Upload 2 files

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+MODEL_NAME = "facebook/bart-large-cnn"
+
+# load model
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+def summarize(text):
+    if not text:
+        return "Please enter text"
+
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024).to(device)
+
+    summary_ids = model.generate(
+        inputs["input_ids"],
+        max_length=150,
+        min_length=50,
+        num_beams=4
+    )
+
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+app = gr.Interface(
+    fn=summarize,
+    inputs=gr.Textbox(lines=10, placeholder="Enter long text here..."),
+    outputs="text",
+    title="LITVISION Summarizer",
+    description="AI-powered book and text summarization"
+)
+
+app.launch()

reqirements.txt

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+transformers
+torch
+sentencepiece