Delete app.py

app.py

@@ -1,112 +0,0 @@
-import os
-import re
-import PyPDF2
-import pandas as pd
-from transformers import pipeline, AutoTokenizer
-import gradio as gr
-import spaces
-
-# 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):
-    pdf_reader = PyPDF2.PdfReader(pdf_file)
-    text = ''
-    for page_num in range(len(pdf_reader.pages)):
-        text += pdf_reader.pages[page_num].extract_text()
-    return text
-
-# Function to split text into chunks of a specified size
-def split_text(text, chunk_size=1024):
-    words = text.split()
-    for i in range(0, len(words), chunk_size):
-        yield ' '.join(words[i:i + chunk_size])
-
-# Load the LED tokenizer
-led_tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384-multi_lexsum-source-long")
-
-# Function to classify text using LED model
-@spaces.GPU(duration=120)
-def classify_text(text):
-    classifier = pipeline("text-classification", model="allenai/led-base-16384-multi_lexsum-source-long", tokenizer=led_tokenizer, framework="pt")
-    try:
-        return classifier(text)[0]['label']
-    except IndexError:
-        return "Unable to classify"
-
-# Function to summarize text using BGE-m3 model
-@spaces.GPU(duration=120)
-def summarize_text(text, max_length=100, min_length=30):
-    summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", tokenizer="sshleifer/distilbart-cnn-12-6", framework="pt")
-    try:
-        return summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)[0]['summary_text']
-    except IndexError:
-        return "Unable to summarize"
-
-# Function to extract a title-like summary from the beginning of the text
-@spaces.GPU(duration=120)
-def extract_title(text, max_length=20):
-    summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", tokenizer="sshleifer/distilbart-cnn-12-6", framework="pt")
-    try:
-        return summarizer(text, max_length=max_length, min_length=5, do_sample=False)[0]['summary_text']
-    except IndexError:
-        return "Unable to extract title"
-
-# Function to process PDF files and generate summaries
-@spaces.GPU(duration=120)
-def process_pdfs(pdf_files):
-    data = []
-
-    for pdf_file in pdf_files:
-        text = extract_text(pdf_file)
-        
-        # Extract a title from the beginning of the text
-        title_text = ' '.join(text.split()[:512])  # Take the first 512 tokens for title extraction
-        title = extract_title(title_text)
-        
-        # Initialize placeholders for combined results
-        combined_abstract = []
-        combined_cleaned_text = []
-
-        # Split text into chunks and process each chunk
-        for chunk in split_text(text, chunk_size=512):
-            # Summarize the text chunk
-            abstract = summarize_text(chunk)
-            combined_abstract.append(abstract)
-            
-            # Clean the text chunk
-            cleaned_text = clean_text(chunk)
-            combined_cleaned_text.append(cleaned_text)
-
-        # Combine results from all chunks
-        final_abstract = ' '.join(combined_abstract)
-        final_cleaned_text = ' '.join(combined_cleaned_text)
-        
-        # Append the data to the list
-        data.append([title, final_abstract, final_cleaned_text])
-
-    # Create a DataFrame from the data list
-    df = pd.DataFrame(data, columns=['Title', 'Abstract', 'Content'])
-
-    # Save the DataFrame to a CSV file in the same folder as the source folder
-    csv_file_path = 'processed_pdfs.csv'
-    df.to_csv(csv_file_path, index=False)
-
-    return csv_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_pdfs, 
-    inputs=pdf_input, 
-    outputs=csv_output,
-    title="Dataset creation",
-    description="Upload PDF files and get a summarized CSV file.",
-    article="""<p>This is an experimental app that allows you to create a dataset from research papers.</p>
-                <p>This app uses the allenai/led-base-16384-multi_lexsum-source-long and sshleifer/distilbart-cnn-12-6 AI models.</p>
-                <p>The output file is a CSV with 3 columns: title, abstract, and content.</p>"""
-).launch(share=True)