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


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

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

# Initialize Gemini
genai.configure(api_key=os.getenv("GEMINI_API_KEY"))  # Ensure your Gemini API key is set as an environment variable
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.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 the following:
    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)
        
        # Call Gemini API once for all the batch generation
        with st.spinner("Generating solar estimate with Gemini..."):
            response = model.generate_content(prompt_text)
        
        # Display structured output
        st.subheader("Solar Project Estimate")
        
        # Break down the response into structured points
        estimated_data = response.text.strip().split("\n")
        for point in estimated_data:
            st.write(f"- {point}")
    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"])
        
        # Call Gemini API once for the batch generation
        with st.spinner("Generating batch estimates..."):
            batch_prompts = [build_prompt(location, roof_size, electricity_bill, ghi, solar_cost_per_kw) for _ in range(batch)]
            batch_responses = [model.generate_content(prompt) for prompt in batch_prompts]
            
            # Process the batch responses and write to CSV
            for i, response in enumerate(batch_responses, 1):
                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.")