Add UI (app.py, requirements, README)

app.py

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+
+import os, torch, gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Use Space env var MODEL_ID if set; otherwise fall back to default below
+MODEL_ID = os.getenv("MODEL_ID", "Shahzeb99/Article_Summarizer")
+HF_TOKEN = os.getenv("HF_TOKEN")  # add as a secret if model is private
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True, token=HF_TOKEN)
+model = AutoModelForSeq2SeqLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.float16 if torch.cuda.is_available() else None,
+    device_map="auto" if torch.cuda.is_available() else None,
+    token=HF_TOKEN
+).to(device)
+
+def summarize_fn(text, max_new_tokens, min_new_tokens, num_beams, length_penalty):
+    if not text or not text.strip():
+        return ""
+    enc = tokenizer("highlights: " + text.strip(), return_tensors="pt", truncation=True, max_length=512)
+    enc = {k: v.to(model.device) for k, v in enc.items()}
+    with torch.no_grad():
+        out = model.generate(
+            **enc,
+            max_new_tokens=int(max_new_tokens),
+            min_new_tokens=int(min_new_tokens),
+            num_beams=int(num_beams),
+            length_penalty=float(length_penalty),
+            early_stopping=True,
+            no_repeat_ngram_size=3
+        )
+    return tokenizer.decode(out[0], skip_special_tokens=True)
+
+with gr.Blocks(title="Article → Highlights") as demo:
+    tip = "" if "Shahzeb99/Article_Summarizer" not in MODEL_ID else "⚠️ Set MODEL_ID in Space settings or edit app.py."
+    gr.Markdown("## Article → Highlights\n" + tip)
+    inp = gr.Textbox(lines=12, label="Article")
+    max_new = gr.Slider(32, 512, value=150, step=8, label="Max new tokens")
+    min_new = gr.Slider(8, 200, value=40, step=4, label="Min new tokens")
+    beams = gr.Slider(1, 8, value=4, step=1, label="Beam size")
+    lp = gr.Slider(0.2, 3.0, value=2.0, step=0.1, label="Length penalty")
+    btn = gr.Button("Generate")
+    out = gr.Textbox(lines=8, label="Highlights")
+    btn.click(summarize_fn, [inp, max_new, min_new, beams, lp], out)
+
+demo.queue(concurrency_count=2, max_size=10)
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers>=4.42.0
+torch>=2.3.0
+accelerate>=0.30.0
+gradio>=4.38.1
+sentencepiece