]
# Display side-by-side
col1, col2 = st.columns(2)
with col1:
st.header(f"Vertical Analysis : {ticker1}")
st.dataframe(ticker_1_vertical)
st.header(f"Horizantal Analysis : {ticker1}")
st.dataframe(ticker1_pct_change)
st.header(f"Trend Analysis : {ticker1}")
st.line_chart(ticker1_pct_change_1)
# st.dataframe(Profit_Margins)
fig, ax =plt.subplots()
plt.title(f"Margins & Tax coverage Ratio of : {ticker1}")
sns.heatmap(Profit_Margins_1, annot=True, fmt='.2f', cmap='coolwarm', ax=ax)
st.pyplot(fig)
with col2:
st.header(f"Vertical Analysis of : {ticker2}")
st.dataframe(ticker_2_vertical)
st.header(f"Horizantal Analysis : {ticker2}")
st.dataframe(ticker2_pct_change)
st.header(f"Trend Analysis of :{ticker2}")
st.line_chart(ticker2_pct_change_2)
fig, ax =plt.subplots()
plt.title(f"Margins & Tax coverage Ratio of : {ticker2}")
sns.heatmap(Profit_Margins_2, annot=True, fmt='.2f', cmap='coolwarm', ax=ax)
st.pyplot(fig)
# # GPT-3 Integration
# openai.api_key = ""
# # Prepare data for GPT-3
# ticker1_summary = f"**{ticker1}**\n\n" \
# f"**Vertical Analysis:**\n{ticker_1_vertical.to_markdown()}\n\n" \
# f"**Horizontal Analysis:**\n{ticker1_pct_change.to_markdown()}\n\n" \
# f"**Key Ratios:**\n{Profit_Margins_1.to_markdown()}\n\n"
# ticker2_summary = f"**{ticker2}**\n\n" \
# f"**Vertical Analysis:**\n{ticker_2_vertical.to_markdown()}\n\n" \
# f"**Horizontal Analysis:**\n{ticker2_pct_change.to_markdown()}\n\n" \
# f"**Key Ratios:**\n{Profit_Margins_2.to_markdown()}\n\n"
# prompt = f"Compare and contrast the financial performance of the two companies based on their P&L data:\n\n" \
# f"{ticker1_summary}\n\n" \
# f"{ticker2_summary}\n\n" \
# f"Provide insights into their profitability, growth trends, and operational efficiency."
# # Generate response from GPT-3
# response = openai.Completion.create(
# engine="text-ada-001", # Choose an appropriate engine
# prompt=prompt,
# max_tokens=1024,
# n=1,
# stop=None,
# temperature=0.7
# )
# # Display GPT-3's analysis
# st.header("GPT-3 Analysis")
# st.write(response.choices[0].text)
except Exception as e:
st.error(f"An error occurred: {e}")
return load_and_display_pl
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