Update app.py

app.py

@@ -11,7 +11,7 @@ tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v
 model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
 
 # Streamlit App: Electricity Bill Estimator
-st.title("🔌 Electricity Bill Estimator")
+st.title("🔌 Electricity Bill & Carbon Footprint Estimator")
 st.sidebar.header("⚙️ User Input")
 
 # Tariff URLs for scraping
@@ -26,17 +26,19 @@ tariff_urls = {
     "TESCO": "https://tesco.gov.pk/index.php/electricity-traiff",
 }
 
-def show_tariff_input():
-    """
-    Displays a dropdown menu for tariff categories loaded from a CSV file.
-    """
-    try:
-        tariff_data = pd.read_csv("data/tariffs.csv")
-        tariff_types = tariff_data["category"].unique()
-        tariff_choice = st.selectbox("Select your tariff category:", tariff_types)
-        st.write(f"✅ Selected Tariff: **{tariff_choice}**")
-    except FileNotFoundError:
-        st.error("⚠️ Tariff data not found. Please ensure 'data/tariffs.csv' exists.")
+# Predefined appliances and their power in watts
+appliances = {
+    "LED Bulb (10W)": 10,
+    "Ceiling Fan (75W)": 75,
+    "Refrigerator (150W)": 150,
+    "Air Conditioner (1.5 Ton, 1500W)": 1500,
+    "Washing Machine (500W)": 500,
+    "Television (100W)": 100,
+    "Laptop (65W)": 65,
+    "Iron (1000W)": 1000,
+    "Microwave Oven (1200W)": 1200,
+    "Water Heater (2000W)": 2000,
+}
 
 def scrape_data():
     """
@@ -57,36 +59,53 @@ def calculate_carbon_footprint(monthly_energy_kwh):
 if st.sidebar.button("Scrape Tariff Data"):
     scrape_data()
 
-# User Inputs for Electricity Usage
-st.subheader("💡 Electricity Usage Input")
-appliance_load = st.number_input(
-    "Enter appliance load in watts:", 
-    min_value=10, max_value=5000, value=1000
+# Sidebar: User Inputs for Appliances
+st.sidebar.subheader("🏠 Add Appliances")
+selected_appliance = st.sidebar.selectbox("Select an appliance:", list(appliances.keys()))
+appliance_power = appliances[selected_appliance]
+appliance_quantity = st.sidebar.number_input(
+    "Enter quantity:", min_value=1, max_value=10, value=1
 )
-usage_time = st.number_input(
-    "Enter daily usage time (in hours):", 
-    min_value=1, max_value=24, value=5
+usage_hours = st.sidebar.number_input(
+    "Enter usage hours per day:", min_value=1, max_value=24, value=5
 )
 
+# Add appliance details to the main list
+if "appliance_list" not in st.session_state:
+    st.session_state["appliance_list"] = []
+
+if st.sidebar.button("Add Appliance"):
+    st.session_state["appliance_list"].append(
+        {
+            "appliance": selected_appliance,
+            "power": appliance_power,
+            "quantity": appliance_quantity,
+            "hours": usage_hours,
+        }
+    )
+
+# Display the list of added appliances
+st.subheader("📋 Added Appliances")
+if st.session_state["appliance_list"]:
+    for idx, appliance in enumerate(st.session_state["appliance_list"], start=1):
+        st.write(
+            f"{idx}. **{appliance['appliance']}** - "
+            f"{appliance['power']}W, {appliance['quantity']} unit(s), "
+            f"{appliance['hours']} hours/day"
+        )
+
 # Electricity Bill and Carbon Footprint Calculation
-if appliance_load and usage_time:
-    daily_energy_kwh = (appliance_load * usage_time) / 1000  # Convert to kWh
-    monthly_energy_kwh = daily_energy_kwh * 30  # Assume 30 days in a month
+if st.session_state["appliance_list"]:
+    total_daily_energy_kwh = sum(
+        (appliance["power"] * appliance["quantity"] * appliance["hours"]) / 1000
+        for appliance in st.session_state["appliance_list"]
+    )
+    monthly_energy_kwh = total_daily_energy_kwh * 30  # Assume 30 days in a month
     bill_amount = monthly_energy_kwh * 0.25  # Simplified bill calculation
     carbon_footprint = calculate_carbon_footprint(monthly_energy_kwh)
 
+    st.subheader("💵 Electricity Bill & 🌍 Carbon Footprint")
     st.write(f"💵 **Estimated Electricity Bill**: **{bill_amount:.2f} PKR**")
     st.write(f"🌍 **Estimated Carbon Footprint**: **{carbon_footprint:.2f} kg CO2 per month**")
-
-# Query processing logic remains in the backend, if needed
-def process_query(query):
-    """
-    Generates embeddings for the given query using the transformer model.
-    """
-    inputs = tokenizer(query, return_tensors="pt", padding=True, truncation=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        embeddings = outputs.last_hidden_state.mean(dim=1).squeeze().numpy()
-    return embeddings
-
-# Note: No user-facing query box displayed on the front-end
+else:
+    st.info("ℹ️ Add appliances to calculate the electricity bill and carbon footprint.")