Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,250 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+import numpy_financial as npf
+from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
+from reportlab.lib.pagesizes import A4
+from reportlab.lib.styles import getSampleStyleSheet
+import io
+import xlsxwriter
+
+# ===============================
+# CONFIGURATION
+# ===============================
+SYSTEM_LOSSES = 0.20
+PANEL_COST_PER_WATT = 55
+INSTALLATION_COST_PER_WATT = 35
+LITHIUM_BATTERY_COST_5KWH = 95000
+
+CITY_SUNLIGHT = {
+    "Karachi": 6.2,
+    "Lahore": 5.5,
+    "Islamabad": 5.2,
+    "Peshawar": 5.6,
+    "Quetta": 6.5,
+}
+
+APPLIANCES_RESIDENTIAL = {
+    "LED Bulb (12W)": 12,
+    "Fan (80W)": 80,
+    "Refrigerator (200W)": 200,
+    "LED TV (150W)": 150,
+    "Air Conditioner 1.5 Ton (1500W)": 1500,
+    "Washing Machine (500W)": 500,
+    "Water Pump (750W)": 750,
+    "Laptop (65W)": 65,
+    "Iron (1000W)": 1000,
+}
+
+APPLIANCES_COMMERCIAL = {
+    "CNC Machine (2kW)": 2000,
+    "Industrial AC (5kW)": 5000,
+    "Lighting System (1kW)": 1000,
+    "Water Pump 3HP (2.2kW)": 2200,
+    "Server Rack (1.5kW)": 1500,
+}
+
+RESIDENTIAL_TARIFF = [
+    (100, 22),
+    (100, 32),
+    (100, 38),
+    (100, 42),
+    (100, 48),
+    (np.inf, 65),
+]
+
+COMMERCIAL_TARIFF = 72  # PKR/unit average
+
+# ===============================
+# FUNCTIONS
+# ===============================
+
+def calculate_residential_bill(units):
+    remaining = units
+    bill = 0
+    for slab_units, rate in RESIDENTIAL_TARIFF:
+        if remaining > slab_units:
+            bill += slab_units * rate
+            remaining -= slab_units
+        else:
+            bill += remaining * rate
+            break
+    return bill
+
+def calculate_commercial_bill(units):
+    return units * COMMERCIAL_TARIFF
+
+def calculate_system(load_watts, hours, sunlight):
+    daily_kwh = (load_watts * hours) / 1000
+    adjusted_kwh = daily_kwh / (1 - SYSTEM_LOSSES)
+    required_kw = adjusted_kwh / sunlight
+    return daily_kwh, round(required_kw, 2)
+
+def calculate_battery(daily_kwh, backup_hours):
+    backup_kwh = (daily_kwh / 24) * backup_hours
+    batteries = math.ceil(backup_kwh / 5)
+    return batteries
+
+def calculate_cost(system_kw, batteries, system_type):
+    base_cost = system_kw * 1000 * (PANEL_COST_PER_WATT + INSTALLATION_COST_PER_WATT)
+    battery_cost = batteries * LITHIUM_BATTERY_COST_5KWH
+    if system_type == "On-Grid":
+        return base_cost
+    elif system_type == "Off-Grid":
+        return base_cost + battery_cost
+    else:
+        return base_cost * 1.1 + battery_cost
+
+def emi_calculator(principal, annual_rate, years):
+    r = annual_rate / 100 / 12
+    n = years * 12
+    emi = principal * r * (1 + r)**n / ((1 + r)**n - 1)
+    return round(emi)
+
+def financial_projection(total_cost, daily_kwh, mode, years=25, inflation_rate=5, energy_price_increase=7):
+    monthly_units = daily_kwh * 30
+    cashflows = []
+    for year in range(1, years+1):
+        if mode == "Homeowner":
+            monthly_bill = calculate_residential_bill(monthly_units * ((1 + energy_price_increase/100)**(year-1)))
+        else:
+            monthly_bill = calculate_commercial_bill(monthly_units * ((1 + energy_price_increase/100)**(year-1)))
+        annual_savings = monthly_bill * 12
+        cashflows.append(annual_savings)
+    npv = npf.npv(inflation_rate/100, [-total_cost]+cashflows)
+    irr = npf.irr([-total_cost]+cashflows)
+    payback_year = next((i for i, cf in enumerate(np.cumsum(cashflows), 1) if cf >= total_cost), None)
+    cumulative_savings = np.cumsum(cashflows)
+    return cashflows, round(npv,2), round(irr*100,2), payback_year, cumulative_savings
+
+def generate_pdf(report_data):
+    file_path = "solar_report.pdf"
+    doc = SimpleDocTemplate(file_path, pagesize=A4)
+    elements = []
+    styles = getSampleStyleSheet()
+    elements.append(Paragraph("<b>Pakistan Solar Feasibility Report</b>", styles['Title']))
+    elements.append(Spacer(1, 12))
+    for key, value in report_data.items():
+        elements.append(Paragraph(f"<b>{key}:</b> {value}", styles['Normal']))
+        elements.append(Spacer(1, 8))
+    doc.build(elements)
+    return file_path
+
+def generate_excel(report_data):
+    output = io.BytesIO()
+    workbook = xlsxwriter.Workbook(output)
+    worksheet = workbook.add_worksheet("Solar Report")
+    bold = workbook.add_format({'bold': True})
+    row = 0
+    for key, value in report_data.items():
+        worksheet.write(row, 0, key, bold)
+        worksheet.write(row, 1, str(value))
+        row += 1
+    workbook.close()
+    output.seek(0)
+    return output
+
+# ===============================
+# STREAMLIT APP
+# ===============================
+st.set_page_config(layout="wide")
+st.title("🇵🇰 Pakistan Solar Engineering & Financial Dashboard")
+
+audience = st.selectbox("Select Audience", ["Homeowner", "Solar Company", "Industrial Investor"])
+city = st.selectbox("Select City", list(CITY_SUNLIGHT.keys()))
+sunlight = CITY_SUNLIGHT[city]
+
+if audience == "Homeowner":
+    appliances = st.multiselect("Select Appliances", list(APPLIANCES_RESIDENTIAL.keys()))
+elif audience == "Solar Company":
+    appliances = st.multiselect("Select Residential / Commercial Appliances", 
+                                list(APPLIANCES_RESIDENTIAL.keys()) + list(APPLIANCES_COMMERCIAL.keys()))
+else:
+    appliances = st.multiselect("Select Industrial Equipment", list(APPLIANCES_COMMERCIAL.keys()))
+
+hours = st.slider("Usage Hours per Day", 1, 24, 8)
+system_type = st.radio("System Type", ["On-Grid", "Off-Grid", "Hybrid"])
+backup_hours = st.slider("Battery Backup Hours", 0, 24, 4)
+
+if st.button("Calculate Solar System"):
+    if not appliances:
+        st.error("Please select at least one appliance or equipment")
+    else:
+        total_load = sum(APPLIANCES_RESIDENTIAL.get(a,0) + APPLIANCES_COMMERCIAL.get(a,0) for a in appliances)
+        daily_kwh, system_kw = calculate_system(total_load, hours, sunlight)
+        batteries = calculate_battery(daily_kwh, backup_hours)
+        total_cost = calculate_cost(system_kw, batteries, system_type)
+
+        interest = st.slider("Bank Interest Rate (%)", 5, 25, 15)
+        years_loan = st.slider("Loan Duration (Years)", 1, 10, 5)
+        emi = emi_calculator(total_cost, interest, years_loan)
+
+        cashflows, npv, irr, payback_year, cumulative_savings = financial_projection(total_cost, daily_kwh, audience)
+
+        # Display Results
+        st.subheader("System Analysis")
+        st.write(f"Total Load: {total_load} W")
+        st.write(f"Daily Energy Consumption: {round(daily_kwh,2)} kWh")
+        st.write(f"Required System Size: {system_kw} kW")
+        st.write(f"Battery Units Required (5kWh each): {batteries}")
+        st.write(f"Estimated System Cost: PKR {round(total_cost):,}")
+        st.write(f"EMI (Monthly): PKR {emi:,}")
+        st.write(f"25-Year Projection: NPV = PKR {npv:,}, IRR = {irr}%, Payback Year = {payback_year}")
+
+        # Dashboard
+        st.subheader("🔹 Daily Load vs Solar Generation")
+        hours_day = np.arange(0,24,1)
+        load_profile = np.array([total_load]*24)
+        solar_profile = np.array([system_kw*1000/sunlight]*24)
+        plt.figure(figsize=(10,4))
+        plt.plot(hours_day, load_profile, label="Load (W)")
+        plt.plot(hours_day, solar_profile, label="Solar Generation (W)")
+        plt.xlabel("Hour of Day")
+        plt.ylabel("Power (W)")
+        plt.title("Daily Load vs Solar Generation")
+        plt.legend()
+        st.pyplot(plt)
+
+        st.subheader("🔹 Cumulative Savings Over 25 Years")
+        plt.figure(figsize=(10,4))
+        plt.plot(range(1,26), cumulative_savings, marker='o')
+        plt.axhline(total_cost, color='r', linestyle='--', label="Total System Cost")
+        plt.xlabel("Year")
+        plt.ylabel("Cumulative Savings (PKR)")
+        plt.title("Payback & Savings Curve")
+        plt.legend()
+        st.pyplot(plt)
+
+        st.subheader("🔹 Yearly Cashflows")
+        df_cashflow = pd.DataFrame({"Year": range(1,26), "Annual Savings (PKR)": cashflows})
+        st.dataframe(df_cashflow)
+
+        st.subheader("🔹 Carbon Emission Reduction Estimate")
+        co2_per_kwh = 0.85
+        total_co2_saved = round(daily_kwh * 365 * 25 * co2_per_kwh)
+        st.write(f"Estimated CO2 Reduction over 25 years: {total_co2_saved:,} kg (~{total_co2_saved/1000:,} tons)")
+
+        # PDF & Excel
+        report_data = {
+            "Audience": audience,
+            "City": city,
+            "System Type": system_type,
+            "Total Load (W)": total_load,
+            "Daily Energy (kWh)": round(daily_kwh,2),
+            "System Size (kW)": system_kw,
+            "Battery Units": batteries,
+            "Total Cost (PKR)": round(total_cost),
+            "EMI (PKR)": emi,
+            "25-Year NPV (PKR)": npv,
+            "IRR (%)": irr,
+            "Payback Year": payback_year
+        }
+
+        pdf_file = generate_pdf(report_data)
+        excel_file = generate_excel(report_data)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+        with open(pdf_file, "rb") as f:
+            st.download_button("Download PDF Report", f, file_name="Solar_Report_Pakistan.pdf")
+        st.download_button("Download Excel Report", data=excel_file, file_name="Solar_Report_Pakistan.xlsx")