Create app.py

app.py

@@ -0,0 +1,16 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the tokenizer and model
+model_checkpoint = "google/mt5-small"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+
+def summarize(text):
+    inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True)
+    outputs = model.generate(inputs, max_length=150, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+iface = gr.Interface(fn=summarize, inputs="text", outputs="text", title="Text Summarizer", description="Summarize any text using the mT5 model.")
+
+iface.launch()