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
+
+# ===============================
+# PAGE CONFIG
+# ===============================
+st.set_page_config(layout="wide")
+st.title("🇵🇰 AI Solar Intelligence Platform (Ultra Premium)")
+
+# ===============================
+# ADVANCED CONFIG
+# ===============================
+
+SYSTEM_LOSSES = 0.20
+DEMAND_FACTOR_INDUSTRIAL = 0.75
+
+CITY_IRRADIANCE_INDEX = {
+    "Karachi": 6.2,
+    "Lahore": 5.5,
+    "Islamabad": 5.2,
+    "Peshawar": 5.6,
+    "Quetta": 6.5,
+}
+
+# Pricing
+PANEL_COST_PER_WATT = 55
+INSTALLATION_COST_PER_WATT = 35
+BATTERY_COST_5KWH = 95000
+
+# Appliance Database (Expanded)
+LOAD_LIBRARY = {
+    "Home": {
+        "Lighting": 60,
+        "Fans": 400,
+        "Fridge": 200,
+        "AC": 1500
+    },
+    "Industrial": {
+        "Motors": 5000,
+        "Compressors": 8000,
+        "CNC Machines": 2000
+    }
+}
+
+# ===============================
+# AI RECOMMENDATION ENGINE
+# ===============================
+
+def ai_solar_recommendation(load_kw, sunlight):
+    optimal_system = (load_kw / sunlight) * 1.2
+    return round(optimal_system, 2)
+
+def financial_projection(cost, daily_kwh):
+    cashflows = []
+    for year in range(1, 26):
+        price_escalation = (1 + 0.07) ** year
+        savings = daily_kwh * 30 * 55 * price_escalation
+        cashflows.append(savings * 12)
+
+    npv = npf.npv(0.05, [-cost] + cashflows)
+    irr = npf.irr([-cost] + cashflows)
+
+    payback = next((i for i, cf in enumerate(np.cumsum(cashflows), 1)
+                    if cf >= cost), None)
+
+    return cashflows, npv, irr, payback, np.cumsum(cashflows)
+
+# ===============================
+# USER INPUT LAYER
+# ===============================
+
+user_type = st.selectbox(
+    "Select Client Type",
+    ["Homeowner", "Commercial Business", "Industrial Investor"]
+)
+
+city = st.selectbox("Select City", list(CITY_IRRADIANCE_INDEX.keys()))
+sunlight = CITY_IRRADIANCE_INDEX[city]
+
+# Load Selection
+if user_type == "Homeowner":
+    load_choice = st.multiselect(
+        "Select Home Loads",
+        ["Lighting", "Fans", "Fridge", "AC"]
+    )
+elif user_type == "Commercial Business":
+    load_choice = st.multiselect(
+        "Select Business Loads",
+        ["Lighting", "Computers", "AC", "Machinery"]
+    )
+else:
+    load_choice = st.multiselect(
+        "Select Industrial Loads",
+        ["Motors", "Compressors", "CNC Machines"]
+    )
+
+hours = st.slider("Daily Usage Hours", 1, 24, 8)
+
+# ===============================
+# CALCULATIONS
+# ===============================
+
+if st.button("🚀 Run AI Solar Analysis"):
+
+    if len(load_choice) == 0:
+        st.error("Please select at least one load")
+    else:
+
+        # Load estimation
+        load_watts = 0
+
+        for item in load_choice:
+            if item in ["Lighting", "Fans"]:
+                load_watts += 200
+            elif item == "Fridge":
+                load_watts += 200
+            elif item == "AC":
+                load_watts += 1500
+            elif item in ["Motors", "Compressors", "CNC Machines"]:
+                load_watts += 3000
+
+        daily_kwh = (load_watts * hours) / 1000
+        daily_kwh = daily_kwh / (1 - SYSTEM_LOSSES)
+
+        # AI Recommendation
+        system_kw = ai_solar_recommendation(daily_kwh, sunlight)
+
+        system_cost = system_kw * 1000 * (PANEL_COST_PER_WATT + INSTALLATION_COST_PER_WATT)
+
+        # Financials
+        cashflows, npv, irr, payback, cumulative = financial_projection(system_cost, daily_kwh)
+
+        # Results Display
+        st.subheader("📊 AI Analysis Results")
+
+        st.write(f"Recommended Solar System Size: {system_kw} kW")
+        st.write(f"Daily Energy Consumption: {round(daily_kwh,2)} kWh")
+        st.write(f"Estimated Installation Cost: PKR {round(system_cost):,}")
+        st.write(f"NPV (25 Years): PKR {round(npv,2):,}")
+        st.write(f"IRR: {round(irr*100,2)} %")
+        st.write(f"Payback Period: {payback} Years")
+
+        # Charts
+        st.subheader("📈 Investment Growth Projection")
+
+        plt.figure(figsize=(10,4))
+        plt.plot(range(1,26), cumulative)
+        plt.axhline(system_cost, linestyle="--")
+        st.pyplot(plt)
+
+        # WhatsApp Style Summary
+        st.subheader("📱 Proposal Summary")
+
+        summary_text = f"""
+        Solar Proposal Summary
+
+        City: {city}
+        Recommended System: {system_kw} kW
+        Cost Estimate: PKR {round(system_cost):,}
+        Payback Period: {payback} Years
+        IRR: {round(irr*100,2)}%
+        """
+
+        st.text_area("Copy for WhatsApp / Proposal", summary_text, height=200)