Delete app.py

app.py

@@ -1,29 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-id2label = {0: 'anger', 1: 'anticipation', 2: 'disgust', 3: 'fear', 4: 'joy', 5: 'love', 6: 'optimism', 7: 'pessimism', 8: 'sadness', 9: 'surprise', 10: 'trust'}
-tokenizer = AutoTokenizer.from_pretrained("winain7788/bert-finetuned-sem_eval-english")
-model = AutoModelForSequenceClassification.from_pretrained("winain7788/bert-finetuned-sem_eval-english")
-
-async def get_sentiment(text):
-    encoding = tokenizer(text, return_tensors="pt")
-    encoding = {k: v.to(model.device) for k,v in encoding.items()}
-
-    outputs = model(**encoding)
-    logits = outputs.logits
-    logits.shape
-    # apply sigmoid + threshold
-    sigmoid = torch.nn.Sigmoid()
-    probs = sigmoid(logits.squeeze().cpu())
-    predictions = np.zeros(probs.shape)
-    predictions[np.where(probs >= 0.5)] = 1
-
-    # turn predicted id's into actual label names
-    predicted_labels = [id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]
-    return predicted_labels
-
-demo = gr.Interface(fn=get_sentiment, inputs="text", outputs="json")
-
-demo.launch()