Create app.py

app.py

@@ -0,0 +1,23 @@
+import gradio as gr
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "t5-base"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define pipeline for text summarization
+summarizer = pipeline('text2text-generation', model=model, tokenizer=tokenizer)
+
+# Define Gradio interface
+def summarize_text(text):
+    result = summarizer(text, max_length=100, min_length=30, do_sample=False)[0]
+    summary = result['generated_text'].strip()
+    return summary
+
+iface = gr.Interface(fn=summarize_text, inputs="text", outputs="text", 
+                     title="Text Summarization with Hugging Face and Gradio", 
+                     description="Enter text to summarize.")
+
+# Launch Gradio interface
+iface.launch()