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import streamlit as st
import pandas as pd
import google.generativeai as genai
import os
from dotenv import load_dotenv
import plotly.graph_objects as go
# Load environment variables from .env file
load_dotenv()
# Set page configuration first
st.set_page_config(page_title="AI-based Solar Project Estimation Tool", layout="centered")
# Initialize Gemini with the API key loaded from the .env file
api_key = os.getenv("GOOGLE_API_KEY")
if api_key:
genai.configure(api_key=api_key)
else:
st.error("API key is missing. Please set the GOOGLE_API_KEY environment variable.")
# Use better model: gemini-1.5-pro
model = genai.GenerativeModel("gemini-1.5-pro")
# Load solar data
@st.cache_data
def load_data():
df = pd.read_csv('https://huggingface.co/spaces/MLDeveloper/AI_based_Solar_Project_Estimation_Tool/resolve/main/solar_data_india_2024.csv')
return df
df = load_data()
# UI - Form for user input
st.title("AI-based Solar Project Estimation Tool")
st.write("### Enter Your Details Below:")
with st.form("solar_form"):
state_options = df['State'].dropna().unique()
location = st.selectbox("Select your State", options=sorted(state_options))
roof_size = st.number_input("Enter your roof size (in sq meters)", min_value=1)
electricity_bill = st.number_input("Enter your monthly electricity bill (₹)", min_value=0)
submitted = st.form_submit_button("Get Estimate")
# Build the clean prompt for Gemini
def build_prompt(location, roof_size, electricity_bill, ghi, solar_cost_per_kw):
prompt = f"""
You are a solar project estimator tool. Based on the following details, calculate and return only the values without any extra description:
Location: {location}
Roof size: {roof_size} sq meters
Monthly electricity bill: ₹{electricity_bill}
Average GHI: {ghi} kWh/m²/day
Solar system cost per kW: ₹{solar_cost_per_kw}
Respond strictly in this format (do not add anything extra):
Estimated solar system size in kW: <value>
Estimated daily solar output in kWh: <value>
Total system cost in ₹: <value>
Monthly savings in ₹: <value>
Payback period in years: <value>
"""
return prompt
# Generate the solar project estimate via Gemini
if submitted and location and roof_size > 0 and electricity_bill >= 0:
state_data = df[df['State'].str.contains(location, case=False)].iloc[0]
if state_data is not None:
ghi = state_data['Avg_GHI (kWh/m²/day)']
solar_cost_per_kw = state_data['Solar_Cost_per_kW (₹)']
prompt_text = build_prompt(location, roof_size, electricity_bill, ghi, solar_cost_per_kw)
# Call Gemini API
with st.spinner("Generating solar estimate with Gemini..."):
response = model.generate_content(prompt_text)
# Display clean structured output
st.subheader("Solar Project Estimate")
estimated_data = response.text.strip().split("\n")
for point in estimated_data:
if ":" in point:
key, value = point.split(":", 1)
st.write(f"**{key.strip()}**: {value.strip()}")
# Variables to store extracted values for graph
system_size_kw = None
monthly_savings_rs = None
for point in estimated_data:
if ":" in point:
try:
key, value = point.split(":", 1)
key = key.strip()
value = value.strip()
st.write(f"**{key}**: {value}")
if "Estimated solar system size" in key:
system_size_kw = float(value.split()[0])
if "Monthly savings" in key:
monthly_savings_rs = float(value.split()[0])
except ValueError:
st.warning("There was an issue processing the response.")
# Show Graph if available
if system_size_kw is not None and monthly_savings_rs is not None:
st.subheader("📊 Visual Summary")
fig = go.Figure(data=[
go.Bar(name='Estimate',
x=['Solar System Size (kW)', 'Monthly Savings (₹)'],
y=[system_size_kw, monthly_savings_rs],
marker_color=['#636EFA', '#00CC96'])
])
fig.update_layout(title="Solar Project Estimates", yaxis_title="Values", xaxis_title="Parameters")
st.plotly_chart(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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