Initial version

app.py

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+import torch
+import numpy as np
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("juliensimon/autonlp-imdb-sentiment-analysis-15862661")
+model = AutoModelForSequenceClassification.from_pretrained("juliensimon/autonlp-imdb-sentiment-analysis-15862661")
+
+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 review is {:.2f}% {}".format(100*score, "negative" if index==0 else "positive")
+
+iface = gr.Interface(fn=predict, inputs="text", outputs="text")
+iface.launch()

requirements.txt

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+torch
+transformers