delete.py

app.py

@@ -1,96 +0,0 @@
-from dotenv import load_dotenv
-load_dotenv()
-
-import os
-import pickle
-import streamlit as st
-from scanned_pdf_parser import get_text_from_scanned_pdf
-from langchain.embeddings import HuggingFaceInstructEmbeddings
-from langchain.llms import GooglePalm
-from langchain.prompts import PromptTemplate
-from langchain.chains import RetrievalQA
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.document_loaders import PyPDFLoader
-from langchain.vectorstores import FAISS
-from langchain.docstore.document import Document
-
-llm = GooglePalm(temperature=0.9)
-
-st.title("PDF Query Tool")
-st.write("Upload your PDF and ask question from it")
-
-uploaded_file = st.file_uploader("Choose a PDF file")
-main_placeholder = st.empty()
-second_placeholder = st.empty()
-
-
-if uploaded_file:
-    filename = uploaded_file.name
-    if not filename.endswith(('.pdf', '.PDF')):
-        main_placeholder.warning("Choose PDF Document !!!")
-        exit()
-    elif not os.path.exists(uploaded_file.name):
-        main_placeholder.text("Data Loading Started...⌛⌛⌛")
-        with open(f'{uploaded_file.name}', 'wb') as f:
-            f.write(uploaded_file.getbuffer())
-
-        pdf_loader = PyPDFLoader(uploaded_file.name)
-        documents = pdf_loader.load()
-
-        raw_text = ''
-        for doc in documents:
-            raw_text += doc.page_content
-
-        if len(raw_text) < 10:
-            main_placeholder.text("It looks like Scanned PDF, No worries converting it...⌛⌛⌛")
-            raw_text = get_text_from_scanned_pdf(uploaded_file.name)
-
-        main_placeholder.text("Splitting text into smaller chunks...⌛⌛⌛")
-        text_splitter = RecursiveCharacterTextSplitter(
-            separators=['\n\n', '\n', '.', ','],
-            chunk_size=2000
-        )
-
-        texts = text_splitter.split_text(raw_text)
-        docs = [Document(page_content=t) for t in texts]
-
-        embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base")
-        main_placeholder.text("Storing data into Vector Database...⌛⌛⌛")
-        vectorstore = FAISS.from_documents(docs, embeddings)
-
-        # Save the FAISS index to a pickle file
-        with open(f'vector_store_{uploaded_file.name}.pkl', "wb") as f:
-            pickle.dump(vectorstore, f)
-
-    main_placeholder.text("Data Loading Completed...✅✅✅")
-
-
-query = second_placeholder.text_input("Question:")
-if query:
-    if os.path.exists(f'vector_store_{uploaded_file.name}.pkl'):
-        with open(f'vector_store_{uploaded_file.name}.pkl', "rb") as f:
-            vector_store = pickle.load(f)
-
-        prompt_template = """
-            <context>
-            {context}
-            </context>
-            Question: {question}
-            Assistant:"""
-        prompt = PromptTemplate(
-            template=prompt_template, input_variables=["context", "question"]
-        )
-
-        chain = RetrievalQA.from_chain_type(
-            llm=llm,
-            chain_type="stuff",
-            retriever=vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 1}),
-            return_source_documents=True,
-            chain_type_kwargs={"prompt": prompt}
-        )
-
-        with st.spinner("Searching for the answer..."):
-            result = chain({"query": query})
-        st.header("Answer")
-        st.write(result["result"])
-