inital

distilgpt2/app.py

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+import gradio as gr
+from transformers import GPT2Tokenizer, GPT2Model
+from transformers import pipeline, set_seed
+
+def inference(text):
+    model_path = "distilgpt2"
+    generator = pipeline('text-generation', model=model_path)
+    set_seed(42)
+
+    output=[]
+    lst=generator(text, max_length=20, num_return_sequences=5)
+    for dic in lst:
+        output.append(dic['generated_text'])
+
+    return output
+
+    # tokenizer = GPT2Tokenizer.from_pretrained(model_path)
+    # model = GPT2Model.from_pretrained(model_path)
+    # encoded_input = tokenizer(text, return_tensors='pt')
+    # output = model(**encoded_input)
+    # print(output)
+    # return output
+
+
+examples=[["Hello, I’m a language model."]]
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+    """
+    # Text generation:distilgpt2
+    Gradio Demo for distilgpt2. To use it, simply type in text, or click one of the examples to load them.
+    """)
+    with gr.Row():
+        text_input = gr.Textbox()
+        text_output = gr.Textbox()
+    image_button = gr.Button("上传")
+    image_button.click(inference, inputs=text_input, outputs=text_output)
+    gr.Examples(examples,inputs=text_input)
+
+demo.launch()

distilgpt2/distilgpt2

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+Subproject commit f241065e938b44ac52db2c5de82c8bd2fafc76d0