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ai/app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+
+# Load summarizer
+device = 0 if torch.cuda.is_available() else -1
+summarizer = pipeline(
+    "summarization",
+    model="csebuetnlp/mT5_multilingual_XLSum",
+    tokenizer="csebuetnlp/mT5_multilingual_XLSum",
+    device=device
+)
+
+print("✅ Model loaded on:", "GPU" if device == 0 else "CPU")
+
+# Function for API and UI
+def summarize_text(text):
+    if not text.strip():
+        return "❌ Error: No text provided."
+    
+    max_len = 1000
+    clean_text = text.strip()[:max_len]
+    result = summarizer([clean_text], max_length=130, min_length=30, do_sample=False)
+    return result[0]["summary_text"]
+
+# Gradio Interface (UI + API)
+iface = gr.Interface(
+    fn=summarize_text,
+    inputs=gr.Textbox(lines=10, placeholder="Paste your news article here..."),
+    outputs="text",
+    title="Multilingual News Summarizer",
+    description="Summarizes news articles using mT5 multilingual XLSum model."
+)
+
+iface.launch()

ai/requirements.txt

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+transformers
+sentencepiece
+gradio
+torch