Update app.py

app.py

@@ -1,106 +1,213 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-import os
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-import google.generativeai as genai
-from langchain.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from dotenv import load_dotenv
+# import streamlit as st
+# from PyPDF2 import PdfReader
+# from langchain.text_splitter import RecursiveCharacterTextSplitter
+# import os
+# from langchain_google_genai import GoogleGenerativeAIEmbeddings
+# import google.generativeai as genai
+# from langchain.vectorstores import FAISS
+# from langchain_google_genai import ChatGoogleGenerativeAI
+# from langchain.chains.question_answering import load_qa_chain
+# from langchain.prompts import PromptTemplate
+# from dotenv import load_dotenv
 
-load_dotenv()
-os.getenv("GOOGLE_API_KEY")
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+# load_dotenv()
+# os.getenv("GOOGLE_API_KEY")
+# genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
 
 
 
 
 
-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_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 = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-    chunks = text_splitter.split_text(text)
-    return chunks
+# def get_text_chunks(text):
+#     text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
+#     chunks = text_splitter.split_text(text)
+#     return chunks
 
 
-def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index",allow_dangerous_deserialization=True)
+# def get_vector_store(text_chunks):
+#     embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+#     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+#     vector_store.save_local("faiss_index",allow_dangerous_deserialization=True)
 
 
-def get_conversational_chain():
+# def get_conversational_chain():
 
-    prompt_template = """
-    Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
-    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
-    Context:\n {context}?\n
-    Question: \n{question}\n
+#     prompt_template = """
+#     Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
+#     provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
+#     Context:\n {context}?\n
+#     Question: \n{question}\n
 
-    Answer:
-    """
+#     Answer:
+#     """
 
-    model = ChatGoogleGenerativeAI(model="gemini-pro",
-                             temperature=0.3)
+#     model = ChatGoogleGenerativeAI(model="gemini-pro",
+#                              temperature=0.3)
 
-    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
-    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+#     prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+#     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
 
-    return chain
+#     return chain
 
 
 
-def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+# def user_input(user_question):
+#     embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
     
-    new_db = FAISS.load_local("faiss_index", embeddings)
-    docs = new_db.similarity_search(user_question)
+#     new_db = FAISS.load_local("faiss_index", embeddings)
+#     docs = new_db.similarity_search(user_question)
 
-    chain = get_conversational_chain()
+#     chain = get_conversational_chain()
 
     
-    response = chain(
-        {"input_documents":docs, "question": user_question}
-        , return_only_outputs=True)
+#     response = chain(
+#         {"input_documents":docs, "question": user_question}
+#         , return_only_outputs=True)
 
-    print(response)
-    st.write("Reply: ", response["output_text"])
+#     print(response)
+#     st.write("Reply: ", response["output_text"])
 
 
 
 
-def main():
-    st.set_page_config("Chat PDF")
-    st.header("Chat with PDF using Gemini💁")
+# def main():
+#     st.set_page_config("Chat PDF")
+#     st.header("Chat with PDF using Gemini💁")
+
+#     user_question = st.text_input("Ask a Question from the PDF Files")
+
+#     if user_question:
+#         user_input(user_question)
+
+#     with st.sidebar:
+#         st.title("Menu:")
+#         pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
+#         if st.button("Submit & Process"):
+#             with st.spinner("Processing..."):
+#                 raw_text = get_pdf_text(pdf_docs)
+#                 text_chunks = get_text_chunks(raw_text)
+#                 get_vector_store(text_chunks)
+#                 st.success("Done")
+
+
+
+# if __name__ == "__main__":
+#     main()
+
+import streamlit as st
+from dotenv import load_dotenv
+# import PyPDF2
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain_community.document_loaders import PyMuPDFLoader
+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
+
+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=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
 
-    user_question = st.text_input("Ask a Question from the PDF Files")
+def get_vectorstore(text_chunks):
+    embeddings = OpenAIEmbeddings()
+    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    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})
+
+    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})
+    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:
-        user_input(user_question)
+        handle_userinput(user_question)
 
     with st.sidebar:
-        st.title("Menu:")
-        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
-        if st.button("Submit & Process"):
-            with st.spinner("Processing..."):
+        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"):
+            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_vector_store(text_chunks)
-                st.success("Done")
 
+                # create vector store
+                vectorstore = get_vectorstore(text_chunks)
+
+                # create conversation chain
+                st.session_state.conversation = get_conversation_chain(
+                    vectorstore)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()