Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from datasets import load_dataset
+import torch
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+SAVED_MODEL_PATH = './bart_base_full_finetune_save'
+model_name = "facebook/bart-base"
+model = AutoModelForSeq2SeqLM.from_pretrained(SAVED_MODEL_PATH).to(device)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+dataset = load_dataset("samsum")
+
+train_data = dataset["train"]
+validation_data = dataset["validation"] 
+test_data = dataset["test"]
+
+def summarize(tokenizer, model, text):
+    inputs = tokenizer(f"Summarize dialogue >>\n {text}", return_tensors="pt", max_length=1000, truncation=True, padding="max_length").to(device)
+    summary_ids = model.generate(inputs.input_ids, num_beams=4, max_length=100, early_stopping=True)
+    summary = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids]
+    return summary[0]
+
+def summarize_dialogue(text):
+    return summarize(tokenizer, model, text)
+
+iface = gr.Interface(
+    fn=summarize_dialogue,
+    inputs=gr.inputs.Textbox(lines=10, label="Input Dialogue"),
+    outputs=gr.outputs.Textbox(label="Generated Summary")
+)
+
+iface.launch()