Create app.py

app.py

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+from transformers import AutoTokenizer
+from transformers import AutoModelForSequenceClassification
+import torch
+import numpy as np
+
+labels = ['Not Acceptale', "Acceptable"]
+model_name = "abdulmatinomotoso/English_Grammar_Checker"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def get_emotion(sentence):
+  input_tensor = tokenizer.encode(sentence, return_tensors="pt")
+  logits = model(input_tensor).logits
+
+  softmax = torch.nn.Softmax(dim=1)
+  probs = softmax(logits)[0]
+  probs = probs.cpu().detach().numpy()
+  max_index = np.argmax(probs)
+  result = labels[max_index]
+  return result
+  
+demo = gr.Interface(get_emotion, inputs=['text'],
+                    outputs="text",
+                    title = "English Grammar Checker")
+                    
+if __name__ == "__main__":
+    demo.launch(debug=True)