update

app.py

@@ -1,7 +1,42 @@
 import gradio as gr
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+tokenizer = GPT2Tokenizer.from_pretrained('NlpHUST/gpt2-vietnamese')
+model = GPT2LMHeadModel.from_pretrained('NlpHUST/gpt2-vietnamese')
+# max_length = 100
+
+
+def run(text, intensity):
+    res="Tham khảo NlpHUST model  \n  \n  \n"
+    max_length=intensity
+        
+    input_ids = tokenizer.encode(text, return_tensors='pt')
+    sample_outputs = model.generate(input_ids,pad_token_id=tokenizer.eos_token_id,
+                                   do_sample=True,
+                                   max_length=max_length,
+                                   min_length=5,
+                                   top_k=40,
+                                   num_beams=5,
+                                   early_stopping=True,
+                                   no_repeat_ngram_size=2,
+                                   num_return_sequences=2)
+    
+    for i, sample_output in enumerate(sample_outputs):
+        res +="Mẫu số {}\n \n{}".format(i+1, tokenizer.decode(sample_output.tolist()))
+        res +='\n \n \n \n'
+    return res
+
+# demo = gr.Interface(
+#     fn=run,
+#     inputs=["text", "slider"],
+#     outputs=["text"],
+# )
+
+demo = gr.Interface(fn=run,
+                    inputs=[gr.Textbox(label="Nhập vào nội dung input",value="Con đường xưa em đi"),gr.Slider(label="Độ dài output muốn tạo ra", value=20, minimum=10, maximum=100, step=2)],
+                    outputs=gr.Textbox(label="Output"),  # <-- Number of output components: 1
+                    )
+
 demo.launch()