Delete app3.py

app3.py

@@ -1,81 +0,0 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-import os
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from langchain_community.vectorstores import Chroma
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.chains.question_answering import load_qa_chain
-from langchain.prompts import PromptTemplate
-from langchain.chains import RetrievalQA 
-
-st.set_page_config(page_title="Document Genie", layout="wide")
-
-st.markdown("""
-## Document Genie: Get instant insights from your Documents
-
-This chatbot is built using the Retrieval-Augmented Generation (RAG) framework, leveraging Google's Generative AI model Gemini-PRO. It processes uploaded PDF documents by breaking them down into manageable chunks, creates a searchable vector store, and generates accurate answers to user queries. This advanced approach ensures high-quality, contextually relevant responses for an efficient and effective user experience.
-
-### How It Works
-
-Follow these simple steps to interact with the chatbot:
-
-1. **Upload Your Documents**: The system accepts multiple PDF files at once, analyzing the content to provide comprehensive insights.
-
-2. **Ask a Question**: After processing the documents, ask any question related to the content of your uploaded documents for a precise answer.
-""")
-
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-
-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
-    provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
-    Context:\n {context}?\n
-    Question: \n{question}\n
-
-    Answer:
-    """
-    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3, google_api_key=api_key)
-    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
-    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-    return chain
-
-
-def get_pdf(pdf_docs,query):
-    text = ""
-    for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
-        for page in pdf_reader.pages:
-            text += page.extract_text()
-
-    text_splitter = RecursiveCharacterTextSplitter(
-    # Set a really small chunk size, just to show.
-    chunk_size=500,
-    chunk_overlap=20,
-    separators=["\n\n","\n"," ",".",","])
-    chunks=text_splitter.split_text(text)
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    vector = Chroma.from_documents(chunk, embeddings)
-    #docs = vector.similarity_search(query)
-    docs = vector_store.as_retriever(search_type='similarity', search_kwargs={'k': 3}) 
-    chain = get_conversational_chain()
-    response = chain({"input_documents": docs, "question": query}, return_only_outputs=True)
-    return response
-  #st.write("Reply: ", response["output_text"])
-
-def main():
-    st.header("Chat with your pdf💁")
-
-    question = st.text_input("Ask a Question from the PDF Files", key="query")
-
-    pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True, key="pdf_uploader")
-    if question and st.button("Submit & Process", key="process_button"):
-        with st.spinner("Processing..."):
-            output = get_pdf(pdf_docs,question)
-            st.success("Done")
-            st.write("Reply: ", output["output_text"])             
-    
-
-if __name__ == "__main__":
-    main()