Update app.py

app.py

@@ -14,11 +14,23 @@ import time
 huggingfacehub_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
 groq_api_key = os.getenv("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")
 
@@ -52,12 +64,15 @@ 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("--------------------------------")
+    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}")