app.py

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import openai

# Load the solar data CSV file
df = pd.read_csv('https://huggingface.co/spaces/MLDeveloper/AI_based_Solar_Project_Estimation_Tool/resolve/main/solar_data_india_2024.csv')

# Set up the openai API key (replace with your actual OpenAI API key)
openai.api_key = 'your_openai.api_key_here'

# Streamlit UI
st.set_page_config(page_title="AI-based Solar Project Estimation Tool", layout="centered")
st.title("AI-based Solar Project Estimation Tool")

# Center all input widgets
st.write("### Enter Your Details Below:")

with st.form("solar_form"):
    # Get the list of unique states from the dataset
    state_options = df['State'].dropna().unique()
    
    # Input widgets
    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")

if submitted and location and roof_size > 0 and electricity_bill >= 0:
    # Fetch state data from the dataset
    state_data = df[df['State'].str.contains(location, case=False)].iloc[0]  # Get the first match
    
    if state_data is not None:
        ghi = state_data['Avg_GHI (kWh/m²/day)']
        solar_cost_per_kw = state_data['Solar_Cost_per_kW (₹)']
        
        # Use LLM to generate solar project estimate (cost, savings, payback period)
        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
        """

        # Get response from OpenAI API
        response = openai.Completion.create(
            engine="gpt-4",  # You can change this to another GPT model if needed
            prompt=prompt,
            max_tokens=250,
            n=1,
            stop=None,
            temperature=0.7,
        )
        
        # Extract the result
        result = response.choices[0].text.strip()
        
        # Display the response from the model
        st.subheader("Estimated Solar System Details:")
        st.write(result)
        
    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.")