Update app.py

app.py

@@ -1,165 +1,196 @@
-import streamlit as st
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-# from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from htmlTemplates import css, bot_template, user_template
-from langchain.llms import HuggingFaceHub
+import google.generativeai as palm
+import streamlit as st 
 import os 
-# from transformers import T5Tokenizer, T5ForConditionalGeneration
-# from langchain.callbacks import get_openai_callback
-
-hub_token = os.environ["HUGGINGFACE_HUB_TOKEN"]
-
-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
 
+# Set your API key
+palm.configure(api_key = os.environ['PALM_KEY'])
 
-def get_text_chunks(text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=200,
-        chunk_overlap=20,
-        length_function=len
-    )
-    chunks = text_splitter.split_text(text)
-    return chunks
+# Select the PaLM 2 model
+model = 'models/text-bison-001'
 
+# Generate text
+if prompt := st.chat_input("Ask your query..."):
+    enprom = f"""Answer the below provided input in context to Bhagwad Geeta. Use the verses and chapters sentences as references to your answer with suggestions
+    coming from Bhagwad Geeta. Your answer to below input should only be in context to Bhagwad geeta only.\nInput= {prompt}"""
+    completion = palm.generate_text(model=model, prompt=enprom, temperature=0.5, max_output_tokens=800)
 
-def get_vectorstore(text_chunks):
-    # embeddings = OpenAIEmbeddings()
-    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
-    embeddings = HuggingFaceEmbeddings()
-    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
-    return vectorstore
+# response = palm.chat(messages=["Hello."])
+# print(response.last) #  'Hello! What can I help you with?'
+# response.reply("Can you tell me a joke?")
 
+# Print the generated text
+    with st.chat_message("Assistant"):
+        st.write(completion.result)
 
-def get_conversation_chain(vectorstore):
-    # llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
-    # tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
-    # model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
 
-    llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-v0.1", huggingfacehub_api_token=hub_token, model_kwargs={"temperature":0.5, "max_length":20})
 
-    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
-    reply = response.run(user_question)
-    st.write(reply)
-    # st.session_state.chat_history = response['chat_history']
 
-    # 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 main():
-    load_dotenv()
-    st.set_page_config(page_title="Chat with multiple PDFs",
-                       page_icon=":books:")
-    st.write(css, unsafe_allow_html=True)
-
-    if "conversation" not in st.session_state:
-        st.session_state.conversation = None
-    if "chat_history" not in st.session_state:
-        st.session_state.chat_history = None
-
-    st.header("Chat with multiple PDFs :books:")
-    user_question = st.text_input("Ask a question about your documents:")
-    if user_question:
-        handle_userinput(user_question)
+# import streamlit as st
+# from dotenv import load_dotenv
+# from PyPDF2 import PdfReader
+# from langchain.text_splitter import CharacterTextSplitter
+# from langchain.embeddings import HuggingFaceEmbeddings
+# from langchain.vectorstores import FAISS
+# # from langchain.chat_models import ChatOpenAI
+# from langchain.memory import ConversationBufferMemory
+# from langchain.chains import ConversationalRetrievalChain
+# from htmlTemplates import css, bot_template, user_template
+# from langchain.llms import HuggingFaceHub
+# import os 
+# # from transformers import T5Tokenizer, T5ForConditionalGeneration
+# # from langchain.callbacks import get_openai_callback
+
+# hub_token = os.environ["HUGGINGFACE_HUB_TOKEN"]
+
+# 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=200,
+#         chunk_overlap=20,
+#         length_function=len
+#     )
+#     chunks = text_splitter.split_text(text)
+#     return chunks
+
+
+# def get_vectorstore(text_chunks):
+#     # embeddings = OpenAIEmbeddings()
+#     # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+#     embeddings = HuggingFaceEmbeddings()
+#     vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+#     return vectorstore
+
+
+# def get_conversation_chain(vectorstore):
+#     # llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
+#     # tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
+#     # model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base")
+
+#     llm = HuggingFaceHub(repo_id="mistralai/Mistral-7B-v0.1", huggingfacehub_api_token=hub_token, model_kwargs={"temperature":0.5, "max_length":20})
+
+#     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
+
 
-    with st.sidebar:
-        st.subheader("Your documents")
-        pdf_docs = st.file_uploader(
-            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
-        if st.button("Process"):
-            if(len(pdf_docs) == 0):
-                st.error("Please upload at least one PDF")
-            else:
-                with st.spinner("Processing"):
-                    # get pdf text
-                    raw_text = get_pdf_text(pdf_docs)
+# def handle_userinput(user_question):
+#     response = st.session_state.conversation
+#     reply = response.run(user_question)
+#     st.write(reply)
+#     # st.session_state.chat_history = response['chat_history']
+
+#     # 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 main():
+#     load_dotenv()
+#     st.set_page_config(page_title="Chat with multiple PDFs",
+#                        page_icon=":books:")
+#     st.write(css, unsafe_allow_html=True)
+
+#     if "conversation" not in st.session_state:
+#         st.session_state.conversation = None
+#     if "chat_history" not in st.session_state:
+#         st.session_state.chat_history = None
+
+#     st.header("Chat with multiple PDFs :books:")
+#     user_question = st.text_input("Ask a question about your documents:")
+#     if user_question:
+#         handle_userinput(user_question)
+
+#     with st.sidebar:
+#         st.subheader("Your documents")
+#         pdf_docs = st.file_uploader(
+#             "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
+#         if st.button("Process"):
+#             if(len(pdf_docs) == 0):
+#                 st.error("Please upload at least one PDF")
+#             else:
+#                 with st.spinner("Processing"):
+#                     # get pdf text
+#                     raw_text = get_pdf_text(pdf_docs)
+
+#                     # get the text chunks
+#                     text_chunks = get_text_chunks(raw_text)
 
-                    # get the text chunks
-                    text_chunks = get_text_chunks(raw_text)
+#                     # create vector store
+#                     vectorstore = get_vectorstore(text_chunks)
 
-                    # create vector store
-                    vectorstore = get_vectorstore(text_chunks)
+#                     # create conversation chain
+#                     st.session_state.conversation = get_conversation_chain(
+#                         vectorstore)
 
-                    # create conversation chain
-                    st.session_state.conversation = get_conversation_chain(
-                        vectorstore)
-
-if __name__ == '__main__':
-    main()
+# if __name__ == '__main__':
+#     main()
 
 
 
 
 
 
-# import os
-# import getpass
-# import streamlit as st
-# from langchain.document_loaders import PyPDFLoader
-# from langchain.text_splitter import RecursiveCharacterTextSplitter
-# from langchain.embeddings import HuggingFaceEmbeddings
-# from langchain.vectorstores import Chroma
-# from langchain import HuggingFaceHub
-# from langchain.chains import RetrievalQA
-# # __import__('pysqlite3')
-# # import sys
-# # sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
+# # import os
+# # import getpass
+# # import streamlit as st
+# # from langchain.document_loaders import PyPDFLoader
+# # from langchain.text_splitter import RecursiveCharacterTextSplitter
+# # from langchain.embeddings import HuggingFaceEmbeddings
+# # from langchain.vectorstores import Chroma
+# # from langchain import HuggingFaceHub
+# # from langchain.chains import RetrievalQA
+# # # __import__('pysqlite3')
+# # # import sys
+# # # sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
 
 
-# # load huggingface api key
-# hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"]
+# # # load huggingface api key
+# # hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"]
 
-# # use streamlit file uploader to ask user for file
-# # file = st.file_uploader("Upload PDF")
+# # # use streamlit file uploader to ask user for file
+# # # file = st.file_uploader("Upload PDF")
 
 
-# path = "Geeta.pdf"
-# loader = PyPDFLoader(path)
-# pages = loader.load()
+# # path = "Geeta.pdf"
+# # loader = PyPDFLoader(path)
+# # pages = loader.load()
 
-# # st.write(pages)
+# # # st.write(pages)
 
-# splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
-# docs = splitter.split_documents(pages)
+# # splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
+# # docs = splitter.split_documents(pages)
 
-# embeddings = HuggingFaceEmbeddings()
-# doc_search = Chroma.from_documents(docs, embeddings)
+# # embeddings = HuggingFaceEmbeddings()
+# # doc_search = Chroma.from_documents(docs, embeddings)
 
-# repo_id = "tiiuae/falcon-7b"
-# llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000})
+# # repo_id = "tiiuae/falcon-7b"
+# # llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000})
 
-# from langchain.schema import retriever
-# retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever())
+# # from langchain.schema import retriever
+# # retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever())
 
-# if query := st.chat_input("Enter a question: "):
-#   with st.chat_message("assistant"):
-#     st.write(retireval_chain.run(query))
+# # if query := st.chat_input("Enter a question: "):
+# #   with st.chat_message("assistant"):
+# #     st.write(retireval_chain.run(query))