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app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from html_template import css, bot_template, user_template
+from langchain.llms import HuggingFaceHub
+import os
+
+FREE_RUN = False
+
+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
+
+def get_vector_store(text_chunks):
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl") if FREE_RUN else OpenAIEmbeddings()
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+def get_conversation_chain(vectorstore):
+    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={
+                        "temperature": 0.5, "max_length": 512}) if FREE_RUN else ChatOpenAI()
+    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():
+    st.set_page_config(page_title="WhisperChain 🔗", page_icon=":link:")
+    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("WhisperChain 🔗")
+    user_question = st.text_input("Ask a question about your documents.")
+
+    if user_question:
+        handle_userinput(user_question)
+
+    with st.sidebar:
+        
+        ###
+        OPENAI_API_KEY = st.sidebar.text_input("Enter OpenAI API Key", type="password")
+        HUGGINGFACEHUB_API_KEY = st.sidebar.text_input("Enter Hugging Face API Key", type="password")
+
+        if not OPENAI_API_KEY or not HUGGINGFACEHUB_API_KEY:
+            st.sidebar.error("Please enter your API keys")
+            st.stop()
+
+        os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+        os.environ["HUGGINGFACEHUB_API_KEY"] = HUGGINGFACEHUB_API_KEY
+
+        #Toggle free run
+        global FREE_RUN
+        FREE_RUN = st.sidebar.checkbox("Free run", value=False)
+        ###
+
+        pdf_docs = st.file_uploader(
+            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
+
+        if st.button("Process"):
+            if pdf_docs:
+                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
+                    vector_store = get_vector_store(text_chunks)
+
+                    # create conversation chain
+                    st.session_state.conversation = get_conversation_chain(vector_store)
+            else:
+                st.error("Please upload at least one PDF")
+
+if __name__ == '__main__':
+    main()

html_template.py

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+css = '''
+<style>
+.chat-message {
+    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
+}
+.chat-message.user {
+    background-color: #2b313e
+}
+.chat-message.bot {
+    background-color: #475063
+}
+.chat-message .avatar {
+  width: 20%;
+}
+.chat-message .avatar img {
+  max-width: 78px;
+  max-height: 78px;
+  border-radius: 50%;
+  object-fit: cover;
+}
+.chat-message .message {
+  width: 80%;
+  padding: 0 1.5rem;
+  color: #fff;
+}
+'''
+
+bot_template = '''
+<div class="chat-message bot">
+    <div class="message">{{MSG}}</div>
+</div>
+'''
+
+user_template = '''
+<div class="chat-message user">  
+    <div class="message">{{MSG}}</div>
+</div>
+'''

requirements.txt

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+streamlit
+pypdf2
+langchain
+python-dotenv
+faiss-cpu
+openai 
+huggingface_hub
+tiktoken
+InstructorEmbedding
+sentence_transformers
+```