Update app.py

app.py

@@ -9,11 +9,8 @@ model = GPT2LMHeadModel.from_pretrained('NlpHUST/gpt2-vietnamese')
 
 
 def run(text, intensity):
-    res="NlpHUST model  \n"
-    if (intensity>=10):
-        max_length=intensity
-    else:
-        max_length=10
+    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,
@@ -27,18 +24,22 @@ def run(text, intensity):
                                    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'
+        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=["text", "slider"],
+#     outputs=["text"],
+# )
 
-demo.launch()
+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()