Update app.py

app.py

@@ -1,6 +1,4 @@
 import os
-import json
-
 import streamlit as st
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_chroma import Chroma
@@ -8,25 +6,33 @@ from langchain_groq import ChatGroq
 from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 
-from vectorize_documents import embeddings
-
-working_dir = os.path.dirname(os.path.abspath(__file__))
-config_data = json.load(open(f"{working_dir}/config.json"))
-GROQ_API_KEY = config_data["GROQ_API_KEY"]
-os.environ["GROQ_API_KEY"] = GROQ_API_KEY
-
+# Ensure required environment variables are set
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+if not GROQ_API_KEY:
+    st.error("GROQ_API_KEY is not set. Please configure it in Hugging Face Spaces secrets.")
+    st.stop()
 
+# Function to set up the vectorstore
 def setup_vectorstore():
+    working_dir = os.path.dirname(os.path.abspath(__file__))
     persist_directory = f"{working_dir}/vector_db_dir"
-    embedddings = HuggingFaceEmbeddings()
-    vectorstore = Chroma(persist_directory=persist_directory,
-                         embedding_function=embeddings)
-    return vectorstore
 
+    # Initialize HuggingFace Embeddings
+    embeddings = HuggingFaceEmbeddings()
+
+    # Initialize Chroma vectorstore
+    vectorstore = Chroma(
+        persist_directory=persist_directory,
+        embedding_function=embeddings
+    )
+    return vectorstore
 
+# Function to set up the chat chain
 def chat_chain(vectorstore):
-    llm = ChatGroq(model="llama-3.1-70b-versatile",
-                   temperature=0)
+    llm = ChatGroq(model="llama-3.1-70b-versatile", 
+                   temperature=0, 
+                   groq_api_key=GROQ_API_KEY)
+    
     retriever = vectorstore.as_retriever()
     memory = ConversationBufferMemory(
         llm=llm,
@@ -34,6 +40,7 @@ def chat_chain(vectorstore):
         memory_key="chat_history",
         return_messages=True
     )
+
     chain = ConversationalRetrievalChain.from_llm(
         llm=llm,
         retriever=retriever,
@@ -42,32 +49,33 @@ def chat_chain(vectorstore):
         verbose=True,
         return_source_documents=True
     )
-
     return chain
 
-
+# Streamlit UI configuration
 st.set_page_config(
     page_title="Multi Doc Chat",
-    page_icon = "📚",
+    page_icon="📚",
     layout="centered"
 )
 
 st.title("📚 Multi Documents Chatbot")
 
+# Initialize session state variables
 if "chat_history" not in st.session_state:
     st.session_state.chat_history = []
 
 if "vectorstore" not in st.session_state:
     st.session_state.vectorstore = setup_vectorstore()
 
-if "conversationsal_chain" not in st.session_state:
-    st.session_state.conversationsal_chain = chat_chain(st.session_state.vectorstore)
-
+if "conversational_chain" not in st.session_state:
+    st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore)
 
+# Display chat history
 for message in st.session_state.chat_history:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
 
+# User input
 user_input = st.chat_input("Ask AI...")
 
 if user_input:
@@ -76,9 +84,10 @@ if user_input:
     with st.chat_message("user"):
         st.markdown(user_input)
 
-
     with st.chat_message("assistant"):
-        response = st.session_state.conversationsal_chain({"question": user_input})
+        # Generate response
+        response = st.session_state.conversational_chain({"question": user_input})
         assistant_response = response["answer"]
+        
         st.markdown(assistant_response)
         st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})