Update app.py

app.py

@@ -10,48 +10,46 @@ from langchain.chains import ConversationalRetrievalChain
 from htmlTemplates import css, bot_template, user_template
 from langchain.llms import HuggingFaceHub, LlamaCpp
 from huggingface_hub import snapshot_download, hf_hub_download
-#from prompts import CONDENSE_QUESTION_PROMPT
+
+# from prompts import CONDENSE_QUESTION_PROMPT
 
 repo_name = "IlyaGusev/saiga2_13b_gguf"
 model_name = "model-q4_K.gguf"
-    
+
 snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
 
+
 def get_pdf_text(pdf_docs):
-    
     text = ""
     for pdf in pdf_docs:
         pdf_reader = PdfReader(pdf)
         for page in pdf_reader.pages:
             text += page.extract_text()
-            
+
     return text
 
 
 def get_text_chunks(text):
-    
     text_splitter = CharacterTextSplitter(separator="\n",
-                                          chunk_size=500, #1000
-                                          chunk_overlap=30, #200
+                                          chunk_size=500,  # 1000
+                                          chunk_overlap=30,  # 200
                                           length_function=len
-                                         )
+                                          )
     chunks = text_splitter.split_text(text)
-    
+
     return chunks
 
 
 def get_vectorstore(text_chunks):
-    
-    #embeddings = OpenAIEmbeddings()
+    # embeddings = OpenAIEmbeddings()
     embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-    #embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
+    # embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2")
     vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    
+
     return vectorstore
 
 
 def get_conversation_chain(vectorstore, model_name):
-
     llm = LlamaCpp(model_path=model_name,
                    temperature=0.1,
                    top_k=30,
@@ -60,26 +58,27 @@ def get_conversation_chain(vectorstore, model_name):
                    n_ctx=2048,
                    n_parts=1,
                    echo=True
-                  )
-    
-    #llm = ChatOpenAI()
+                   )
 
+    # llm = ChatOpenAI()
+    
     memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
     
     conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm,
-                                                               #condense_question_prompt=CONDENSE_QUESTION_PROMPT,
+                                                               # condense_question_prompt=CONDENSE_QUESTION_PROMPT,
                                                                retriever=vectorstore.as_retriever(),
                                                                memory=memory,
                                                                return_source_documents=True
-                                                              )
+                                                               )
     
-    return conversation_chain, conversation_chain['source_documents'][0]
+    result = conversation_chain
+
+    return result, result['source_documents'][0]
 
 
 def handle_userinput(user_question):
-    
     response = st.session_state.conversation({'question': user_question})
-    
+
     st.session_state.chat_history = response['chat_history']
 
     for i, message in enumerate(st.session_state.chat_history):
@@ -90,6 +89,7 @@ def handle_userinput(user_question):
             st.write(bot_template.replace(
                 "{{MSG}}", message.content), unsafe_allow_html=True)
 
+
 # main code
 load_dotenv()
 
@@ -126,3 +126,4 @@ with st.sidebar:
             # create conversation chain
             st.session_state.conversation, retrieved_docs = get_conversation_chain(vectorstore, model_name)
             st.text_area(retrieved_docs)
+