Create app.py

app.py

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+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from htmlTemplates import bot_template, user_template, css
+
+from transformers import pipeline
+
+def get_pdf_text(pdf_files):
+    
+    text = ""
+    for pdf_file in pdf_files:
+        reader = PdfReader(pdf_file)
+        for page in reader.pages:
+            text += page.extract_text()
+    return text
+
+def get_chunk_text(text):
+    
+    text_splitter = CharacterTextSplitter(
+    separator = "\n",
+    chunk_size = 1000,
+    chunk_overlap = 200,
+    length_function = len
+    )
+
+    chunks = text_splitter.split_text(text)
+
+    return chunks
+
+
+def get_vector_store(text_chunks):
+    
+    # For OpenAI Embeddings
+    
+    embeddings = OpenAIEmbeddings()
+    
+    # For Huggingface Embeddings
+
+    # embeddings = HuggingFaceInstructEmbeddings(model_name = "hkunlp/instructor-xl")
+
+    vectorstore = FAISS.from_texts(texts = text_chunks, embedding = embeddings)
+    
+    return vectorstore
+
+def get_conversation_chain(vector_store):
+    
+    # OpenAI Model
+
+    llm = ChatOpenAI()
+
+    # HuggingFace Model
+
+    # 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 = vector_store.as_retriever(),
+        memory = memory
+    )
+
+    return conversation_chain
+
+def handle_user_input(question):
+
+    response = st.session_state.conversation({'question':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 Your own 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 Your own PDFs :books:')
+    question = st.text_input("Ask anything to your PDF: ")
+
+    if question:
+        handle_user_input(question)
+    
+
+    with st.sidebar:
+        st.subheader("Upload your Documents Here: ")
+        pdf_files = st.file_uploader("Choose your PDF Files and Press OK", type=['pdf'], accept_multiple_files=True)
+
+        if st.button("OK"):
+            with st.spinner("Processing your PDFs..."):
+
+                # Get PDF Text
+                raw_text = get_pdf_text(pdf_files)
+
+                # Get Text Chunks
+                text_chunks = get_chunk_text(raw_text)
+                
+
+                # Create Vector Store
+                
+                vector_store = get_vector_store(text_chunks)
+                st.write("DONE")
+
+                # Create conversation chain
+
+                st.session_state.conversation =  get_conversation_chain(vector_store)
+
+
+if __name__ == '__main__':
+    main()