app.py

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


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
load_dotenv()

# Set page configuration
st.set_page_config(page_title="☀️AI-Based Solar Project Estimation Tool", layout="centered")

# 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()

# Constants
TARIFF_RATE = 7  # ₹7 per kWh
ROOFTOP_CONVERSION_FACTOR = 0.10  # 0.10 kW per sq meter

# UI - Form
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))
    
    st.radio(
        "Select Solar Project Type",
        options=["Rooftop Solar"],
        index=0,
        disabled=True
    )

    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")

# Calculate directly
if submitted and location:
    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 (₹)']

        system_size_kw = round(roof_size * ROOFTOP_CONVERSION_FACTOR, 2)
        estimated_daily_output = round(system_size_kw * ghi, 2)

        total_system_cost = round(system_size_kw * solar_cost_per_kw, 2)
        monthly_savings = round(estimated_daily_output * 30 * TARIFF_RATE, 2)
        payback_period = round(total_system_cost / (monthly_savings * 12), 2)

        # Display Results
        st.subheader("🔹 Solar Project Estimate")
        st.write(f"**Estimated solar system size in kW**: {system_size_kw}")
        st.write(f"**Estimated daily solar output in kWh**: {estimated_daily_output}")
        st.write(f"**Total system cost in ₹**: {total_system_cost}")
        st.write(f"**Monthly savings in ₹**: {monthly_savings}")
        st.write(f"**Payback period in years**: {payback_period}")

        # Visual Summary
        st.subheader("📊 Visual Summary")
        fig = go.Figure(data=[ 
            go.Bar(
                name="System Parameters",
                x=["System Size (kW)", "Daily Output (kWh)", "Total Cost (₹)", "Monthly Savings (₹)", "Payback (Years)"],
                y=[system_size_kw, estimated_daily_output, total_system_cost, monthly_savings, payback_period],
                marker_color='#636EFA'
            )
        ])
        fig.update_layout(
            title="Solar System Estimation Overview",
            yaxis_title="Values",
            xaxis_title="Parameters"
        )
        st.plotly_chart(fig, use_container_width=True)

        st.info("Note: Tariff assumed ₹7/kWh. Actual payback may vary based on location, grid policy, and maintenance.")

    else:
        st.error("State data not found. Please try a valid state.")
else:
    st.warning("Please complete all fields to get your estimate.")