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import streamlit as st | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
# (Import other necessary libraries for visualization and Gemini API interaction) | |
# Define Color Palette | |
primary_color = "#3498db" # Example: Bright blue | |
secondary_color = "#e74c3c" # Example: Vibrant red | |
background_color = "#f0f0f0" # Example: Light gray | |
text_color = "#2c3e50" # Example: Dark gray | |
# Apply Styling | |
st.markdown( | |
f""" | |
<style> | |
.stApp {{ | |
background-color: {background_color}; | |
color: {text_color}; | |
}} | |
.sidebar .sidebar-content {{ | |
background-color: {primary_color}; | |
color: white; | |
}} | |
.stButton button {{ | |
background-color: {secondary_color}; | |
color: white; | |
}} | |
</style> | |
""", | |
unsafe_allow_html=True, | |
) | |
st.title("Event Management Financial Forecaster") | |
# Sidebar | |
gemini_api_key = st.sidebar.text_input("Google Gemini API Key") | |
# Sliders for Revenue Variables | |
st.sidebar.subheader("Revenue") | |
ticket_sales = st.sidebar.slider("Ticket Sales", 0, 100000, 50000) | |
# (Add sliders for other revenue variables) | |
# Sliders for Expense Variables | |
st.sidebar.subheader("Expenses") | |
venue_rental = st.sidebar.slider("Venue Rental", 0, 50000, 25000) | |
# (Add sliders for other expense variables) | |
# Calculate Financial Metrics | |
# (Implement the logic to calculate P&L, cash flow, sensitivity, KPIs based on slider values) | |
# Main Area | |
st.subheader("Projected P&L Statement") | |
# Visualization Styling for P&L | |
fig_pl, ax_pl = plt.subplots() | |
# ... (plot P&L data) | |
ax_pl.set_facecolor(background_color) | |
plt.grid(color="white", linestyle="--", linewidth=0.5) | |
# ... (other plot customizations) | |
st.pyplot(fig_pl) | |
st.subheader("Cash Flow Forecast") | |
# Visualization Styling for Cash Flow | |
fig_cf, ax_cf = plt.subplots() | |
# ... (plot cash flow data) | |
ax_cf.set_facecolor(background_color) | |
plt.grid(color="white", linestyle="--", linewidth=0.5) | |
# ... (other plot customizations) | |
st.pyplot(fig_cf) | |
# (Add other visualizations for sensitivity analysis and KPIs with similar styling) | |
# Optional: AI-Powered Insights (using Gemini API) | |
if gemini_api_key: | |
st.subheader("AI-Powered Insights") | |
# (Integrate Gemini API to generate analysis and scenario simulations based on input data) |