create app.py

app.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+
+sample_text = [
+    [
+        "Poems on various subjects. Whereto is prefixed a short essay on the structure of English verse"
+    ],
+    [
+        "Journal of a Residence in China and the neighbouring countries from 1830 to 1833. With an introductory essay by the Hon. and Rev. Baptist Wriothesley Noel. [With a map.]"
+    ],
+    ["The Adventures of Oliver Twist. [With plates.]"],
+    ["['The Adventures of Sherlock Holmes', 'Single Works']"],
+    [
+        "['Coal, Iron, and Oil; or, the Practical American miner. A plain and popular work on our mines and mineral resources ... With numerous maps and engravings, etc']"
+    ],
+    [
+        "Summer Travelling in Iceland; being the narrative of two journeys across the island ... With a chapter on Askja by E. Delmar Morgan ... Containing also a literal translation of three sagas. Maps, etc'"
+    ],
+    [
+        "Histoire de France au moyen aÃÇge, depuis Philippe-Auguste jusqu'aÃÄ la fin du reÃÄgne de Louis XI. 1223-1483. Troisieme eÃÅdition"
+    ],
+    [
+        "Two Centuries of Soho: its institutions, firms, and amusements. By the Clergy of St. Anne's, Soho, J. H. Cardwell ... H. B. Freeman ... G. C. Wilton ... assisted by other contributors, etc"
+    ],
+    ["""A Christmas Carol"""],
+]
+
+description = """
+British Library Books genre detection model
+"""
+
+tokenizer = AutoTokenizer.from_pretrained("BritishLibraryLabs/bl-books-genre")
+
+model = AutoModelForSequenceClassification.from_pretrained("BritishLibraryLabs/bl-books-genre")
+classifier = pipeline('text-classification',model=model, tokenizer=tokenizer, return_all_scores=True)
+
+
+def predict(text):
+    predictions = classifier(text)
+    return {pred['label']: pred['score'] for pred in predictions[0]}
+
+gr.Interface(predict,
+    inputs=gr.inputs.Textbox(label="Book title"),
+    outputs=gr.outputs.Label(label="Predicted genre"), 
+    interpretation='shap',
+    examples=sample_text,description=description,
+   ).launch(enable_queue=True)
+