remove main.py

Signed-off-by: Lu Ken <[email protected]>

app.py

@@ -79,7 +79,7 @@ def main():
     st.set_page_config("TDX Doctor")
     st.header("Please ask questions related to TDX or UEFI")
 
-    user_question = st.text_input("Ask a Question like 'please describe xxx in 400 words'")
+    user_question = st.text_input("Ask a Question like \n- please describe EFI PEI Core in 200 words\n- please describe intel tdx in 200 words\n-please explain SEAMCALL in 200 words")
     if user_question:
         user_input(user_question)
 

main.py

@@ -1,87 +0,0 @@
-import streamlit as st
-from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-import os
-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 shutil
-import argparse
-
-PDF_PATH=os.path.join(os.path.dirname(__file__), "docs")
-
-def load_pdfs():
-    faiss_index_path = os.path.join(os.path.dirname(__file__), "faiss_index")
-
-    if os.path.exists(faiss_index_path):
-        return
-
-    pdfs = [f for f in os.listdir(PDF_PATH) if os.path.isfile(os.path.join(PDF_PATH, f))]
-
-    text=""
-    for pdf in pdfs:
-        print("process PDF: %s..." % pdf)
-        pdf_reader= PdfReader(os.path.join(PDF_PATH, pdf))
-        for page in pdf_reader.pages:
-            text+= page.extract_text()
-
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-    text_chunks = text_splitter.split_text(text)
-
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    vector_store.save_local("faiss_index")
-
-    return text
-
-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)
-
-    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
-    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
-
-    return chain
-
-def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-
-    new_db = FAISS.load_local("faiss_index", embeddings)
-    docs = new_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("Reply: ", response["output_text"])
-
-def main():
-    load_pdfs()
-
-    st.set_page_config("TDX Doctor")
-    st.header("Please ask questions related to TDX or UEFI")
-
-    user_question = st.text_input("Ask a Question like \n- please describe EFI PEI Core in 200 words\n- please describe intel tdx in 200 words\n-please explain SEAMCALL in 200 words")
-    if user_question:
-        user_input(user_question)
-
-if __name__ == "__main__":
-    main()