Update app.py

app.py

@@ -14,27 +14,11 @@ 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
-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()
+llm = ChatGroq(api_key=groq_api_key, model_name="Llama3-8b-8192")
 
 st.title("DataScience Chatgroq With Llama3")
 
@@ -68,15 +52,12 @@ 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)
-    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}")
+    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("--------------------------------")