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app.py

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+import os
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+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
+
+def extract_text_from_pdfs(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 split_text_into_chunks(text):
+    text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)
+    return text_splitter.split_text(text)
+
+def create_vector_store_from_text_chunks(text_chunks):
+    key = os.getenv('OPENAI_KEY')
+    embeddings = OpenAIEmbeddings(openai_api_key=key)
+    return FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+
+def create_conversation_chain(vectorstore):
+    llm = ChatOpenAI()
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)
+
+def process_user_input(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):
+        template = user_template if i % 2 == 0 else bot_template
+        st.write(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)
+
+    st.header("Chat with multiple PDFs :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        process_user_input(user_question)
+
+    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"):
+            with st.spinner("Processing"):
+                raw_text = extract_text_from_pdfs(pdf_docs)
+                text_chunks = split_text_into_chunks(raw_text)
+                vectorstore = create_vector_store_from_text_chunks(text_chunks)
+                st.session_state.conversation = create_conversation_chain(vectorstore)
+
+if __name__ == '__main__':
+    main()

htmlTemplates.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="avatar">
+        <img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
+    </div>
+    <div class="message">{{MSG}}</div>
+</div>
+'''
+
+user_template = '''
+<div class="chat-message user">
+    <div class="avatar">
+        <img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png">
+    </div>    
+    <div class="message">{{MSG}}</div>
+</div>
+'''

requirements.txt

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+langchain==0.0.184
+PyPDF2==3.0.1
+python-dotenv==1.0.0
+streamlit==1.18.1
+openai==0.27.6
+faiss-cpu==1.7.4
+tiktoken
+
+# uncomment to use huggingface llms
+# huggingface-hub==0.14.1
+
+# uncomment to use instructor embeddings
+# InstructorEmbedding==1.0.1
+# sentence-transformers==2.2.2