format

app.py

@@ -19,11 +19,9 @@ sample_text = [
         "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'"
     ],
     [
-       'History of the Monument. With a brief account of the Great Fire of London, which it commemorates. By Charles Welch. (With illustrations and a map of Old London.)',
-    ],
-    [
-      "The history and antiquities of Newbury and its environs [By E. W. Gray.]"
+        "History of the Monument. With a brief account of the Great Fire of London, which it commemorates. By Charles Welch. (With illustrations and a map of Old London.)",
     ],
+    ["The history and antiquities of Newbury and its environs [By E. W. Gray.]"],
     ["""A Christmas Carol"""],
 ]
 
@@ -67,19 +65,25 @@ The model is trained on a particular collection of books digitised by the Britis
 
 tokenizer = AutoTokenizer.from_pretrained("TheBritishLibrary/bl-books-genre")
 
-model = AutoModelForSequenceClassification.from_pretrained("TheBritishLibrary/bl-books-genre")
-classifier = pipeline('text-classification',model=model, tokenizer=tokenizer, top_k=10)
+model = AutoModelForSequenceClassification.from_pretrained(
+    "TheBritishLibrary/bl-books-genre"
+)
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=10)
 
 
 def predict(text):
     predictions = classifier(text)
-    return {pred['label']: float(pred['score']) for pred in predictions}
-
+    return {pred["label"]: float(pred["score"]) for pred in predictions}
 
 
-gr.Interface(predict,
+gr.Interface(
+    predict,
     inputs=gr.Textbox(label="Book title"),
-    outputs='label', 
-    interpretation='shap',  num_shap=10.0, theme="huggingface",
-    examples=sample_text,description=description,article=article,
-   ).launch(enable_queue=True)
+    outputs="label",
+    interpretation="shap",
+    num_shap=10.0,
+    theme="huggingface",
+    examples=sample_text,
+    description=description,
+    article=article,
+).launch(enable_queue=True)