| 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 |
| import csv |
| from io import StringIO |
|
|
| |
| st.set_page_config(page_title="AI-based Solar Project Estimation Tool", layout="centered") |
|
|
| |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) |
| 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: |
| 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) |
| |
| |
| if prompt_text: |
| with st.spinner("Generating solar estimate with Gemini..."): |
| response = model.generate_content(prompt_text) |
| st.subheader("Solar Project Estimate") |
| st.text_area("Generated Estimate", response.text, height=200) |
| else: |
| st.warning("Please check the inputs again.") |
| 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.") |
|
|
| |
| 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"]) |
| |
| |
| 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] |
| |
| |
| 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.") |
|
|
