Delete app.py

app.py

@@ -1,79 +0,0 @@
-import streamlit as st
-import os
-from langchain_groq import ChatGroq
-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 langchain_google_genai import GoogleGenerativeAIEmbeddings
-from dotenv import load_dotenv
-import time
-
-load_dotenv()
-
-## load the GROQ And OpenAI API KEY 
-groq_api_key = os.getenv('GROQ_API_KEY')
-os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")
-
-st.title("Gemma Model Document Q&A")
-
-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.
-<context>
-{context}
-<context>
-Questions: {input}
-"""
-)
-
-def vector_embedding(uploaded_files):
-
-    if "vectors" not in st.session_state:
-
-        st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-        # Load documents from the uploaded PDF files
-        documents = []
-        for uploaded_file in uploaded_files:
-            loader = PyPDFLoader(uploaded_file)
-            documents.extend(loader.load())
-
-        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
-        st.session_state.final_documents = st.session_state.text_splitter.split_documents(documents)
-        
-        if st.session_state.final_documents:
-            st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
-            st.write("Vector Store DB Is Ready")
-        else:
-            st.write("No documents were loaded or processed. Please check your files.")
-
-prompt1 = st.text_input("Enter Your Question From Documents")
-
-uploaded_files = st.file_uploader("Upload your PDF files", accept_multiple_files=True, type=["pdf"])
-
-if st.button("Documents Embedding") and uploaded_files:
-    vector_embedding(uploaded_files)
-
-if prompt1 and "vectors" in st.session_state:
-    document_chain = create_stuff_documents_chain(llm, prompt)
-    retriever = st.session_state.vectors.as_retriever()
-    retrieval_chain = create_retrieval_chain(retriever, document_chain)
-    start = time.process_time()
-    response = retrieval_chain.invoke({'input': prompt1})
-    st.write(f"Response time: {time.process_time() - start:.2f} seconds")
-    st.write(response['answer'])
-
-    # With a Streamlit expander
-    with st.expander("Document Similarity Search"):
-        # Find the relevant chunks
-        for i, doc in enumerate(response["context"]):
-            st.write(doc.page_content)
-            st.write("--------------------------------")
-else:
-    st.write("Please upload your documents and click on 'Documents Embedding' first.")