app.py

import streamlit as st
import pandas as pd
import google.generativeai as genai
import os
from io import StringIO

# Initialize Gemini
genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
model = genai.GenerativeModel("gemini-1.5-flash")

# 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.set_page_config(page_title="AI-based Solar Project Estimation Tool", layout="centered")
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 prompt for Gemini
def build_prompt(location, roof_size, electricity_bill, ghi, solar_cost_per_kw):
    prompt = f"""
    Estimate the solar system for the location '{location}' based on the following details:
    - Roof size: {roof_size} sq meters
    - Monthly electricity bill: ₹{electricity_bill}
    - Average GHI (solar radiation) for {location}: {ghi} kWh/m²/day
    - Solar system cost per kW in {location}: ₹{solar_cost_per_kw}

    Provide:
    1. Estimated solar system size in kW
    2. Estimated daily solar output in kWh
    3. Total system cost in ₹
    4. Monthly savings in ₹
    5. Payback period in years
    """
    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)
        
        if prompt_text:
            with st.spinner("Generating solar estimate with Gemini..."):
                response = model.generate_content(prompt_text)
            st.subheader("Solar Project Estimate")
            st.text_area("Generated Estimate", response.text, height=200)
        else:
            st.warning("Please check the inputs again.")
    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.")

# Batch CSV Export
st.markdown("### Export Solar Estimates")
batch = st.number_input("How many estimates to generate?", min_value=1, max_value=100, value=5)

if st.button("Generate Batch & Download CSV"):
    if location and roof_size > 0 and electricity_bill >= 0:
        csv_buffer = StringIO()
        writer = csv.writer(csv_buffer)
        writer.writerow(["Sequence_no", "Solar Estimate"])
        
        with st.spinner("Generating estimates..."):
            for i in range(1, batch + 1):
                state_data = df[df['State'].str.contains(location, case=False)].iloc[0]
                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)
                
                # Generate the estimate using Gemini
                response = model.generate_content(prompt_text)
                writer.writerow([i, response.text.strip()])
        
        st.download_button(
            label="Download Solar Estimates CSV",
            data=csv_buffer.getvalue(),
            file_name="solar_estimates.csv",
            mime="text/csv"
        )
    else:
        st.warning("Please fill out all fields to generate batch estimates.")