delete

gr_api.py

@@ -1,35 +0,0 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch.nn as nn
-
-name = ["negative","neutral","positive"]
-
-
-def main_note(sentence,aspect):
-    tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-base-absa-v1.1")
-    model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-base-absa-v1.1")
-    # model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-large-absa-v1.1")
-    input_str = "[CLS]" + sentence + "[SEP]" + aspect + "[SEP]"
-    # input_str = "[CLS] when tables opened up, the manager sat another party before us. [SEP] manager [SEP]"
-    inputs = tokenizer(input_str, return_tensors="pt")
-    outputs = model(**inputs)
-    softmax = nn.Softmax(dim=1)
-    outputs = softmax(outputs.logits)
-    result = [round(i,4) for i in outputs.tolist()[0]]
-    # print(result)
-    return dict(zip(name,result))
-
-# main_note("","")
-
-iface = gr.Interface(
-    fn = main_note,
-    inputs=["text","text"],
-    outputs = gr.outputs.Label(),
-    examples=[["1.) Instead of being at the back of the oven, the cord is attached at the front right side.","cord"],
-    ["The pan I received was not in the same league as my old pan, new is cheap feeling and does not have a plate on the bottom.","pan"],
-    ["The pan I received was not in the same league as my old pan, new is cheap feeling and does not have a plate on the bottom.","bottom"],
-    ["They seem much more durable and less prone to staining, retaining their white properties for a much longer period of time.","durability"],
-    ["It took some time to clean and maintain, but totally worth it!","clean"],
-    ["this means that not only will the smallest burner heat up the pan, but it will also vertically heat up 1\" of the handle.","handle"]])
-
-iface.launch(share=True)