Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import numpy as np
+import torch
+import gradio as gr
+labels = ['sadness', 'joy','love', 'anger','fear', 'surprise']
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model_name = "abdulmatinomotoso/emotion_detection_finetuned_distilbert"
+model = AutoModelForSequenceClassification.from_pretrained(model_name).to(device)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+
+def get_emotion(text):
+  input_tensor = tokenizer.encode(text, return_tensors="pt").to("cuda")
+  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)
+  emotion = labels[max_index]
+  return emotion
+  
+demo = gr.Interface(get_emotion, inputs='text',
+                    outputs="text",
+                    title = "Emotion Detection")
+                    
+if __name__ == "__main__":
+    demo.launch(debug=True)