created app

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("Rahmat82/DistilBERT-finetuned-on-emotion")
+model.to(device)
+
+def predict(query: str) -> dict:
+    inputs = tokenizer(query, return_tensors='pt')
+    inputs.to(device)
+    outputs = model(**inputs)
+    outputs = torch.sigmoid(outputs.logits)
+    outputs = outputs.detach().cpu().numpy()
+    label2ids = {
+        "sadness": 0,
+        "love": 2,
+        "anger": 3,
+        "fear": 4,
+        "surprise": 5,
+        "joy": 6
+    }
+
+    for i, k in enumerate(label2ids.keys()):
+        label2ids[k] = outputs[0][i]
+    label2ids = {k: float(v) for k, v in sorted(label2ids.items(), key=lambda item: item[1], reverse=True)}
+    return label2ids
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.components.Textbox(label='Input query'),
+    outputs=gr.components.Label(label='Predictions', num_top_classes=6),
+    allow_flagging='never'
+)
+
+demo.launch(share=True)