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 RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from htmltemp import css, bot_template, user_template
+from langchain.llms import HuggingFaceHub
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="PDF Chatbot", page_icon="📚")
+    custom_html = """
+    <div class="banner">
+        <img src="https://www.canva.com/design/DAFys-F940k/2s_2FuK_FCWlBKS8VEWLMA/view?utm_content=DAFys-F940k&utm_campaign=designshare&utm_medium=link&utm_source=editor" alt="Banner Image">
+    </div>
+    <style>
+        .banner {
+            width: 160%;
+            height: 200px;
+            overflow: hidden;
+            }
+        .banner img {
+            width: 100%;
+            object-fit: cover;
+            }
+    </style>
+    """
+    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 PDFs 📚")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+
+    with st.sidebar:
+        st.sidebar.info("""Note: I haven't used any GPU for this project so It can take 
+        long time to process large PDFs. Also this is POC project and can be easily upgraded
+        with better model and resources.  """)
+
+        st.subheader("Your PDFs")
+        pdf_docs = st.file_uploader(
+            "Upload your PDFs here", 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)
+
+                # create vector store
+                vectorstore = get_vectorstore(text_chunks)
+
+                # create conversation chain
+                st.session_state.conversation = get_conversation_chain(vectorstore)
+                st.success("file uploaded")
+
+
+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(
+        separators=["\n\n", "\n", "."], chunk_size=900, chunk_overlap=200, length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def get_vectorstore(text_chunks):
+    embeddings = HuggingFaceBgeEmbeddings(model_name="BAAI/bge-base-en-v1.5")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+
+def get_conversation_chain(vectorstore):
+    llm = HuggingFaceHub(
+        repo_id="google/flan-t5-large",
+        model_kwargs={"temperature": 0.5, "max_length": 1024},
+        
+    )
+
+    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
+            )
+
+
+if __name__ == "__main__":
+    main()