initial import

Signed-off-by: Lu Ken <ken.lu@intel.com>

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
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 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.pdf filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,87 @@
+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 'please describe xxx in 400 words'")
+    if user_question:
+        user_input(user_question)
+
+if __name__ == "__main__":
+    main()

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requirements.txt

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+streamlit
+google-generativeai
+python-dotenv
+langchain
+PyPDF2
+chromadb
+faiss-cpu
+langchain_google_genai