File size: 2,864 Bytes
b555fe1 b0848d8 b555fe1 0b6ddc5 b555fe1 5aee751 cd574eb b555fe1 b0848d8 b555fe1 b0848d8 b555fe1 b0848d8 b555fe1 5aee751 b555fe1 5aee751 b555fe1 5aee751 b555fe1 5aee751 b555fe1 5aee751 b555fe1 5aee751 b555fe1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
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.")
|