Initial Version

app.py

@@ -1,15 +1,17 @@
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+from transformers import pipeline
 
-# Load the model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("Waris01/google-t5-finetuning-text-summarization")
-model = AutoModelForSeq2SeqLM.from_pretrained("Waris01/google-t5-finetuning-text-summarization")
+pipe = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# Input text for summarization
-text = "Printed Round Neck Cotton Oversize Half sleeves Co-ord set Tracksuit For Men-P-MACK52845"
 
-# Tokenize and generate summary
-inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
-summary_ids = model.generate(inputs["input_ids"], max_length=65, min_length=5, early_stopping=True)
-summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+def process(text):
+    return pipe(text, max_length=65, min_length=10, do_sample=False, clean_up_tokenization_spaces=True, truncation=True)
+
+demo = gr.Interface(
+    fn=process, 
+    inputs=gr.Textbox(label="Input Text"), 
+    outputs="json"
+)
+
+demo.launch(share=True)
 
-print(summary)