Create app.py

app.py

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+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
+
+# ===============================
+# PAGE CONFIG
+# ===============================
+st.set_page_config(layout="wide")
+st.title("🇵🇰 Pakistan Solar Engineering & Financial Dashboard")
+
+# ===============================
+# 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,
+}
+
+# Appliance Database
+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,
+}
+
+# Demo Load Profiles
+DEMO_SELECTIONS = {
+    "Homeowner": ["LED Bulb (12W)", "Fan (80W)", "Refrigerator (200W)", "LED TV (150W)"],
+    "Solar Company": ["LED Bulb (12W)", "Fan (80W)", "CNC Machine (2kW)", "Industrial AC (5kW)"],
+    "Industrial Investor": ["CNC Machine (2kW)", "Industrial AC (5kW)", "Water Pump 3HP (2.2kW)", "Server Rack (1.5kW)"]
+}
+
+# Tariffs
+RESIDENTIAL_TARIFF = [
+    (100, 22),
+    (100, 32),
+    (100, 38),
+    (100, 42),
+    (100, 48),
+    (np.inf, 65),
+]
+
+COMMERCIAL_TARIFF = 72
+
+# ===============================
+# CALCULATION 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
+    return math.ceil(backup_kwh / 5)
+
+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
+    return round(principal * r * (1 + r)**n / ((1 + r)**n - 1))
+
+def financial_projection(total_cost, daily_kwh, mode, years=25):
+    monthly_units = daily_kwh * 30
+    cashflows = []
+
+    for year in range(1, years + 1):
+        price_increase = (1 + 0.07) ** (year - 1)
+
+        if mode == "Homeowner":
+            monthly_bill = calculate_residential_bill(monthly_units * price_increase)
+        else:
+            monthly_bill = calculate_commercial_bill(monthly_units * price_increase)
+
+        cashflows.append(monthly_bill * 12)
+
+    npv = npf.npv(0.05, [-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)
+
+    return cashflows, round(npv, 2), round(irr * 100, 2), payback_year, np.cumsum(cashflows)
+
+# ===============================
+# PDF REPORT
+# ===============================
+
+def generate_pdf(report_data):
+    file_path = "solar_report.pdf"
+    doc = SimpleDocTemplate(file_path, pagesize=A4)
+    elements = []
+    styles = getSampleStyleSheet()
+
+    elements.append(Paragraph("Pakistan Solar Feasibility Report", styles['Title']))
+    elements.append(Spacer(1, 12))
+
+    for k, v in report_data.items():
+        elements.append(Paragraph(f"{k}: {v}", styles['Normal']))
+        elements.append(Spacer(1, 6))
+
+    doc.build(elements)
+    return file_path
+
+# ===============================
+# STREAMLIT UI
+# ===============================
+
+audience = st.selectbox("Select Audience", ["Homeowner", "Solar Company", "Industrial Investor"])
+city = st.selectbox("Select City", list(CITY_SUNLIGHT.keys()))
+sunlight = CITY_SUNLIGHT[city]
+
+# Demo Settings Buttons
+if st.button("🎯 Load Demo Appliances"):
+    if audience in DEMO_SELECTIONS:
+        st.session_state["demo_appliances"] = DEMO_SELECTIONS[audience]
+        st.success("Demo appliances loaded!")
+
+if "demo_appliances" in st.session_state:
+    default_selection = st.session_state["demo_appliances"]
+else:
+    default_selection = []
+
+# Appliance Selection
+if audience == "Homeowner":
+    appliances = st.multiselect("Select Appliances", list(APPLIANCES_RESIDENTIAL.keys()),
+                                default=default_selection)
+
+elif audience == "Solar Company":
+    appliances = st.multiselect("Select Appliances", 
+                                list(APPLIANCES_RESIDENTIAL.keys()) + list(APPLIANCES_COMMERCIAL.keys()),
+                                default=default_selection)
+
+else:
+    appliances = st.multiselect("Select Industrial Equipment",
+                                list(APPLIANCES_COMMERCIAL.keys()),
+                                default=default_selection)
+
+# Demo Settings Button
+if st.button("🎯 Load Demo City & Settings"):
+    city = "Karachi"
+    sunlight = CITY_SUNLIGHT[city]
+    hours = 8
+    backup_hours = 4
+    st.success("Demo settings loaded!")
+
+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)
+
+# Calculation Trigger
+if st.button("⚡ Calculate Solar System"):
+
+    if not appliances:
+        st.error("Please select appliances")
+    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)
+
+        # Results
+        st.subheader("System Results")
+        st.write(f"Total Load: {total_load} W")
+        st.write(f"Daily Energy: {round(daily_kwh,2)} kWh")
+        st.write(f"System Size: {system_kw} kW")
+        st.write(f"Battery Units: {batteries}")
+        st.write(f"Total Cost: PKR {round(total_cost):,}")
+        st.write(f"EMI: PKR {emi:,}")
+        st.write(f"NPV: PKR {npv:,}")
+        st.write(f"IRR: {irr}%")
+        st.write(f"Payback Year: {payback_year}")
+
+        # Charts
+        st.subheader("Savings Growth")
+        plt.figure(figsize=(10,4))
+        plt.plot(range(1,26), cumulative_savings)
+        plt.axhline(total_cost, linestyle="--")
+        st.pyplot(plt)
+
+        # PDF + Excel
+        report_data = {
+            "Audience": audience,
+            "City": city,
+            "System Size (kW)": system_kw,
+            "Total Cost": total_cost,
+            "IRR": irr,
+            "NPV": npv,
+            "Payback Year": payback_year
+        }
+
+        pdf_file = generate_pdf(report_data)
+
+        with open(pdf_file, "rb") as f:
+            st.download_button("Download PDF Report", f, file_name="Solar_Report.pdf")