Update app.py

app.py

@@ -26,73 +26,73 @@ def get_pdf_text(pdf_docs):
 
 #  Chunk size and overlap must not exceed the models capacity!
 #
-def get_text_chunks(text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=800,    #  1000
-        chunk_overlap=200,
-        length_function=len
-    )
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-def get_vectorstore(text_chunks):
-    # embeddings = OpenAIEmbeddings()
-
-    #  pip install InstructorEmbedding
-    #  pip install sentence-transformers==2.2.2
-    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-
-    #  from InstructorEmbedding import INSTRUCTOR
-    # model = INSTRUCTOR('hkunlp/instructor-xl')
-    # sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
-    # instruction = "Represent the Science title:"
-    # embeddings = model.encode([[instruction, sentence]])
-
-    # embeddings = model.encode(text_chunks)
-    print('have Embeddings:   ')
-
-    # text_chunks="this is a test"
-    #   FAISS,  Chroma and other vector databases
-    #
-    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    print('FAISS succeeds:   ')
-
-    return vectorstore
-
-def get_conversation_chain(vectorstore):
-    # llm = ChatOpenAI()
-    #  llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
-    #  google/bigbird-roberta-base     facebook/bart-large
-    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 512})
-
-    memory = ConversationBufferMemory(
-        memory_key='chat_history', return_messages=True)
-    conversation_chain = ConversationalRetrievalChain.from_llm(
-        llm=llm,
-        retriever=vectorstore.as_retriever(),
-        memory=memory,
-    )
-    return conversation_chain
-
-def handle_userinput(user_question):
-
-    response = st.session_state.conversation({'question': user_question})
-    # response = st.session_state.conversation({'summarization': user_question})
-    st.session_state.chat_history = response['chat_history']
-
-
-    # st.empty()
-
-    for i, message in enumerate(st.session_state.chat_history):
-        if i % 2 == 0:
-            st.write(user_template.replace(
-                "{{MSG}}", message.content), unsafe_allow_html=True)
-
-        else:
-            st.write(bot_template.replace(
-                "{{MSG}}", message.content), unsafe_allow_html=True)
+# def get_text_chunks(text):
+#     text_splitter = CharacterTextSplitter(
+#         separator="\n",
+#         chunk_size=800,    #  1000
+#         chunk_overlap=200,
+#         length_function=len
+#     )
+#     chunks = text_splitter.split_text(text)
+#     return chunks
+
+
+# def get_vectorstore(text_chunks):
+#     # embeddings = OpenAIEmbeddings()
+
+#     #  pip install InstructorEmbedding
+#     #  pip install sentence-transformers==2.2.2
+#     embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+
+#     #  from InstructorEmbedding import INSTRUCTOR
+#     # model = INSTRUCTOR('hkunlp/instructor-xl')
+#     # sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
+#     # instruction = "Represent the Science title:"
+#     # embeddings = model.encode([[instruction, sentence]])
+
+#     # embeddings = model.encode(text_chunks)
+#     print('have Embeddings:   ')
+
+#     # text_chunks="this is a test"
+#     #   FAISS,  Chroma and other vector databases
+#     #
+#     vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+#     print('FAISS succeeds:   ')
+
+#     return vectorstore
+
+# def get_conversation_chain(vectorstore):
+#     # llm = ChatOpenAI()
+#     #  llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
+#     #  google/bigbird-roberta-base     facebook/bart-large
+#     llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 512})
+
+#     memory = ConversationBufferMemory(
+#         memory_key='chat_history', return_messages=True)
+#     conversation_chain = ConversationalRetrievalChain.from_llm(
+#         llm=llm,
+#         retriever=vectorstore.as_retriever(),
+#         memory=memory,
+#     )
+#     return conversation_chain
+
+# def handle_userinput(user_question):
+
+#     response = st.session_state.conversation({'question': user_question})
+#     # response = st.session_state.conversation({'summarization': user_question})
+#     st.session_state.chat_history = response['chat_history']
+
+
+#     # st.empty()
+
+#     for i, message in enumerate(st.session_state.chat_history):
+#         if i % 2 == 0:
+#             st.write(user_template.replace(
+#                 "{{MSG}}", message.content), unsafe_allow_html=True)
+
+#         else:
+#             st.write(bot_template.replace(
+#                 "{{MSG}}", message.content), unsafe_allow_html=True)