Update app.py
Browse files
app.py
CHANGED
@@ -4,20 +4,21 @@ import google.generativeai as genai
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import os
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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# Set page configuration
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st.set_page_config(page_title="AI-based Solar Project Estimation Tool", layout="centered")
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# Initialize Gemini
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api_key = os.getenv("GOOGLE_API_KEY")
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if api_key:
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genai.configure(api_key=api_key)
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else:
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st.error("API key is missing. Please set the GOOGLE_API_KEY environment variable.")
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model
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# Load solar data
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@st.cache_data
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@@ -27,85 +28,64 @@ def load_data():
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df = load_data()
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#
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def calculate_solar_estimate(roof_size, monthly_bill, electricity_price, ghi, cost_per_kw):
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daily_consumption_inr = monthly_bill / 30
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daily_consumption_kwh = daily_consumption_inr / electricity_price
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# Assume system size based on consumption
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estimated_system_size_kw = 3 # Fixed for now as realistic estimate
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peak_sun_hours = ghi # Use GHI as sun hours (approximation)
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daily_solar_output_kwh = estimated_system_size_kw * peak_sun_hours * 0.75 # considering derating factor
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total_system_cost = estimated_system_size_kw * cost_per_kw
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monthly_generation_kwh = daily_solar_output_kwh * 30
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monthly_savings_inr = monthly_generation_kwh * electricity_price
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annual_savings_inr = monthly_savings_inr * 12
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payback_period_years = total_system_cost / annual_savings_inr
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return {
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"Estimated solar system size in kW": round(estimated_system_size_kw, 2),
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"Estimated daily solar output in kWh": round(daily_solar_output_kwh, 2),
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"Total system cost in ₹": int(total_system_cost),
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"Monthly savings in ₹": int(monthly_savings_inr),
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"Payback period in years": round(payback_period_years, 2)
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}
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# UI - Form
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st.title("AI-based Solar Project Estimation Tool")
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st.write("### Enter Your Details:")
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with st.form("solar_form"):
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state_options = df['State'].dropna().unique()
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location = st.selectbox("Select your State", options=sorted(state_options))
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roof_size = st.number_input("Enter your roof size (in sq meters)", min_value=1)
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submitted = st.form_submit_button("Get Estimate")
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state_data = df[df['State'].str.contains(location, case=False)].iloc[0]
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if state_data is not None:
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ghi = state_data['Avg_GHI (kWh/m²/day)']
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solar_cost_per_kw = state_data['Solar_Cost_per_kW (₹)']
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electricity_price = 8 # Assume ₹8/kWh
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estimates = calculate_solar_estimate(roof_size, monthly_bill, electricity_price, ghi, solar_cost_per_kw)
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# Build clean prompt for Gemini to verify the calculation
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prompt = f"""
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ONLY output these 5 points based on inputs:
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1. Estimated solar system size in kW
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2. Estimated daily solar output in kWh
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3. Total system cost in ₹
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4. Monthly savings in ₹
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5. Payback period in years
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Solar system cost per kW = ₹{solar_cost_per_kw}
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"""
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# Call Gemini API (just to double-check/validate if you want)
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with st.spinner("Generating final estimate..."):
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gemini_response = model.generate_content(prompt)
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final_response = gemini_response.text.strip()
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# Display calculated values directly (without trusting Gemini text output)
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st.subheader("Solar Project Estimate")
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else:
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st.error("
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else:
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st.warning("Please fill all
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import os
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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# Set page configuration first
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st.set_page_config(page_title="AI-based Solar Project Estimation Tool", layout="centered")
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# Initialize Gemini with the API key loaded from the .env file
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api_key = os.getenv("GOOGLE_API_KEY")
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if api_key:
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genai.configure(api_key=api_key)
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else:
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st.error("API key is missing. Please set the GOOGLE_API_KEY environment variable.")
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# Use better model: gemini-1.5-pro
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model = genai.GenerativeModel("gemini-1.5-pro")
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# Load solar data
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@st.cache_data
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df = load_data()
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# UI - Form for user input
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st.title("AI-based Solar Project Estimation Tool")
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st.write("### Enter Your Details Below:")
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with st.form("solar_form"):
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state_options = df['State'].dropna().unique()
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location = st.selectbox("Select your State", options=sorted(state_options))
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roof_size = st.number_input("Enter your roof size (in sq meters)", min_value=1)
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electricity_bill = st.number_input("Enter your monthly electricity bill (₹)", min_value=0)
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submitted = st.form_submit_button("Get Estimate")
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# Build the clean prompt for Gemini
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def build_prompt(location, roof_size, electricity_bill, ghi, solar_cost_per_kw):
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prompt = f"""
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You are a solar project estimator tool. Based on the following details, calculate and return only the values without any extra description:
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Location: {location}
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Roof size: {roof_size} sq meters
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Monthly electricity bill: ₹{electricity_bill}
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Average GHI: {ghi} kWh/m²/day
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Solar system cost per kW: ₹{solar_cost_per_kw}
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Respond strictly in this format (do not add anything extra):
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Estimated solar system size in kW: <value>
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Estimated daily solar output in kWh: <value>
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Total system cost in ₹: <value>
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Monthly savings in ₹: <value>
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Payback period in years: <value>
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"""
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return prompt
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# Generate the solar project estimate via Gemini
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if submitted and location and roof_size > 0 and electricity_bill >= 0:
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state_data = df[df['State'].str.contains(location, case=False)].iloc[0]
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if state_data is not None:
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ghi = state_data['Avg_GHI (kWh/m²/day)']
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solar_cost_per_kw = state_data['Solar_Cost_per_kW (₹)']
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prompt_text = build_prompt(location, roof_size, electricity_bill, ghi, solar_cost_per_kw)
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# Call Gemini API
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with st.spinner("Generating solar estimate with Gemini..."):
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response = model.generate_content(prompt_text)
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# Display clean structured output
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st.subheader("Solar Project Estimate")
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estimated_data = response.text.strip().split("\n")
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for point in estimated_data:
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if ":" in point:
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key, value = point.split(":", 1)
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st.write(f"**{key.strip()}**: {value.strip()}")
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else:
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st.error("Sorry, the location entered does not match any available data.")
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else:
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st.warning("Please fill out all fields to see your solar project estimate.")
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