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app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the summarization model
+summarizer = pipeline("summarization", model="AventIQ-AI/t5-text-summarizer")
+
+# Define the summarization function
+def summarize_text(input_text):
+    summary = summarizer(input_text, max_length=150, min_length=30, do_sample=False)
+    return summary[0]['summary_text']
+
+# Create the Gradio UI
+iface = gr.Interface(
+    fn=summarize_text,
+    inputs=gr.Textbox(lines=5, placeholder="Enter text to summarize..."),
+    outputs="text",
+    title="T5 Text Summarizer",
+    description="Enter a passage, and the T5 model will generate a concise summary."
+)
+
+# Launch the app
+iface.launch()

requirements.txt

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+gradio
+transformers
+torch