Create app.py

app.py

@@ -1,26 +1,26 @@
-import google.generativeai as palm
-import streamlit as st 
-import os 
+# import google.generativeai as palm
+# import streamlit as st 
+# import os 
 
-# Set your API key
-palm.configure(api_key = os.environ['PALM_KEY'])
+# # Set your API key
+# palm.configure(api_key = os.environ['PALM_KEY'])
 
-# Select the PaLM 2 model
-model = 'models/text-bison-001'
+# # 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)
+# # 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)
 
-# response = palm.chat(messages=["Hello."])
-# print(response.last) #  'Hello! What can I help you with?'
-# response.reply("Can you tell me a joke?")
+# # 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)
+# # Print the generated text
+#     with st.chat_message("Assistant"):
+#         st.write(completion.result)
 
 
 
@@ -29,168 +29,168 @@ if prompt := st.chat_input("Ask your query..."):
 
 
 
-# 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
+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_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_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})
+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")
 
-#     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
+    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 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
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Chat with multiple PDFs",
+                       page_icon=":books:")
+    st.write(css, unsafe_allow_html=True)
 
-#     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)
+    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
 
-#     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)
+    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)
 
-#                     # get the text chunks
-#                     text_chunks = get_text_chunks(raw_text)
+    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)
 
-#                     # create vector store
-#                     vectorstore = get_vectorstore(text_chunks)
+                    # get the text chunks
+                    text_chunks = get_text_chunks(raw_text)
 
-#                     # create conversation chain
-#                     st.session_state.conversation = get_conversation_chain(
-#                         vectorstore)
+                    # create vector store
+                    vectorstore = get_vectorstore(text_chunks)
 
-# if __name__ == '__main__':
-#     main()
+                    # create conversation chain
+                    st.session_state.conversation = get_conversation_chain(
+                        vectorstore)
+
+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))