import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

import PyPDF2
import gradio as gr
from langchain.prompts import PromptTemplate
from pathlib import Path
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from langchain_core.output_parsers import JsonOutputParser

llm = HuggingFaceEndpoint(
    repo_id="mistralai/Mistral-7B-Instruct-v0.3",
    task="text-generation",
    max_new_tokens=4096,
    temperature=0.5,
    do_sample=False,
)
llm_engine_hf = ChatHuggingFace(llm=llm)

def read_pdf(file_path):
    pdf_reader = PyPDF2.PdfReader(file_path)
    text = ""
    for page in range(len(pdf_reader.pages)):
        text += pdf_reader.pages[page].extract_text()
    return text
    
def summarize(file, n_words):
    global llm
    # Read the content of the uploaded file
    file_path = file.name
    if file_path.endswith('.pdf'):
        text = read_pdf(file_path)
    else:
        with open(file_path, 'r', encoding='utf-8') as f:
            text = f.read()
            
    template_detect = '''
Please carefully read the following document:

<document>
{TEXT}
</document>

identify the MOST used language in the document, return detected language in json format with key "language" and value is the detected language
'''
    
    prompt_detect = PromptTemplate(
        template=template_detect,
        input_variables=['TEXT']    
    )
    
    language_detect = prompt_detect | llm | JsonOutputParser()
    formatted_prompt = prompt_detect.format(TEXT=text)
    language = language_detect.invoke(formatted_prompt)

    lang = language["language"]
    template_translate = '''
Please carefully read the following document:
<document>
{TEXT}
</document>
After reading through the document, pinpoint the key points and main ideas covered in the text. 
Organize these key points into a concise bulleted list that summarizes the essential information from the document. 
The summary should be in {LANG} language.
'''
    
    prompt_summarize = PromptTemplate(
        template=template_translate,
        input_variables=["TEXT", "LANG"]
    )
    formatted_prompt = prompt_summarize.format(TEXT=text, LANG=lang)
    summary = llm.invoke(formatted_prompt)
    
    return summary

def download_summary(output_text):
    if output_text:
        file_path = Path('summary.txt')
        with open(file_path, 'w', encoding='utf-8') as f:
            f.write(output_text)
        return file_path
    else:
        return None
def create_download_file(summary_text):
    file_path = download_summary(summary_text)
    return str(file_path) if file_path else None

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## Document Summarizer")

    with gr.Row():
        with gr.Column():
            file = gr.File(label="Submit a file")
        
        with gr.Column():
            output_text = gr.Textbox(label="Summary", lines=20)

    submit_button = gr.Button("Summarize")
    submit_button.click(summarize, inputs=[file], outputs=output_text)

    def generate_file():
        summary_text = output_text
        file_path = download_summary(summary_text)
        return file_path

    download_button = gr.Button("Download Summary")
    download_button.click(
        fn=create_download_file,
        inputs=[output_text],
        outputs=gr.File()
    )
# Run the Gradio app
demo.launch(share=True)