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import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import openai
# Load the solar data CSV file
df = pd.read_csv('solar_data_india_2024.csv')
# Set up the Gemini API (replace with your actual API key)
openai.api_key = 'your_gemini_api_key_here'
# Function to get solar radiation and tariff based on user input location
def get_state_data(state_name):
state_data = df[df['State'].str.contains(state_name, case=False)]
if state_data.empty:
return None
return state_data.iloc[0]
# Function to estimate solar system size (in kW)
def estimate_system_size(roof_size, ghi):
# Simple formula: 1 kW system needs about 10-12 sq meters of roof space
system_size = roof_size / 10 # in kW (assuming 10 sq meters = 1 kW)
estimated_output = ghi * system_size # kWh per day
return system_size, estimated_output
# Function to calculate cost, savings, and payback period
def estimate_cost_and_savings(system_size, state_data, electricity_bill):
# Cost per kW (₹)
solar_cost_per_kw = state_data['Solar_Cost_per_kW (₹)']
# Total solar system cost
system_cost = system_size * solar_cost_per_kw
# Savings per month assuming 100% replacement of electricity bill
savings_per_month = electricity_bill
# Payback period in months
payback_period = system_cost / (savings_per_month * 12)
return system_cost, savings_per_month, payback_period
# Streamlit UI
st.title("AI-based Solar Project Estimation Tool")
# User input form
st.sidebar.header("Enter Your Details")
location = st.sidebar.text_input("Enter your city or state", "")
roof_size = st.sidebar.number_input("Enter your roof size (in sq meters)", min_value=1)
electricity_bill = st.sidebar.number_input("Enter your monthly electricity bill (₹)", min_value=0)
if location and roof_size > 0 and electricity_bill >= 0:
# Fetch state data from the dataset
state_data = get_state_data(location)
if state_data is not None:
ghi = state_data['Avg_GHI (kWh/m²/day)']
# Estimate solar system size and output
system_size, estimated_output = estimate_system_size(roof_size, ghi)
# Estimate cost, savings, and payback period
system_cost, savings_per_month, payback_period = estimate_cost_and_savings(system_size, state_data, electricity_bill)
# Display results
st.subheader("Estimated Solar System Details:")
st.write(f"**Location**: {location}")
st.write(f"**System Size**: {system_size:.2f} kW")
st.write(f"**Estimated Daily Output**: {estimated_output:.2f} kWh/day")
st.write(f"**Total System Cost**: ₹{system_cost:,.2f}")
st.write(f"**Monthly Savings**: ₹{savings_per_month:,.2f}")
st.write(f"**Payback Period**: {payback_period:.2f} years")
# Plot savings and payback period
fig, ax = plt.subplots()
ax.bar(['Total System Cost', 'Monthly Savings', 'Payback Period'],
[system_cost, savings_per_month, payback_period*12], color=['blue', 'green', 'orange'])
ax.set_ylabel("Amount (₹) / Time (years)")
ax.set_title("Solar System Estimation Breakdown")
st.pyplot(fig)
else:
st.error("Sorry, the location entered does not match any available data.")
else:
st.warning("Please fill out all fields to see your solar project estimate.")
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