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
+
+
+target_list = ["Cute", "Infuriating", "Sentimental", "Empathetic",
+               "Cynical", "Depressing", "Awe-inspiring", "Patriotic", "Educational",
+               "Encouraging", "Voyeuristic", "Funny", "Sarcastic", "Dismissive", "Disparaging"]
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+
+model_name = 'abdulmatinomotoso/finetuned-distilbert-multi-label-emotion_5'
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+
+def get_probs(logits, threshold=0.5):
+    sigm = 1 / (1 + np.exp(-logits))
+    return sigm 
+
+
+def multi_label_emotions(text):
+  inputs = tokenizer(text, return_tensors="pt", truncation=True)
+  model.to(device)
+  with torch.no_grad():
+      logits = model(**inputs).logits
+      #probs = logits.int().numpy()[0]
+      log_probs = get_probs(logits)
+
+      final_log_probs = []
+      for log in log_probs:
+        final_log_probs.append(log.numpy())
+
+      final_output = []
+      for i in zip(final_log_probs[0], target_list):
+        final_output.append(i)
+
+      final_output.sort(reverse=True)
+      
+      final_dict = {}
+      for k,v in final_output:
+        final_dict[v] = float(k)
+
+      return final_dict
+
+
+demo = gr.Interface(multi_label_emotions, inputs=gr.inputs.Textbox(),
+                    outputs = gr.Label(num_top_classes=4),
+                    title='Multi-label-emotion-classification')
+
+if __name__ == '__main__':
+  demo.launch(debug=True)