| import streamlit as st |
| import pandas as pd |
| import google.generativeai as genai |
| import os |
| from io import StringIO |
| import csv |
| from dotenv import load_dotenv |
|
|
|
|
| |
| load_dotenv() |
|
|
| |
| st.set_page_config(page_title="AI-based Solar Project Estimation Tool", layout="centered") |
|
|
| |
| 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.") |
|
|
| model = genai.GenerativeModel("gemini-1.5-flash") |
|
|
| |
| @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() |
|
|
| |
| 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") |
|
|
| |
| 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 |
|
|
| |
| 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) |
| |
| |
| with st.spinner("Generating solar estimate with Gemini..."): |
| response = model.generate_content(prompt_text) |
| |
| |
| st.write("Raw Response:", response.text) |
| |
| |
| st.subheader("Solar Project Estimate") |
| |
| |
| estimated_data = response.text.strip().split("\n") |
| |
| |
| system_size = None |
| total_cost = None |
| monthly_savings = None |
| payback_period = None |
| |
| |
| for point in estimated_data: |
| if "solar system size" in point.lower(): |
| system_size = point.split(":")[1].strip() |
| elif "total system cost" in point.lower(): |
| total_cost = point.split(":")[1].strip() |
| elif "monthly savings" in point.lower(): |
| monthly_savings = point.split(":")[1].strip() |
| elif "payback period" in point.lower(): |
| payback_period = point.split(":")[1].strip() |
|
|
| |
| if system_size and total_cost and monthly_savings and payback_period: |
| st.write(f"1. Estimated solar system size in kW: {system_size}") |
| st.write(f"2. Total system cost in ₹: {total_cost}") |
| st.write(f"3. Monthly savings in ₹: {monthly_savings}") |
| st.write(f"4. Payback period in years: {payback_period}") |
| else: |
| st.error("Unable to extract valid data from the response.") |
| 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.") |
|
|
