Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+labels = labels = ['Comment (Expert / Leadership)', 'Personal News','Event Participation', 'Obituary', 'Award / Recognition', 'Company achievement',
+          'Financial Insight of stockholding', 'Job Updates', 'Philanthropy', 'Negative News', 'Achievement / Highlighting']
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+
+model_name = 'abdulmatinomotoso/finetuned-distilbert-shidhant-emotion-article-categorization'
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def get_category(text):
+  #text = read_in_text(file.name)
+
+  input_tensor = tokenizer.encode(text, return_tensors='pt', truncation=True)
+  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)
+  sentiment = labels[max_index]
+  return sentiment
+
+demo = gr.Interface(get_category, inputs=gr.inputs.Textbox(),
+                    outputs = 'text',
+                    title='Articles emotion Categorization')
+
+if __name__ == '__main__':
+  demo.launch(debug=True)