Create app.py

app.py

@@ -0,0 +1,20 @@
+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")
+
+iface = gr.Interface(fn=predict, inputs='text', outputs='text')
+iface.launch()