app.py

import streamlit as st
import pandas as pd
import google.generativeai as genai
import os
from dotenv import load_dotenv

# 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()}")
    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.")