add additional inputs

app.py

@@ -20,16 +20,24 @@ from transformers import BertTokenizerFast, BertTokenizer
 model = GPT2LMHeadModel.from_pretrained("binxu/Ziyue-GPT2")
 generator = pipeline('text-generation', model=model, tokenizer='bert-base-chinese')
 
-def generate(prompt):
-    outputs = generator(prompt, max_length=50, num_return_sequences=5, num_beams=10, repetition_penalty=1.5)
+def generate(prompt, num_beams, max_length, repetition_penalty, seed):
+
+    if seed is not None:
+        torch.manual_seed(seed)
+
+    outputs = generator(prompt, max_length=max_length, num_return_sequences=5, num_beams=num_beams, repetition_penalty=repetition_penalty)
     output_texts = [output['generated_text'] for output in outputs]
     output_all = "\n\n".join(output_texts)
     return output_all
 
-examples = ["子曰", "子墨子曰", "孟子", "秦王", "子路问仁"]
+examples = ["子曰", "子墨子曰", "孟子", "秦王", "子路问仁", "孙行者笑道", "牛魔王与红孩儿", "鲲鹏", "宝玉道", "黛玉行至贾母处"]
 
 iface = gr.Interface(fn=generate,
-                     inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+                     inputs=[gr.inputs.Textbox(lines=2, label="Prompt"),
+                             gr.inputs.Slider(minimum=1, maximum=20, default=10, label="Number of beams"),
+                              gr.inputs.Slider(minimum=10, maximum=100, default=50, label="Max length"),
+                              gr.inputs.Slider(minimum=1, maximum=5, default=1.5, label="Repetition penalty"),
+                              gr.inputs.Number(default=0, label="Seed")],
                      outputs=gr.outputs.Textbox(label="Generated Text"),
                      examples=examples)
 iface.launch()