Update app.py

app.py

@@ -9,9 +9,11 @@ from groq import Groq
 # Initialize Groq client
 client = Groq(api_key=os.environ.get('GroqApi'))
 
-# Initialize session state for scraped data
+# Initialize session state
 if "scraped_data" not in st.session_state:
     st.session_state.scraped_data = []
+if "appliance_data" not in st.session_state:
+    st.session_state.appliance_data = []
 
 def scrape_web_data(url):
     """Scrape text data from the given URL."""
@@ -70,9 +72,6 @@ if st.button("Scrape Data"):
 
 # Step 2: Appliance Inputs
 st.subheader("Step 2: Enter Appliance Details")
-if "appliance_data" not in st.session_state:
-    st.session_state.appliance_data = []
-
 appliance_name = st.text_input("Appliance Name")
 appliance_load = st.number_input("Load (Watts per Appliance)", min_value=1, value=100)
 appliance_quantity = st.number_input("Quantity", min_value=1, value=1)
@@ -87,7 +86,6 @@ if st.button("Add Appliance"):
     })
     st.success(f"Added {appliance_quantity} {appliance_name}(s) to the list!")
 
-# Display Appliance List
 if st.session_state.appliance_data:
     st.subheader("Appliance List")
     for idx, appliance in enumerate(st.session_state.appliance_data):
@@ -101,12 +99,10 @@ if st.button("Ask Query"):
     if not st.session_state.scraped_data:
         st.warning("Please scrape tariff data first.")
     else:
-        # Load vectorstore
         embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-        vectorstore = FAISS.load_local("vectorstore/", embeddings)
+        vectorstore = FAISS.load_local("vectorstore/", embeddings, allow_dangerous_deserialization=True)
         retriever = vectorstore.as_retriever()
 
-        # Combine scraped data for context
         context = "\n".join([item["Data"] for item in st.session_state.scraped_data])
         prompt = f"Context: {context}\n\nUser Query: {user_query}\nAnswer:"
         response = query_with_groq(prompt)