Update app.py

app.py

@@ -1,112 +1,2 @@
 import os
-# exec(os.getenv("CODE"))   # to execute the whole code in huggingface.
-
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-import google.generativeai as genai
-from langchain_community.vectorstores import FAISS
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from dotenv import load_dotenv
-import base64
-from io import BytesIO
-
-load_dotenv()
-
-genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
-
-## going to each and very pdf and each page of that padf and extracting text from it.
-def get_pdf_text(pdf_docs):
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(BytesIO(pdf.read()))
-        for page in pdf_reader.pages:
-            text+=page.extract_text()
-    return text
-
-def get_text_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size = 10000, chunk_overlap = 1000)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-## converting chunks into vectors
-def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    vector_store = FAISS.from_texts(text_chunks, embedding =embeddings)
-    vector_store.save_local("faiss_index")
-
-## developing bot
-def get_conversational_chain():
-    prompt_template= """
-    Answer the question as detailed as possible from the provided context, make sure to provide
-    all the details if the answer is not in the provided context just say, "answer is not available in the context",
-    don't provide the wrong answer.
-    Context: \n{context}?\n
-    Question: \n{question}\n
-
-    Answer:
-    """
-    model = ChatGoogleGenerativeAI(model = "gemini-pro", temperature= 0.45)
-    prompt= PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
-    chain = load_qa_chain(model, chain_type="stuff", prompt= prompt)
-    return chain
-
-## the user input interface
-def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model='models/embedding-001')
-
-    db = FAISS.load_local('faiss_index', embeddings, allow_dangerous_deserialization= True)
-    docs = db.similarity_search(user_question)
-
-    chain = get_conversational_chain()
-
-    response= chain({"input_documents":docs, "question":user_question}, return_only_outputs=True)
-
-    print(response)
-    st.write("Bot: ", response["output_text"])
-
-# streamlit app
-def main():
-    st.set_page_config(page_title="Chat With Multiple PDF")
-
-    # Function to set a background image
-    def set_background(image_file):
-        with open(image_file, "rb") as image:
-            b64_image = base64.b64encode(image.read()).decode("utf-8")
-        css = f"""
-        <style>
-        .stApp {{
-            background: url(data:image/png;base64,{b64_image});
-            background-size: cover;
-            background-position: centre;
-            backgroun-repeat: no-repeat;
-        }}
-        </style>
-        """
-        st.markdown(css, unsafe_allow_html=True)
-
-    # Set the background image
-    set_background("background_image.png")
-
-    st.header("Podcast With Your PDF's")
-
-    user_question = st.text_input("Ask a Question from the PDF Files")
-
-    if user_question:
-        user_input(user_question)
-    
-    with st.sidebar:
-        st.title("Menu:")
-        pdf_docs = st.file_uploader("Upload Your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True, type='pdf')
-        if st.button("Submit & Process") :
-            with st.spinner("Processing..."):
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                get_vector_store(text_chunks)
-                st.success("Done")
-
-if __name__ == "__main__":
-    main()
+exec(os.getenv("CODE"))