Update app.py

app.py

@@ -14,11 +14,27 @@ import time
 huggingfacehub_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
 groq_api_key = os.getenv("GROQ_API_KEY")
 
+# Debugging: Print the API keys to ensure they are being retrieved (remove these prints in production)
+st.write("Hugging Face Hub API Token:", huggingfacehub_api_token)
+st.write("GROQ API Key:", groq_api_key)
+
+# Check if the keys are retrieved correctly
+if not huggingfacehub_api_token:
+    st.error("HUGGINGFACEHUB_API_TOKEN environment variable is not set")
+    st.stop()
+if not groq_api_key:
+    st.error("GROQ_API_KEY environment variable is not set")
+    st.stop()
+
 # Set environment variables for Hugging Face
 os.environ['HUGGINGFACEHUB_API_TOKEN'] = huggingfacehub_api_token
 
 # Initialize the ChatGroq LLM with the retrieved API key
-llm = ChatGroq(api_key=groq_api_key, model_name="Llama3-8b-8192")
+try:
+    llm = ChatGroq(api_key=groq_api_key, model_name="Llama3-8b-8192")
+except Exception as e:
+    st.error(f"Failed to initialize ChatGroq LLM: {e}")
+    st.stop()
 
 st.title("DataScience Chatgroq With Llama3")
 
@@ -41,23 +57,31 @@ def vector_embedding():
         st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)  # Chunk Creation
         st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs[:20])  # Splitting
         st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)  # Vector HuggingFace embeddings
+        st.write("Vector Store DB Is Ready")
+    else:
+        st.write("Vectors already initialized.")
 
 prompt1 = st.text_input("Enter Your Question From Documents")
 
 if st.button("Documents Embedding"):
     vector_embedding()
-    st.write("Vector Store DB Is Ready")
 
 if prompt1:
-    document_chain = create_stuff_documents_chain(llm, prompt)
-    retriever = st.session_state.vectors.as_retriever()
-    retrieval_chain = create_retrieval_chain(retriever, document_chain)
-    start = time.process_time()
-    response = retrieval_chain.invoke({'input': prompt1})
-    st.write("Response time: ", time.process_time() - start)
-    st.write(response['answer'])
-
-    with st.expander("Document Similarity Search"):
-        for i, doc in enumerate(response["context"]):
-            st.write(doc.page_content)
-            st.write("--------------------------------")
+    if "vectors" not in st.session_state:
+        st.error("Vectors are not initialized. Please click 'Documents Embedding' first.")
+    else:
+        document_chain = create_stuff_documents_chain(llm, prompt)
+        retriever = st.session_state.vectors.as_retriever()
+        retrieval_chain = create_retrieval_chain(retriever, document_chain)
+        try:
+            start = time.process_time()
+            response = retrieval_chain.invoke({'input': prompt1})
+            st.write("Response time: ", time.process_time() - start)
+            st.write(response['answer'])
+
+            with st.expander("Document Similarity Search"):
+                for i, doc in enumerate(response["context"]):
+                    st.write(doc.page_content)
+                    st.write("--------------------------------")
+        except Exception as e:
+            st.error(f"Failed to retrieve the answer: {e}")