Create app.py

app.py

@@ -0,0 +1,27 @@
+import gradio as gr
+import transformers
+
+model_name = "t5-small"
+tokenizer = transformers.T5Tokenizer.from_pretrained(model_name)
+model = transformers.T5ForCausalLM.from_pretrained(model_name)
+
+def summarize_text(text, max_length):
+    input_ids = tokenizer.encode(text, return_tensors='pt', max_length=512)
+    summary_ids = model.generate(input_ids, 
+                                 max_length=max_length, 
+                                 num_beams=4, 
+                                 early_stopping=True)
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+iface = gr.Interface(
+    fn=summarize_text,
+    inputs=gr.inputs.Textbox(lines=5, default="Enter your text here"),
+    outputs=gr.outputs.Textbox(lines=3, default="Summary will appear here"),
+    parameters={
+        "max_length": gr.inputs.Slider(default=50, min_value=20, max_value=200, step=10, label="Summary Length")
+    },
+    title="Text Summarization with T5",
+    description="Generate a brief summary of the input text using the T5 model."
+)
+
+iface.launch()