Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+"""import gradio as gr
 import nltk
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
@@ -56,3 +56,65 @@ iface = gr.Interface(
 
 # 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)