Spaces:
Sleeping
Sleeping
File size: 2,178 Bytes
5218bf9 82a012a 5218bf9 82a012a 5218bf9 82a012a 5218bf9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
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) |