correct formatting

app.py

@@ -1,84 +1,100 @@
 import gradio as gr
 from transformers import pipeline
 
-get_completion = pipeline("summarization",model="sshleifer/distilbart-cnn-12-6")
+get_completion = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
 get_ner = pipeline("ner", model="dslim/bert-base-NER")
-get_zero = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli")
+get_zero = pipeline(
+    "zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli"
+)
+
 
 def summarize_text(input):
     output = get_completion(input)
-    return output[0]['summary_text']
+    return output[0]["summary_text"]
+
 
 def merge_tokens(tokens):
     merged_tokens = []
     for token in tokens:
-        if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):
+        if (
+            merged_tokens
+            and token["entity"].startswith("I-")
+            and merged_tokens[-1]["entity"].endswith(token["entity"][2:])
+        ):
             # If current token continues the entity of the last one, merge them
             last_token = merged_tokens[-1]
-            last_token['word'] += token['word'].replace('##', '')
-            last_token['end'] = token['end']
-            last_token['score'] = (last_token['score'] + token['score']) / 2
+            last_token["word"] += token["word"].replace("##", "")
+            last_token["end"] = token["end"]
+            last_token["score"] = (last_token["score"] + token["score"]) / 2
         else:
             # Otherwise, add the token to the list
             merged_tokens.append(token)
         return merged_tokens
 
+
 def named_entity_recognition(input):
     output = get_ner(input)
     merged_output = merge_tokens(output)
     return {"text": input, "entities": output}
 
-def zero_shot_pred(text,check_labels):
-    output = get_zero(text,check_labels)
+
+def zero_shot_pred(text, check_labels):
+    output = get_zero(text, check_labels)
     return output
 
-def label_score_dict(text,check_labels):
-    zero_shot_out = zero_shot_pred(text,check_labels)
+
+def label_score_dict(text, check_labels):
+    zero_shot_out = zero_shot_pred(text, check_labels)
     out = {}
-    for i,j in zip(zero_shot_out['labels'],zero_shot_out['scores']):
-        out.update({i:j})
+    for i, j in zip(zero_shot_out["labels"], zero_shot_out["scores"]):
+        out.update({i: j})
     print(out)
     return out
 
-interface_summarise = gr.Interface(fn=summarize_text,
- inputs=[gr.Textbox(label="Text to summarise", lines=5)],
- outputs=[gr.Textbox(label="Summary")],
- title="Text Summarizer",
- description="Summary of text via `distillBART-CNN` model!")
-
-interface_ner = gr.Interface(fn=named_entity_recognition,
-                    inputs=[gr.Textbox(label="Text to find entities", lines=2)],
-                    outputs=[gr.HighlightedText(label="Text with entities")],
-                    title="NER with dslim/bert-base-NER",
-                    description="Find entities using the `dslim/bert-base-NER` model under the hood!",
-                    allow_flagging="never",
-                    examples=[
-                        "Tim Cook is the CEO of Apple, stays in California and makes iPhones ",
-                        "My name is Bose and I am a physicist living in Delhi"
-                    ])
-
-interface_zero_shot=gr.Interface(fn=label_score_dict,
-                    inputs=[
-                        gr.Textbox(label="Text to classify", lines=2),
-                        gr.Textbox(label="Check for labels")
-                    ],
-                    outputs=gr.Label(num_top_classes=4),
-                    title="Zero-Shot Preds using DeBERTa-v3-base-mnli",
-                    description="Classify sentence on self defined target vars",
-                    examples=[
-                        ["Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.",
-                        "mobile, website, billing, account access"],
-                        # "My name is Bose and I am a physicist living in Delhi"
-                    ])
-
-demo = gr.TabbedInterface([
-    interface_summarise, 
-    interface_ner,
-    interface_zero_shot],
-    ["Text Summary ",
-     "Named Entity Recognition",
-     "Zero Shot Classifications"
-    ])
+
+interface_summarise = gr.Interface(
+    fn=summarize_text,
+    inputs=[gr.Textbox(label="Text to summarise", lines=5)],
+    outputs=[gr.Textbox(label="Summary")],
+    title="Text Summarizer",
+    description="Summary of text via `distillBART-CNN` model!",
+)
+
+interface_ner = gr.Interface(
+    fn=named_entity_recognition,
+    inputs=[gr.Textbox(label="Text to find entities", lines=2)],
+    outputs=[gr.HighlightedText(label="Text with entities")],
+    title="NER with dslim/bert-base-NER",
+    description="Find entities using the `dslim/bert-base-NER` model under the hood!",
+    allow_flagging="never",
+    examples=[
+        "Tim Cook is the CEO of Apple, stays in California and makes iPhones ",
+        "My name is Bose and I am a physicist living in Delhi",
+    ],
+)
+
+interface_zero_shot = gr.Interface(
+    fn=label_score_dict,
+    inputs=[
+        gr.Textbox(label="Text to classify", lines=2),
+        gr.Textbox(label="Check for labels"),
+    ],
+    outputs=gr.Label(num_top_classes=4),
+    title="Zero-Shot Preds using DeBERTa-v3-base-mnli",
+    description="Classify sentence on self defined target vars",
+    examples=[
+        [
+            "Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.",
+            "mobile, website, billing, account access",
+        ],
+        # "My name is Bose and I am a physicist living in Delhi"
+    ],
+)
+
+demo = gr.TabbedInterface(
+    [interface_summarise, interface_ner, interface_zero_shot],
+    ["Text Summary ", "Named Entity Recognition", "Zero Shot Classifications"],
+)
 
 if __name__ == "__main__":
     demo.launch(enable_queue=True)