Rename app (1).py to app.py

app (1).py → app.py

@@ -1,18 +1,10 @@
-"""
-Question Answering with Retrieval QA and LangChain Language Models featuring FAISS vector stores.
-This script uses the LangChain Language Model API to answer questions using Retrieval QA 
-and FAISS vector stores. It also uses the Mistral huggingface inference endpoint to 
-generate responses.
-"""
 
 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 HuggingFaceBgeEmbeddings
 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
@@ -20,20 +12,6 @@ from langchain.llms import HuggingFaceHub
 
 
 def get_pdf_text(pdf_docs):
-    """
-    Extract text from a list of PDF documents.
-
-    Parameters
-    ----------
-    pdf_docs : list
-        List of PDF documents to extract text from.
-
-    Returns
-    -------
-    str
-        Extracted text from all the PDF documents.
-
-    """
     text = ""
     for pdf in pdf_docs:
         pdf_reader = PdfReader(pdf)
@@ -43,20 +21,6 @@ def get_pdf_text(pdf_docs):
 
 
 def get_text_chunks(text):
-    """
-    Split the input text into chunks.
-
-    Parameters
-    ----------
-    text : str
-        The input text to be split.
-
-    Returns
-    -------
-    list
-        List of text chunks.
-
-    """
     text_splitter = CharacterTextSplitter(
         separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
     )
@@ -65,20 +29,6 @@ def get_text_chunks(text):
 
 
 def get_vectorstore(text_chunks):
-    """
-    Generate a vector store from a list of text chunks using HuggingFace BgeEmbeddings.
-
-    Parameters
-    ----------
-    text_chunks : list
-        List of text chunks to be embedded.
-
-    Returns
-    -------
-    FAISS
-        A FAISS vector store containing the embeddings of the text chunks.
-
-    """
     model = "BAAI/bge-base-en-v1.5"
     encode_kwargs = {
         "normalize_embeddings": True
@@ -91,20 +41,6 @@ def get_vectorstore(text_chunks):
 
 
 def get_conversation_chain(vectorstore):
-    """
-    Create a conversational retrieval chain using a vector store and a language model.
-
-    Parameters
-    ----------
-    vectorstore : FAISS
-        A FAISS vector store containing the embeddings of the text chunks.
-
-    Returns
-    -------
-    ConversationalRetrievalChain
-        A conversational retrieval chain for generating responses.
-
-    """
     llm = HuggingFaceHub(
         repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
         model_kwargs={"temperature": 0.5, "max_length": 1048},
@@ -119,45 +55,29 @@ def get_conversation_chain(vectorstore):
 
 
 def handle_userinput(user_question):
-    """
-    Handle user input and generate a response using the conversational retrieval chain.
-    Parameters
-    ----------
-    user_question : str
-        The user's 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: " + message.content)
+            st.write("👤 User: " + message.content)
         else:
             st.write("🤖 ChatBot: " + message.content)
 
 
 def main():
-    """
-    Putting it all together.
-    """
     st.set_page_config(
-        page_title="Chat with a Bot that tries to answer questions about multiple PDFs",
-        page_icon=":books:",
+        page_title="Chat with multiple PDFs",
+        page_icon="📄",
     )
 
-    st.markdown("# Chat with a Bot")
-    st.markdown("This bot tries to answer questions about multiple PDFs. Let the processing of the PDF finish before adding your question. 🙏🏾")
+    st.markdown("# Chat with multiple PDFs 📄")
 
     st.write(css, unsafe_allow_html=True)
 
-    # set huggingface hub token in st.text_input widget
-    # then hide the input
     huggingface_token = st.text_input("Enter your HuggingFace Hub token", type="password")
-    #openai_api_key = st.text_input("Enter your OpenAI API key", type="password")
 
-    # set this key as an environment variable
     os.environ["HUGGINGFACEHUB_API_TOKEN"] = huggingface_token
-    #os.environ["OPENAI_API_KEY"] = openai_api_key
 
 
     if "conversation" not in st.session_state:
@@ -165,7 +85,6 @@ def main():
     if "chat_history" not in st.session_state:
         st.session_state.chat_history = None
 
-    st.header("Chat with a Bot 🤖🦾 that tries to answer questions about multiple PDFs :books:")
     user_question = st.text_input("Ask a question about your documents:")
     if user_question:
         handle_userinput(user_question)