app

app.py

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+import gradio as gr
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+from huggingface_hub import hf_hub_download
+import numpy as np
+
+tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
+model = DistilBertForSequenceClassification.from_pretrained('jdmartinev/imdbreviews_classification_distilbert_v02')
+id2label = {0: "negative", 1: "positive"}
+
+def classify_text(input_text,id2label):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model(**inputs)
+    pred = np.argmax(outputs)
+    return(f"Predicted class: {id2label[pred]}")
+
+demo = gr.Interface(classify_text, "text", "text")
+demo.launch()
+
+
+

requirements.txt

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+gradio