Update app.py

app.py

@@ -2,11 +2,25 @@ import gradio as gr
 from transformers import AutoModelForCausalLM
 from transformers import BloomTokenizerFast
 from transformers import pipeline, set_seed
+import random
 
 model_name = "bloom-560m"
 model = AutoModelForCausalLM.from_pretrained(f"jslin09/{model_name}-finetuned-fraud")
 tokenizer = BloomTokenizerFast.from_pretrained(f'bigscience/{model_name}', bos_token = '<s>', eos_token = '</s>')
 
+def rnd_generate(prompt):
+    rnd_seed = random.randint(10, 500)
+    set_seed(rnd_seed)
+    inputs = tokenizer(prompt, return_tensors="pt") # 回傳的張量使用 Pytorch的格式。如果是 Tensorflow 格式的話，則指定為 "tf"。
+    results = model.generate(inputs["input_ids"],
+                       max_length=500,
+                       num_return_sequences=1,
+                       do_sample=True,
+                       temperature=0.75,
+                       top_k=50, 
+                       top_p=0.9)
+    return tokenizer.decode(results[0])
+
 def generate(prompt):
     result_length = len(prompt) + 4
     inputs = tokenizer(prompt, return_tensors="pt") # 回傳的張量使用 Pytorch的格式。如果是 Tensorflow 格式的話，則指定為 "tf"。
@@ -39,6 +53,11 @@ with gr.Blocks() as demo:
             prompt.change(generate, inputs=[prompt], outputs=[result])
             btn = gr.Button("Next sentence")
             btn.click(generate, inputs=[result], outputs=[result])
+        with gr.Column():
+            result2 = gr.components.Textbox(lines=7, label="生成草稿", show_label=True, placeholder=examples[0])
+            gr.Examples(examples, label='例句', inputs=[result2])
+            btn = gr.Button("隨機生成草稿")
+            btn.click(rnd_generate, inputs=[result2], outputs=[result2])
     
 if __name__ == "__main__":
     demo.launch()