Create app.py

app.py

@@ -0,0 +1,32 @@
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+
+
+model = AutoModelForSeq2SeqLM.from_pretrained("PRAli22/flan-t5-base-imdb-text-classification")
+tokenizer = AutoTokenizer.from_pretrained("PRAli22/flan-t5-base-imdb-text-classification")
+
+
+
+def summarize(text):
+    inputs = tokenizer.encode_plus(text, padding='max_length', max_length=512, return_tensors='pt')
+    summarized_ids = model.generate(inputs['input_ids'], attention_mask=inputs['attention_mask'], 
+                                    max_length=150, num_beams=4, early_stopping=True)
+
+    return " ".join([tokenizer.decode(token_ids, skip_special_tokens=True)
+                    for token_ids in summarized_ids])
+
+
+css_code='body{background-image:url("https://media.istockphoto.com/id/1256252051/vector/people-using-online-translation-app.jpg?s=612x612&w=0&k=20&c=aa6ykHXnSwqKu31fFR6r6Y1bYMS5FMAU9yHqwwylA94=");}'
+
+demo = gr.Interface(
+    fn=summarize,
+    inputs=
+        gr.Textbox(label="text", placeholder="Enter the text "),
+    
+    outputs=gr.Textbox(label="summary"),
+    title="Text Summarizer",
+    description= "This is  Text Summarizer System, it takes a text  in English as inputs and returns it's summary",
+    css = css_code
+)
+
+demo.launch()