add new

app.py

@@ -0,0 +1,35 @@
+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)