Update app.py

app.py

@@ -1,20 +1,5 @@
-import torch
-import numpy as np
-import gradio as gr
-
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-tokenizer = AutoTokenizer.from_pretrained("juliensimon/autonlp-imdb-demo-hf-16622775")
-model = AutoModelForSequenceClassification.from_pretrained("juliensimon/autonlp-imdb-demo-hf-16622775")
-
-def predict(review):
-    inputs = tokenizer(review, padding=True, truncation=True, return_tensors="pt")
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    predictions = predictions.detach().numpy()[0]
-    index = np.argmax(predictions)
-    score = predictions[index]
-    return "This revied os {:.3f}% {}".format(100*score, "negative" if index == 0 else "positive")
+def predict(x):
+    return x
 
 iface = gr.Interface(fn=predict, inputs='text', outputs='text')
 iface.launch()