Create app.py

app.py

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+import pandas as pd
+import streamlit as st
+import pickle
+from PIL import Image
+
+# Set page configuration
+st.set_page_config(
+    page_title="Electric Bill Predictor",
+    page_icon="⚡",
+    layout="centered",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""
+    <style>
+        .title {
+            color: #2c3e50;
+            text-align: center;
+            margin-bottom: 30px;
+        }
+        .input-section {
+            background-color: #f8f9fa;
+            padding: 20px;
+            border-radius: 10px;
+            margin-bottom: 20px;
+            border-left: 5px solid #27ae60;
+        }
+        .result-box {
+            background-color: #e8f5e9;
+            padding: 25px;
+            border-radius: 10px;
+            margin-top: 20px;
+            text-align: center;
+            font-size: 1.5em;
+            font-weight: bold;
+            border: 2px solid #27ae60;
+        }
+        .stButton>button {
+            background-color: #27ae60;
+            color: white;
+            border-radius: 8px;
+            padding: 12px 24px;
+            width: 100%;
+            transition: all 0.3s;
+            font-size: 1.1em;
+        }
+        .stButton>button:hover {
+            background-color: #2ecc71;
+            transform: scale(1.02);
+        }
+        .stSelectbox, .stNumberInput {
+            margin-bottom: 15px;
+        }
+        .footer {
+            text-align: center;
+            margin-top: 30px;
+            color: #777;
+            font-size: 0.9em;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Load model with caching and error handling
+@st.cache_resource
+def load_model():
+    try:
+        with open("final_model_.pkl", "rb") as f:
+            model = pickle.load(f)
+        st.success("✅ Model loaded successfully!")
+        return model
+    except FileNotFoundError:
+        st.error("❌ Model file not found! Please ensure 'final_model_.pkl' is in the correct directory.")
+        return None
+    except Exception as e:
+        st.error(f"❌ Error loading model: {str(e)}")
+        return None
+
+model = load_model()
+
+# App title and header
+st.markdown("<h1 class='title'>⚡ Smart Electric Bill Predictor</h1>", unsafe_allow_html=True)
+st.markdown("Predict your monthly electricity bill based on appliance usage patterns and location.")
+
+# Main input section
+with st.expander("🏠 **Property & Usage Details**", expanded=True):
+    st.markdown("<div class='input-section'>", unsafe_allow_html=True)
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.subheader("Appliance Usage (Hours)")
+        fan = st.slider("Fan Hours per Day", 5.0, 23.0, 13.0, 0.5,
+                       help="Daily usage hours of ceiling/table fans")
+        fridge = st.slider("Refrigerator Hours", 17.0, 23.0, 18.0, 0.5,
+                          help="Refrigerator running hours (typically 18-24 hours)")
+        ac = st.slider("Air Conditioner Hours", 0.0, 3.0, 1.0, 0.5,
+                      help="Daily AC usage hours (0 if not used)")
+        tv = st.slider("Television Hours", 3.0, 22.0, 12.0, 0.5,
+                      help="Daily TV viewing hours")
+        monitor = st.slider("Computer Monitor Hours", 1.0, 12.0, 8.0, 0.5,
+                           help="Daily computer usage hours")
+        
+    with col2:
+        st.subheader("Location & Billing")
+        city = st.selectbox("City", 
+                          ['Hyderabad', 'Vadodara', 'Shimla', 'Mumbai', 'Ratnagiri',
+                           'New Delhi', 'Dahej', 'Ahmedabad', 'Noida', 'Nagpur', 'Chennai',
+                           'Faridabad', 'Kolkata', 'Pune', 'Gurgaon', 'Navi Mumbai'],
+                          help="Select your city for regional tariff rates")
+        
+        company = st.selectbox("Electricity Provider", 
+                              ['Tata Power Company Ltd.', 'NHPC', 'Jyoti Structure',
+                               'Power Grid Corp', 'Ratnagiri Gas and Power Pvt. Ltd. (RGPPL)',
+                               'Adani Power Ltd.', 'Kalpataru Power', 'Orient Green',
+                               'Sterlite Power Transmission Ltd',
+                               'Neueon Towers / Sujana Towers Ltd.', 'KEC International',
+                               'Indowind Energy', 'Unitech Power Transmission Ltd.',
+                               'Bonfiglioli Transmission Pvt. Ltd.', 'SJVN Ltd.',
+                               'Maha Transco – Maharashtra State Electricity Transmission Co, Ltd.',
+                               'L&T Transmission & Distribution', 'Guj Ind Power',
+                               'Torrent Power Ltd.', 'Reliance Energy', 'GE T&D India Limited',
+                               'NTPC Pvt. Ltd.',
+                               'Optibelt Power Transmission India Private Limited', 'CESC',
+                               'Ringfeder Power Transmission India Pvt. Ltd.', 'Reliance Power',
+                               'JSW Energy Ltd.', 'Sunil Hitech Eng',
+                               'Toshiba Transmission & Distribution Systems (India) Pvt. Ltd.',
+                               'Jaiprakash Power', 'TransRail Lighting', 'NLC India'],
+                              help="Select your electricity provider company")
+        
+        month = st.selectbox("Month", 
+                            ["January", "February", "March", "April", "May", "June",
+                             "July", "August", "September", "October", "November", "December"],
+                            index=7,  # Default to August
+                            help="Select month for seasonal variation")
+        
+        monthly_hours = st.slider("Total Monthly Usage Hours", 95.0, 926.0, 826.0, 10.0,
+                                help="Sum of all appliance usage hours for the month")
+        
+        tariff_rate = st.slider("Electricity Tariff Rate (₹/kWh)", 7.4, 9.3, 8.2, 0.1,
+                              help="Current electricity rate per unit")
+    
+    st.markdown("</div>", unsafe_allow_html=True)
+
+# Prediction button and results
+if st.button("🔍 Predict Monthly Bill", use_container_width=True):
+    if model is None:
+        st.error("Cannot make prediction - model not loaded.")
+    else:
+        try:
+            # Convert month name to number
+            month_num = ["January", "February", "March", "April", "May", "June",
+                        "July", "August", "September", "October", "November", "December"].index(month) + 1
+            
+            input_data = pd.DataFrame([[fan, fridge, ac, tv, monitor, month_num, 
+                                      city, company, monthly_hours, tariff_rate]],
+                                    columns=['Fan', 'Refrigerator', 'AirConditioner', 
+                                             'Television', 'Monitor', 'Month', 
+                                             'City', 'Company', 'MonthlyHours', 
+                                             'TariffRate'])
+            
+            predicted_price = model.predict(input_data)[0]
+            
+            # Display result with visual impact
+            st.markdown(f"""
+                <div class='result-box'>
+                    <div style='font-size: 1.2em; margin-bottom: 10px;'>Estimated Monthly Bill</div>
+                    <div style='font-size: 2em; color: #27ae60;'>₹ {predicted_price:,.2f} INR</div>
+                    <div style='margin-top: 15px; font-size: 0.8em; color: #555;'>
+                        Based on your usage patterns in {city} ({month})
+                    </div>
+                </div>
+            """, unsafe_allow_html=True)
+            
+            # Add energy saving tips based on prediction
+            if predicted_price > 5000:
+                st.warning("💡 High Bill Alert: Consider reducing AC usage and switching to energy-efficient appliances.")
+            elif predicted_price > 3000:
+                st.info("💡 Moderate Bill: You might save by using fans instead of AC when possible.")
+            else:
+                st.success("💡 Efficient Usage: Your electricity consumption is well managed!")
+            
+        except Exception as e:
+            st.error(f"❌ Prediction error: {str(e)}")
+
+# Additional information section
+with st.expander("ℹ️ About This Prediction"):
+    st.markdown("""
+    **How this prediction works:**
+    - The model analyzes your appliance usage patterns, location, and local electricity rates
+    - Calculations consider seasonal variations in energy consumption
+    - Predictions are based on machine learning models trained on historical billing data
+    
+    **For more accurate results:**
+    - Provide exact usage hours from your electricity meter if available
+    - Update tariff rates according to your latest electricity bill
+    - Consider seasonal adjustments for AC/heating usage
+    """)
+
+# Footer
+st.markdown("""
+    <div class='footer'>
+        Energy Conservation Starts With Awareness • Powered by Machine Learning
+    </div>
+""", unsafe_allow_html=True)