Create app.py

app.py

@@ -0,0 +1,39 @@
+# app.py
+
+from transformers import pipeline
+import gradio as gr
+
+# Load the summarization pipeline
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Define summarization function
+def summarize_text(text):
+    if not text or len(text.strip()) == 0:
+        return "⚠️ Please enter some text to summarize."
+
+    summary = summarizer(
+        text,
+        max_length=130,
+        min_length=30,
+        do_sample=False
+    )
+    return summary[0]['summary_text']
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=summarize_text,
+    inputs=gr.Textbox(
+        lines=12,
+        placeholder="✍️ Paste your article, paragraph, or research text here..."
+    ),
+    outputs=gr.Textbox(label="🧠 Generated Summary"),
+    title="Text Summarizer using Hugging Face 🤗",
+    description="Enter any paragraph or document, and get a concise summary using the BART model.",
+    examples=[
+        ["The Hugging Face Transformers library provides general-purpose architectures for NLP tasks such as text classification, information extraction, question answering, summarization, translation, and text generation. It allows easy use of pre-trained models and fine-tuning for custom datasets."]
+    ]
+)
+
+# Launch app
+if __name__ == "__main__":
+    demo.launch()