Delete app.py

app.py

@@ -1,99 +0,0 @@
-import os
-import streamlit as st
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain_community.vectorstores import FAISS
-from langchain.memory import ConversationBufferMemory
-from langchain.chains import ConversationalRetrievalChain
-from langchain.llms import HuggingFaceHub
-from langchain.embeddings import HuggingFaceEmbeddings
-
-def get_pdf_text(pdf_docs):
-    text = ""
-    for pdf in pdf_docs:
-        try:
-            pdf_reader = PdfReader(pdf)
-            for page in pdf_reader.pages:
-                text += page.extract_text()
-        except Exception as e:
-            st.error(f"Error reading {pdf.name}: {e}. Skipping this file.")
-    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_vectorstore(text_chunks):
-    try:
-        embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-        vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embedding)
-        return vectorstore
-    except Exception as e:
-        st.error(f"Error creating vector store: {e}")
-        return None
-
-def get_conversation_chain(vectorstore):
-    # Fetch the HuggingFace API token from environment variable
-    api_token = os.getenv("HUGGINGFACE_API_TOKEN")
-    if not api_token:
-        st.error("HuggingFace API token not found. Please ensure it is set in the environment variables.")
-        return None
-
-    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.5, "max_length": 512}, huggingfacehub_api_token=api_token)
-
-    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']
-
-def main():
-    load_dotenv()
-    st.set_page_config(page_title="Chat with multiple PDFs", page_icon=":books:")
-
-    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 multiple PDFs :books:")
-    user_question = st.text_input("Ask a question about your documents:")
-    if user_question:
-        handle_userinput(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"):
-                # get pdf text
-                raw_text = get_pdf_text(pdf_docs)
-
-                if raw_text:  # Proceed only if there is valid text
-                    # get the text chunks
-                    text_chunks = get_text_chunks(raw_text)
-
-                    # create vector store
-                    vectorstore = get_vectorstore(text_chunks)
-
-                    if vectorstore:  # Check if vectorstore is valid
-                        # create conversation chain
-                        st.session_state.conversation = get_conversation_chain(vectorstore)
-
-if __name__ == '__main__':
-    main()