updated app.py #1

app.py

@@ -1,12 +1,43 @@
 import gradio as gr
+import torch
 
-title = "Nepali News Summarization"
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
+# Predict with test data (first 5 rows)
 
-demo = gr.load(
-    "GenzNepal/mt5-summarize-nepalil",
+model_ckpt = "GenzNepal/mt5-summarize-nepali"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+t5_tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
+
+model = AutoModelForSeq2SeqLM.from_pretrained(model_ckpt).to(device)
+
+
+
+def summarize(text):
+    inputs = t5_tokenizer(text, return_tensors="pt", max_length=1024, padding= "max_length", truncation=True, add_special_tokens=True)
+    generation = model.generate(
+      input_ids = inputs['input_ids'].to(device),
+      attention_mask=inputs['attention_mask'].to(device),
+      num_beams=6,
+      num_return_sequences=1,
+      no_repeat_ngram_size=2,
+      repetition_penalty=1.0,
+      min_length=100,
+      max_length=250,
+      length_penalty=2.0,
+      early_stopping=True
+    )
+    # # Convert id tokens to text
+    output = t5_tokenizer.decode(generation[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    return output
+
+
+demo = gr.interface(
+    summarize,
     inputs=gr.Textbox(lines=5, max_lines=20, label="Input Text"),
-    title=title,
+    outputs="Summarization"
 )
 
 if __name__ == "__main__":