Update app.py

app.py

@@ -53,6 +53,10 @@ PINECONE_ENVIRONMENT=os.getenv("PINECONE_ENVIRONMENT")
 
 # pc = PineconeGRPC( api_key=os.environ.get("PINECONE_API_KEY") ) # Now do stuff if 'my_index' not in pc.list_indexes().names(): pc.create_index( name='my_index', dimension=1536, metric='euclidean', spec=ServerlessSpec( cloud='aws', region='us-west-2' ) )
 
+# Load environment variables from .env file
+load_dotenv()
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
 def connect_pinecone():
     pinecone = PineconeGRPC(api_key=PINECONE_API_KEY, environment=PINECONE_ENV)
     # st.code(pinecone)
@@ -81,7 +85,7 @@ def get_pinecone_semantic_index(pinecone):
 
 
 
-def prompt_engineer(text, query):
+def prompt_engineer(text, longtext, query):
     summary_prompt_template = """
     write a concise summary of the following text delimited by triple backquotes.
     return your response in bullet points which convers the key points of the text.
@@ -121,7 +125,7 @@ def prompt_engineer(text, query):
     """
 
     prompt_template = ChatPromptTemplate.from_template(GENERATION_PROMPT_TEMPLATE)
-    prompt = prompt_template.format(context=text, question=query)
+    prompt = prompt_template.format(context=longtext, question=query)
     response_text = ""
     result = ""
     
@@ -132,7 +136,8 @@ def prompt_engineer(text, query):
         response_text = llm.invoke(prompt)
         escaped_query = re.escape(query)
         result = re.split(f'Answer the question based on the above context: {escaped_query}\n',response_text)[-1]
-        st.write(result)
+        st.write("reponse generated see chat window 👉🏻")
+        st.divider()
     except Exception as e:
         st.error(f"Error invoke: {e}")
 
@@ -173,7 +178,7 @@ def chat_actions():
         p = math.pow(1024, 2)
         mbsize = round(len(bytesize) / p, 2)
         st.write(f"Text length of {len(consolidated_text)} characters with {mbsize}MB size")
-        summary, response = prompt_engineer(consolidated_text[:1024], query)
+        summary, response = prompt_engineer(consolidated_text[:1024], consolidated_text, query)
 
     for res in result['matches']:
         st.session_state["chat_history"].append(