Create app.py

app.py

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+import os
+import streamlit as st
+from PyPDF2 import PdfReader
+from sentence_transformers import SentenceTransformer
+import faiss
+from groq import Groq
+import json
+
+# Set up Groq client
+client = Groq(api_key=os.environ.get('GroqApi'))
+
+# Load the model
+model = SentenceTransformer('all-MiniLM-L6-v2')  # Use an open-source embedding model
+
+# Initialize FAISS
+dimension = 384  # Embedding dimension for 'all-MiniLM-L6-v2'
+index = faiss.IndexFlatL2(dimension)
+
+# Streamlit app
+st.title("Electricity Bill Calculation App")
+
+# File upload
+uploaded_file = st.file_uploader("Upload your electricity bill PDF", type="pdf")
+
+if uploaded_file:
+    # Extract text from PDF
+    reader = PdfReader(uploaded_file)
+    text = " ".join([page.extract_text() for page in reader.pages])
+
+    # Tokenize and chunk text
+    sentences = text.split(". ")  # Simple sentence splitting
+    embeddings = model.encode(sentences)
+
+    # Store embeddings in FAISS
+    faiss.normalize_L2(embeddings)
+    index.add(embeddings)
+
+    # Extract company and user type
+    company = None
+    if "LESCO" in text.upper():
+        company = "LESCO"
+    elif "FESCO" in text.upper():
+        company = "FESCO"
+
+    user_type = None
+    if "PROTECTED" in text.upper():
+        user_type = "Protected"
+    elif "UNPROTECTED" in text.upper():
+        user_type = "Unprotected"
+
+    st.write(f"Detected Company: {company}")
+    st.write(f"Detected User Type: {user_type}")
+
+    if company and user_type:
+        # Appliance usage input
+        st.subheader("Appliance Usage Details")
+        num_appliances = st.number_input("Number of appliances", min_value=1, max_value=20, step=1)
+        
+        appliance_data = []
+        for i in range(num_appliances):
+            st.write(f"Appliance {i + 1}")
+            name = st.text_input(f"Name of Appliance {i + 1}", key=f"appliance_{i}")
+            power = st.number_input(f"Power (Watts) of {name}", key=f"power_{i}")
+            hours = st.number_input(f"Usage hours per day for {name}", key=f"hours_{i}")
+            if name and power and hours:
+                appliance_data.append({"name": name, "power": power, "hours": hours})
+
+        if st.button("Calculate Bill"):
+            # Calculate total units
+            total_units = sum([(appliance["power"] * appliance["hours"] * 30) / 1000 for appliance in appliance_data])
+            st.write(f"Total Units Consumed: {total_units:.2f} kWh")
+
+            # Get tariff rate from Groq
+            query_content = {
+                "company": company,
+                "user_type": user_type,
+                "units": total_units,
+            }
+            chat_completion = client.chat.completions.create(
+                messages=[{"role": "user", "content": json.dumps(query_content)}],
+                model="llama3-8b-8192",
+            )
+            tariff_rate = float(chat_completion.choices[0].message.content.strip())
+
+            # Calculate and display bill
+            total_bill = tariff_rate * total_units
+            st.write(f"Tariff Rate: {tariff_rate} PKR/kWh")
+            st.write(f"Total Bill: {total_bill:.2f} PKR")
+
+# Run Streamlit using: streamlit run app.py