Update app.py

app.py

@@ -131,36 +131,31 @@ model = torch.load("./model.pt", map_location=torch.device('cpu'))
 
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 
-text = "Bitcoin to the moon"
+def predict(text):
 
-encoding = tokenizer(text, return_tensors="pt")
-encoding = {k: v.to(trainer.model.device) for k,v in encoding.items()}
+    encoding = tokenizer(text, return_tensors="pt")
+    encoding = {k: v.to(trainer.model.device) for k,v in encoding.items()}
 
-outputs = trainer.model(**encoding)
+    outputs = trainer.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]
-console.log("a")
-console.log(predicted_labels)
-console.log("a")
+    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
+    return([id2label[idx] for idx, label in enumerate(predictions) if label == 1.0])
 
 
 
-inp = [gr.Textbox(label='API Key', placeholder="What is your API Key?"), gr.Textbox(label='Audio File URL', placeholder="Audio file URL?")]
+inp = [gr.Textbox(label='Text or tweet text', placeholder="Insert text")]
 out = gr.Textbox(label='Output')
 text_button = gr.Button("Flip")
-text_button.click(audio_to_text, inputs=inp, outputs=out)
+text_button.click(predict, inputs=inp, outputs=out)
 
 interface = gr.Interface.load(input=inp,output=out,
 title = title,