app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from langchain.prompts import PromptTemplate
from langchain.chains.summarize import load_summarize_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.documents import Document
from pathlib import Path

# Load the Mistral model from Hugging Face
model_name = "mistralai/Mistral-7B-Instruct-v0.3"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

# Define the text splitter and summarize chain
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

# Define the summarization function
def summarize(file, n_words):
    # Read the content of the uploaded file
    file_path = file.name
    with open(file_path, 'r', encoding='utf-8') as f:
        file_content = f.read()
    
    # Split the content into chunks
    chunks = text_splitter.create_documents([file_content])
    
    # Summarize each chunk and concatenate the results
    summaries = []
    for chunk in chunks:
        inputs = tokenizer(chunk.text, return_tensors="pt", max_length=512, truncation=True)
        summary_ids = model.generate(inputs["input_ids"], max_length=n_words, num_beams=4, early_stopping=True)
        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
        summaries.append(summary)
    
    return " ".join(summaries)

# Define the download summary function
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():
            n_words = gr.Slider(minimum=50, maximum=500, step=50, label="Number of words")
            file = gr.File(label="Submit a file")
        
        with gr.Column():
            output_text = gr.Textbox(label="Summary will be printed here", lines=20)

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

    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)