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


# Load environment variables from .env file
load_dotenv()

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

# Initialize Gemini with the API key
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 Gemini-1.5-Pro model
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()

# Build prompt for Gemini
def build_prompt(location, project_type, roof_size=None, desired_kwh=None, electricity_bill=None, ghi=None, solar_cost_per_kw=None):
    if project_type == "Rooftop Solar":
        prompt = f"""
You are a solar project estimator tool. Based on the following details, calculate and return only the values without any extra description:
Project Type: Rooftop Solar
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>
"""
    else:  # Ground Mount Solar
        prompt = f"""
You are a solar project estimator tool. Based on the following details, calculate and return only the values without any extra description:
Project Type: Ground Mount Solar
Location: {location}
Desired monthly solar production: {desired_kwh} kWh
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):
Required 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

# 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))
    
    project_type = st.radio(
        "Select Solar Project Type",
        options=["Rooftop Solar", "Ground Mount Solar"]
    )

    if project_type == "Rooftop Solar":
        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)
        desired_kwh = None
    else:
        desired_kwh = st.number_input("Enter desired monthly solar electricity production (kWh)", min_value=1)
        electricity_bill = st.number_input("Enter your monthly electricity bill (₹)", min_value=0)
        roof_size = None

    submitted = st.form_submit_button("Get Estimate")

# Generate the solar project estimate via Gemini
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 (₹)']

        prompt_text = build_prompt(location, project_type, roof_size=roof_size, desired_kwh=desired_kwh, electricity_bill=electricity_bill, ghi=ghi, solar_cost_per_kw=solar_cost_per_kw)
        
        # Call Gemini API
        with st.spinner("Generating solar estimate with Gemini..."):
            response = model.generate_content(prompt_text)
        
        # Display structured output
        st.subheader("🔹 Solar Project Estimate")
        
        estimated_data = response.text.strip().split("\n")
        
        system_size_kw = None
        monthly_savings_rs = None
        total_system_cost = None
        payback_period_years = 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 or "Required solar system size" in key:
                        system_size_kw = float(value.split()[0])
                    if "Monthly savings" in key:
                        monthly_savings_rs = float(value.split()[0])
                    if "Total system cost" in key:
                        total_system_cost = float(value.split()[0])
                    if "Payback period" in key:
                        payback_period_years = float(value.split()[0])

                except ValueError:
                    st.warning("There was an issue processing the response. Please try again.")

        # Show Graph if values are available
        if total_system_cost is not None and monthly_savings_rs is not None and payback_period_years is not None:
            st.subheader("📊 Visual Summary")

            # Prepare data for Area Chart
            months = int(payback_period_years * 12)
            savings_cumulative = [monthly_savings_rs * month for month in range(1, months + 1)]

            fig = go.Figure()

            # Area plot
            fig.add_trace(go.Scatter(
                x=list(range(1, months + 1)),
                y=savings_cumulative,
                mode='lines',
                fill='tozeroy',
                name='Cumulative Savings (₹)',
                line=dict(color='#00CC96')
            ))

            # Line for Total Cost
            fig.add_trace(go.Scatter(
                x=[1, months],
                y=[total_system_cost, total_system_cost],
                mode='lines',
                name='Total System Cost (₹)',
                line=dict(color='red', dash='dash')
            ))

            fig.update_layout(
                title="Cumulative Savings vs Total System Cost Over Time",
                xaxis_title="Months",
                yaxis_title="Amount (₹)",
                legend_title="Legend",
                template="plotly_white"
            )

            st.plotly_chart(fig, use_container_width=True, key="solar_graph")

    else:
        st.error("Sorry, the location entered does not match any available data.")
else:
    st.info("Please fill out all fields to get your solar project estimate.")