test_remote

app.py

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+# import gradio as gr
+# from transformers import AutoTokenizer
+
+# # 第一个功能：基于输入文本和对应的损失值对文本进行着色展示
+# def color_text(text_list=["hi", "FreshEval"], loss_list=[0.1,0.7]):
+#     """
+#     根据损失值为文本着色。
+#     """
+#     highlighted_text = []
+#     for text, loss in zip(text_list, loss_list):
+#         # color = "#FF0000" if float(loss) > 0.5 else "#00FF00"
+#         color=loss
+#         highlighted_text.append({"text": text, "bg_color": color})
+#     return gr.HighlightedText(highlighted_text).get_html()
+
+# # 第二个功能：根据 ID 列表和 tokenizer 将 ID 转换为文本，并展示
+# def get_text(ids_list=[0.1,0.7], tokenizer=None):
+#     """
+#     给定一个 ID 列表和 tokenizer 名称，将这些 ID 转换成文本。
+#     """
+#     return ['Hi', 'Adam']
+#     # tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+#     # text = tokenizer.decode(eval(ids_list), skip_special_tokens=True)
+#     # 这里只是简单地返回文本，但是可以根据实际需求添加颜色或其他样式
+#     # return text
+
+
+# def get_ids_loss(text, tokenizer, model):
+#     """
+#     给定一个文本，返回其对应的 IDs 和损失值。
+#     """
+#     # tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+#     # model = AutoModelForCausalLM.from_pretrained(model_name)
+#     # 这里只是简单地返回 IDs 和损失值，但是可以根据实际需求添加颜色或其他样式
+#     return [1, 2], [0.1, 0.7]
+
+
+# def color_pipeline(text=["hi", "FreshEval"],  model=None):
+#     """
+#     给定一个文本，返回其对应的着色文本。
+#     """
+#     tokenizer=None
+#     ids, loss = get_ids_loss(text, tokenizer, model)
+#     text = get_text(ids, tokenizer)
+#     return color_text(text, loss)
+
+# # 创建 Gradio 界面
+# with gr.Blocks() as demo:
+#     with gr.Tab("color your text"):
+#         with gr.Row():
+#             text_input = gr.Textbox(label="input text", placeholder="input your text here...")
+#             # loss_input = gr.Number(label="loss")
+#             model_input = gr.Textbox(label="model name", placeholder="input your model name here...")
+#             color_text_output = gr.HTML(label="colored text")
+#             gr.Markdown("## Text Examples")
+#             # gr.Examples(
+#             #     [["hi", "Adam"], [0.1,0.7]],
+#             #     [text_input, loss_input],
+#             #     cache_examples=True,
+#             #     fn=color_text,
+#             #     outputs=color_text_output
+#             # )
+#         color_text_button = gr.Button("color the text").click(color_pipeline, inputs=[text_input, model_input], outputs=color_text_output)
+
+
+#         date_time_input = gr.Textbox(label="the date when the text is generated")#TODO add date time input
+#         description_input = gr.Textbox(label="description of the text")
+#         submit_button = gr.Button("submit a post or record")
+#         #TODO add model and its score
+
+    
+#     # with gr.Tab("ID 转文本展示"):
+#     #     with gr.Row():
+#     #         ids_input = gr.Textbox(label="输入 IDs (如 [101, 102, ...])")
+#     #         tokenizer_input = gr.Textbox(label="Tokenizer 名称", value="bert-base-uncased")
+#     #         show_text_output = gr.Textbox(label="转换后的文本")
+#     #     show_text_button = gr.Button("转换并展示").click(show_text, inputs=[ids_input, tokenizer_input], outputs=show_text_output)
+    
+#     with gr.Tab("model ppl with time"):
+#         '''
+#         see the matplotlib example, to see ppl with time, select the models
+#         '''
+
+    
+#     with gr.Tab("model ppl with time"):
+#         '''
+#         see the matplotlib example, to see ppl with time, select the models
+#         ''' 
+
+        
+
+# demo.launch()
+
+
+
+# import gradio as gr
+# from transformers import pipeline
+
+
+# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+# def predict(input_img):
+#     predictions = pipeline(input_img)
+#     return input_img, {p["label"]: p["score"] for p in predictions} 
+
+# gradio_app = gr.Interface(
+#     predict,
+#     inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
+#     outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+#     title="Hot Dog? Or Not?",
+# )
+
+# if __name__ == "__main__":
+#     gradio_app.launch()
+
+
+
+import gradio as gr
+
+def greet(name, intensity):
+    return "Hello, " + name + "!" * int(intensity)
+
+demo = gr.Interface(
+    fn=greet,
+    inputs=["text", "slider"],
+    outputs=["text"],
+)
+
+demo.launch(debug=True)

requirements.txt

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+lm-evaluation-harness
+transformers
+torch