Delete WRONG_Question.txt

WRONG_Question.txt

@@ -1,130 +0,0 @@
-# Importing dependencies
-from dotenv import load_dotenv
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.prompts import PromptTemplate
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-from htmlTemplates import css, bot_template, user_template
-
-# Load environment variables
-load_dotenv()
-
-# Creating custom template to guide LLM model
-custom_template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question, in its original language.
-Chat History:
-{chat_history}
-Follow Up Input: {question}
-Standalone question:"""
-
-CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(custom_template)
-
-# Extracting text from PDF
-def get_pdf_text(docs):
-    text = ""
-    for pdf in docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
-
-# Converting text to chunks
-def get_chunks(raw_text):
-    text_splitter = CharacterTextSplitter(
-        separator="\n",
-        chunk_size=1000,
-        chunk_overlap=200,
-        length_function=len
-    )
-    chunks = text_splitter.split_text(raw_text)
-    return chunks
-
-# Using Hugging Face embeddings model and FAISS to create vectorstore
-def get_vectorstore(chunks):
-    embeddings = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-L6-v2",
-        model_kwargs={'device': 'cpu'}
-    )
-    vectorstore = FAISS.from_texts(texts=chunks, embedding=embeddings)
-    return vectorstore
-
-# Generating conversation chain
-def get_conversationchain(vectorstore):
-    # Use a Hugging Face model for question-answering
-    model_name = "distilbert-base-uncased-distilled-squad"  # Pretrained QA model
-    qa_pipeline = pipeline("question-answering", model=model_name, tokenizer=model_name)
-
-    def qa_function(question, context):
-        response = qa_pipeline(question=question, context=context)
-        return response['answer']
-
-    memory = ConversationBufferMemory(
-        memory_key='chat_history',
-        return_messages=True,
-        output_key='answer'
-    )
-
-    def conversation_chain(inputs):
-        question = inputs['question']
-        # Extract text content from Document objects
-        documents = vectorstore.similarity_search(question, k=5)
-        if not documents:
-            answer = "Sorry, I couldn't find relevant information in the document. Please ask a question related to the document."
-            memory.save_context({"user_input": question}, {"answer": answer})
-            return {"chat_history": memory.chat_memory.messages, "answer": answer}
-
-        context = "\n".join([doc.page_content for doc in documents])  # Extract `page_content` from each Document
-        answer = qa_function(question, context)
-        memory.save_context({"user_input": question}, {"answer": answer})
-        return {"chat_history": memory.chat_memory.messages, "answer": answer}
-
-    return conversation_chain
-
-# Generating response from user queries and displaying them accordingly
-def handle_question(question):
-    response = st.session_state.conversation({'question': question})
-    st.session_state.chat_history = response["chat_history"]
-    for i, msg in enumerate(st.session_state.chat_history):
-        if i % 2 == 0:
-            st.write(user_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
-        else:
-            st.write(bot_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
-
-def main():
-    st.set_page_config(page_title="Chat with multiple 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("CSS Edge - Intelligent Document Chatbot :books:")
-    question = st.text_input("Ask a question from your document:")
-    if question:
-        handle_question(question)
-    
-    with st.sidebar:
-        st.subheader("Your documents")
-        docs = st.file_uploader("Upload your PDF here and click on 'Process'", accept_multiple_files=True)
-        if st.button("Process"):
-            with st.spinner("Processing..."):
-                # Get the PDF text
-                raw_text = get_pdf_text(docs)
-                
-                # Get the text chunks
-                text_chunks = get_chunks(raw_text)
-                
-                # Create vectorstore
-                vectorstore = get_vectorstore(text_chunks)
-                
-                # Create conversation chain
-                st.session_state.conversation = get_conversationchain(vectorstore)
-
-if __name__ == '__main__':
-    main()