Delete app.py

app.py

@@ -1,86 +0,0 @@
-import torch
-import re
-import pandas as pd
-from gradio import Interface, File
-import spaces
-import gradio as gr
-from PyPDF2 import PdfReader
-from transformers import AutoTokenizer, pipeline, AutoModelForSeq2SeqLM
-
-# Load the tokenizer and model
-led_tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384-multi_lexsum-source-long")
-summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", tokenizer="sshleifer/distilbart-cnn-12-6", framework="pt")
-
-# Load the model separately
-model = AutoModelForSeq2SeqLM.from_pretrained("allenai/led-base-16384-multi_lexsum-source-long")
-
-# Move the model to CUDA if available
-if torch.cuda.is_available():
-    model = model.to("cuda")
-
-# Function to clean text by keeping only alphanumeric characters and spaces
-def clean_text(text):
-    return re.sub(r'[^a-zA-Z0-9\s]', '', text)
-
-# Function to extract text from PDF files
-def extract_text(pdf_file):
-    try:
-        with open(pdf_file, 'rb') as file:
-            pdf_reader = PdfReader(file)
-            if pdf_reader.is_encrypted:
-                print(f"Skipping encrypted file: {pdf_file}")
-                return None
-            return ' '.join(page.extract_text() or '' for page in pdf_reader.pages)
-    except Exception as e:
-        print(f"Error extracting text from {pdf_file}: {e}")
-        return None
-
-# Function to classify text using LED model in batches
-def classify_texts(texts):
-    return [classifier(text)["label"] for text in texts]
-
-# Function to summarize text using the summarizer model in batches
-@spaces.GPU
-def summarize_texts(texts):
-    return [summarizer(text, max_length=100, min_length=30, do_sample=False)[0]['summary_text'] for text in texts]
-
-# Function to extract a title-like summary from the beginning of the text
-@spaces.GPU
-def extract_title(text):
-    return summarizer(text, max_length=20, min_length=5, do_sample=False)[0]['summary_text']
-
-# Function to process PDF files
-@spaces.GPU
-def process_files(pdf_files):
-    data = []
-    for pdf_file in pdf_files:
-        text = extract_text(pdf_file)
-        if text is None:
-            continue
-
-        title_text = text.split(maxsplit=512)[0]
-        title = extract_title(title_text)
-
-        # Clean the entire text at once
-        cleaned_text = clean_text(text)
-
-        data.append([title, summarize_texts([cleaned_text])[0], cleaned_text])
-
-    df = pd.DataFrame(data, columns=['Title', 'Abstract', 'Content'])
-    output_file_path = 'processed_pdfs.csv'
-    df.to_csv(output_file_path, index=False)
-    return output_file_path
-
-# Gradio interface
-pdf_input = gr.File(label="Upload PDF Files", file_types=[".pdf"], file_count="multiple")
-csv_output = gr.File(label="Download CSV")
-
-gr.Interface(
-    fn=process_files,
-    inputs=pdf_input,
-    outputs=csv_output,
-    title="Dataset creation",
-    description="Upload PDF files and get a summarized CSV file.",
-    article="""<p>This app creates a dataset from research papers using AI models.</p>
-                <p>It uses models for classification and summarization to extract titles, abstracts, and content from PDFs.</p>"""
-).launch(share=True)