Update app.py

app.py

@@ -1,126 +1,3 @@
-"""import gradio as gr
-import nltk
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-nltk.download('punkt')
-
-def fragment_text(text, tokenizer):
-    sentences = nltk.tokenize.sent_tokenize(text)
-    max_len = tokenizer.max_len_single_sentence
-
-    chunks = []
-    chunk = ""
-    count = -1
-
-    for sentence in sentences:
-        count += 1
-        combined_length = len(tokenizer.tokenize(sentence)) + len(chunk)
-
-        if combined_length <= max_len:
-            chunk += sentence + " "
-        else:
-            chunks.append(chunk.strip())
-            chunk = sentence + " "
-
-    if chunk != "":
-        chunks.append(chunk.strip())
-
-    return chunks
-
-
-def summarize_text(text, tokenizer, model):
-    chunks = fragment_text(text, tokenizer)
-
-    summaries = []
-    for chunk in chunks:
-        input = tokenizer(chunk, return_tensors='pt')
-        output = model.generate(**input)
-        summary = tokenizer.decode(*output, skip_special_tokens=True)
-        summaries.append(summary)
-
-    final_summary = " ".join(summaries)
-    return final_summary
-
-# Load pre-trained model and tokenizer
-checkpoint = "tclopess/bart_samsum"
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
-
-# Define Gradio Interface
-iface = gr.Interface(
-    fn=summarize_text,
-    inputs=gr.Textbox(),
-    outputs=gr.Textbox(),
-    live=True
-)
-
-# Launch the Gradio Interface
-iface.launch()
-
-
-import gradio as gr
-import nltk
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-nltk.download('punkt')
-
-def fragment_text(text, tokenizer):
-    sentences = nltk.tokenize.sent_tokenize(text)
-    max_len = tokenizer.max_len_single_sentence
-
-    chunks = []
-    chunk = ""
-    count = -1
-
-    for sentence in sentences:
-        count += 1
-        combined_length = len(tokenizer.tokenize(sentence)) + len(chunk)
-
-        if combined_length <= max_len:
-            chunk += sentence + " "
-        else:
-            chunks.append(chunk.strip())
-            chunk = sentence + " "
-
-    if chunk != "":
-        chunks.append(chunk.strip())
-
-    return chunks
-
-
-def summarize_text(text, tokenizer, model):
-    chunks = fragment_text(text, tokenizer)
-
-    summaries = []
-    for chunk in chunks:
-        input = tokenizer(chunk, return_tensors='pt')
-        output = model.generate(**input)
-        summary = tokenizer.decode(*output, skip_special_tokens=True)
-        summaries.append(summary)
-
-    final_summary = " ".join(summaries)
-    return final_summary
-
-checkpoint = "tclopess/bart_samsum"
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
-
-def summarize_and_display(text):
-    summary = summarize_text(text, tokenizer, model)
-    return summary
-
-iface = gr.Interface(
-    fn=summarize_and_display,
-    inputs=gr.Textbox(label="Enter text to summarize:"),
-    outputs=gr.Textbox(label="Summary:"),
-    live=True,
-    title="Text Summarizer with Button",
-    description="Click the 'Summarize' button to generate a summary of the text.",
-)
-
-iface.launch(share=True)
-""""
-
 import gradio as gr
 import nltk
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM