import gradio as gr
from langchain_community.llms import GooglePalm
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import GooglePalmEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
from secret1 import GOOGLE_API as google_api
import PyPDF2
# def chatbot_response(user_input, history):
#     # This is a placeholder function. Replace with your actual chatbot logic.
#     bot_response = "You said: " + user_input
#     history.append((user_input, bot_response))
#     return history, history

def text_splitter_function(text):
    text_splitter = CharacterTextSplitter(
        separator = '\n',
        chunk_size = 1000,
        chunk_overlap = 40,
        length_function = len,
    )
    texts = text_splitter.split_text(text)
    return texts;


def helper(text_splitter):
    db = FAISS.from_texts(text_splitter, embeddings);
    return 'hi';
    
def text_extract(file):
    pdf_reader = PyPDF2.PdfReader(file.name)
    # Get the number of pages
    num_pages = len(pdf_reader.pages)
    # Extract text from each page
    text = ""
    for page_num in range(num_pages):
        page = pdf_reader.pages[page_num]
        text += page.extract_text()
    text_splitter=text_splitter_function(text);
    
    result=helper(text_splitter);
    return result
# db = FAISS.from_texts(text_splitter, embeddings);
#     retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2})
#     llm=GooglePalm(google_api_key=google_api)
#     qa = RetrievalQA.from_chain_type(
#             llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True
#         )
#     result=qa.invoke("where is tajmahal")

    
    
with gr.Blocks() as demo:
    gr.Markdown("# Chat with ChatGPT-like Interface")

    chatbot = gr.Chatbot()
    state = gr.State([])
    # with gr.Row():
    #     with gr.Column():
    #         user_input = gr.Textbox(show_label=False, placeholder="Type your message here...")
    #         send_btn = gr.Button("Send")
    #     with gr.Column():
    #         input_file=gr.File(label="Upload PDF", file_count="single")
    #         submit_btn=gr.Button("Submit")
    # submit_btn.click(text_extract, [input_file], [user_input])
    #send_btn.click(chatbot_response,[user_input,state],[chatbot, state])

if __name__ == "__main__":
    embeddings=GooglePalmEmbeddings(google_api_key=google_api)
    demo.launch()