Update app.py

app.py

@@ -6,14 +6,15 @@ from scraper import scrape_tariffs
 from transformers import AutoTokenizer, AutoModel
 import torch
 
-# Load pre-trained transformer model for embeddings directly
+# Load pre-trained transformer model for query embeddings
 tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
 model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
 
-# Initialize Streamlit components
-st.title("Electricity Bill Estimator")
-st.sidebar.header("User Input")
+# Streamlit App: Electricity Bill Estimator
+st.title("🔌 Electricity Bill Estimator")
+st.sidebar.header("⚙️ User Input")
 
+# Tariff URLs for scraping
 tariff_urls = {
     "IESCO": "https://iesco.com.pk/index.php/customer-services/tariff-guide",
     "FESCO": "https://fesco.com.pk/tariff",
@@ -22,40 +23,58 @@ tariff_urls = {
     "LESCO": "https://www.lesco.gov.pk/ElectricityTariffs",
     "PESCO": "https://pesconlinebill.pk/pesco-tariff/",
     "QESCO": "http://qesco.com.pk/Tariffs.aspx",
-    "TESCO": "https://tesco.gov.pk/index.php/electricity-traiff"
+    "TESCO": "https://tesco.gov.pk/index.php/electricity-traiff",
 }
 
 def show_tariff_input():
-    # Display tariff rates selection
-    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}")
+    """
+    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.")
 
 def scrape_data():
-    # Scraping tariff data using provided URLs
+    """
+    Scrapes tariff data from the provided URLs.
+    """
+    st.info("🔄 Scraping tariff data... Please wait.")
     scrape_tariffs(list(tariff_urls.values()))
+    st.success("✅ Tariff data scraping complete.")
 
-# Streamlit actions
-if st.sidebar.button("Scrape Data"):
+# Sidebar: Scrape Tariff Data
+if st.sidebar.button("Scrape Tariff Data"):
     scrape_data()
 
-# User inputs for appliance and usage time (replace placeholders as needed)
-appliance_load = st.number_input("Enter appliance load in watts", min_value=10, max_value=5000, value=1000)
-usage_time = st.number_input("Enter usage time (in hours)", min_value=1, max_value=24, value=5)
+# 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
+)
+usage_time = st.number_input(
+    "Enter daily usage time (in hours):", 
+    min_value=1, max_value=24, value=5
+)
 
-# Placeholder for electricity bill calculation and output display
+# Electricity Bill Calculation
 if appliance_load and usage_time:
-    bill_amount = appliance_load * usage_time * 0.25  # Add your own calculation based on tariffs
-    st.write(f"Your electricity bill: {bill_amount} PKR")
+    bill_amount = appliance_load * usage_time * 0.25  # Simplified calculation
+    st.write(f"💵 Estimated Electricity Bill: **{bill_amount:.2f} PKR**")
 
-# Example of using Hugging Face's transformers directly to encode queries
-user_query = st.text_input("Ask about your tariff or appliance:")
+# Query Input for Tariff Details
+st.subheader("🧠 Query About Tariff or Appliance")
+user_query = st.text_input("Enter your query:")
 if user_query:
-    # Tokenizing the query and calculating embeddings
+    # Generate embeddings for the user query
     inputs = tokenizer(user_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()
-
-    st.write(f"Query embedding (for further processing): {embeddings}")
+    
+    st.write(f"🔍 Query Embedding (for further processing):")
+    st.code(embeddings)