Delete app.py

app.py

@@ -1,108 +0,0 @@
-import streamlit as st
-import os
-from langchain_groq import ChatGroq
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.chains.combine_documents import create_stuff_documents_chain
-from langchain_core.prompts import ChatPromptTemplate
-from langchain.chains import create_retrieval_chain
-from langchain_community.vectorstores import FAISS
-from langchain_community.document_loaders import PyPDFLoader
-from dotenv import load_dotenv
-import tempfile
-
-
-load_dotenv()
-
-
-groq_api_key = os.getenv('GROQ_API_KEY')
-
-
-st.markdown("<h2 style='text-align: center;'>PDF Insights: Interactive Q&A Chatbot with Groq API</h2>", unsafe_allow_html=True)
-
-
-llm = ChatGroq(groq_api_key=groq_api_key, model_name="Llama3-8b-8192")
-
-
-prompt = ChatPromptTemplate.from_template(
-    """
-    Answer the questions based on the provided context only.
-    Please provide the most accurate response based on the question.
-    If the answer is not in the document, just say "Please Contact the Business Directly". Dont say wrong answer.
-    <context>
-    {context}
-    <context>
-    Questions: {input}
-    """
-)
-
-def create_vector_db_out_of_the_uploaded_pdf_file(pdf_file):
-
-
-    if "vector_store" not in st.session_state:
-
-        with tempfile.NamedTemporaryFile(delete=False) as temp_file:
-
-            temp_file.write(pdf_file.read())
-
-            pdf_file_path = temp_file.name
-
-        st.session_state.embeddings = HuggingFaceEmbeddings(model_name='BAAI/bge-small-en-v1.5', model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
-        
-        st.session_state.loader = PyPDFLoader(pdf_file_path)
-
-        st.session_state.text_document_from_pdf = st.session_state.loader.load()
-
-        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-        
-        st.session_state.final_document_chunks = st.session_state.text_splitter.split_documents(st.session_state.text_document_from_pdf)
-
-        st.session_state.vector_store = FAISS.from_documents(st.session_state.final_document_chunks, st.session_state.embeddings)
-
-
-pdf_input_from_user = st.file_uploader("Upload the PDF file", type=['pdf'])
-
-
-if pdf_input_from_user is not None:
-
-    if st.button("Create the Vector DB from the uploaded PDF file"):
-        
-        if pdf_input_from_user is not None:
-            
-            create_vector_db_out_of_the_uploaded_pdf_file(pdf_input_from_user)
-            
-            st.success("Vector Store DB for this PDF file Is Ready")
-        
-        else:
-            
-            st.write("Please upload a PDF file first")
-
-
-
-if "vector_store" in st.session_state:
-
-    user_prompt = st.text_input("Enter Your Question related to the uploaded PDF")
-
-    if st.button('Submit Prompt'):
-
-        if user_prompt:
-            
-            if "vector_store" in st.session_state:
-
-                document_chain = create_stuff_documents_chain(llm, prompt)
-
-                retriever = st.session_state.vector_store.as_retriever()
-
-                retrieval_chain = create_retrieval_chain(retriever, document_chain)
-
-                response = retrieval_chain.invoke({'input': user_prompt})
-
-                st.write(response['answer'])
-
-            else:
-
-                st.write("Please embed the document first by uploading a PDF file.")
-
-        else:
-
-            st.error('Please write your prompt')