Initial Commit

app.py

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+from transformers import pipeline
+
+import gradio as gr
+
+
+pipelines_text = {
+    'Spam': {'BERT': pipeline("text-classification", model="mariagrandury/distilbert-base-uncased-finetuned-sms-spam-detection"),
+             'RoBERTa': pipeline("text-classification", model="mariagrandury/roberta-base-finetuned-sms-spam-detection")
+            },
+    'Sentiment': {
+        'BERT': pipeline("text-classification", model="lxyuan/distilbert-base-multilingual-cased-sentiments-student"),
+        'RoBERTa': pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-sentiment-latest")
+    },
+    'Emotion': {'BERT': pipeline("text-classification", model="bhadresh-savani/bert-base-go-emotion"),
+             'RoBERTa': pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
+            }
+}
+
+def parseImage(file, radio): 
+    return file.name
+
+max_textboxes = 100
+def change_textboxes(n):
+    return [gr.Textbox.update(visible=True, interactive=True)]*n + [gr.Textbox.update(visible=False, interactive=True)]*(max_textboxes-int(n))
+
+def parseText(text_upload_file, delimeter_dropdown):
+    delimeter_mapping = {'New Line': '\n','Tab': '\t','Comma': ','}
+    delimeter = delimeter_mapping[delimeter_dropdown]
+    text_boxes = ['' for i in range(max_textboxes)]
+    with open(text_upload_file.name, 'r') as f:
+        text_upload = f.read()
+        for idx, text in enumerate(text_upload.split(delimeter)):
+            text_boxes[idx] = text
+    return text_boxes
+
+def annotateText(text_boxes_slider, annotation_radio, model_dropdown, *text_boxes_texbox):
+    
+    text_boxes_texbox = [text for text in text_boxes_texbox]
+    res_label = ['' for i in range(max_textboxes)]
+    res_score = ['' for i in range(max_textboxes)]
+    
+    # predictions
+    pipe = pipelines_text[annotation_radio][model_dropdown]
+    predictions = pipe([text_boxes_texbox[i] for i in range(text_boxes_slider)])
+    for idx, pred in enumerate(predictions):
+        # special case for spam (might change later)
+        if annotation_radio == 'Spam':
+            res_label[idx] = 'Not Spam' if pred['label'] == 'LABEL_0' else 'Spam'
+        else:
+            res_label[idx] = pred['label']
+        res_score[idx] = '{:.2f}'.format(pred['score'])        
+            
+    with open('annotations.csv', 'w') as f:
+        f.write('text,annotation,confidence\n')
+        for idx in range(max_textboxes):
+            if text_boxes_texbox[idx]:
+                f.write('{},{},{}\n'.format(text_boxes_texbox[idx], res_label[idx], res_score[idx]))
+        
+    return ['./annotations.csv'] + text_boxes_texbox + res_label + res_score
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Data Annotation Tool")
+    gr.Markdown('Upload a file or enter text in the Data Viewer section. Sample files are at the end of the page.')
+    with gr.Tab("Text"):
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("## Data Upload")
+                text_upload_file = gr.File(file_types=['text'])
+                delimeter_dropdown = gr.Dropdown(choices=['New Line','Tab','Comma'], label='Delimeter')
+                text_upload_button = gr.Button('Parse File')
+                
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("## Data Viewer")
+                # slider component
+                text_boxes_slider = gr.Slider(1, max_textboxes, value=3, step=1)
+                # text box components (3 visible and max_textboxes-3 not visible)
+                text_boxes_texbox = [gr.Textbox(show_label=False,interactive=True) for i in range(3)] + [gr.Textbox(show_label=False, visible=False) for i in range(max_textboxes-3)]
+                annotation_radio = gr.Radio(choices=['Spam', 'Sentiment', 'Emotion'], label='Annotation', value='RoBERTa')
+                model_dropdown = gr.Dropdown(choices=['BERT', 'RoBERTa'], label='Model')
+                text_submit_button = gr.Button('Annotate Data')
+        with gr.Row():
+            gr.Markdown("## Data Output")
+        with gr.Row():
+            with gr.Column(scale=6):
+                gr.Markdown("Text")
+                text_output_boxes = [gr.Textbox(show_label=False,interactive=False) for i in range(3)] + [gr.Textbox(show_label=False, visible=False, interactive=False) for i in range(max_textboxes-3)]
+            with gr.Column(scale=1):
+                gr.Markdown("Annotations")
+                text_output_annotations_boxes = [gr.Textbox(show_label=False,interactive=False) for i in range(3)] + [gr.Textbox(show_label=False, visible=False, interactive=False) for i in range(max_textboxes-3)]
+            with gr.Column(scale=1):
+                gr.Markdown("Confidence")
+                text_output_confidence_boxes = [gr.Textbox(show_label=False,interactive=False) for i in range(3)] + [gr.Textbox(show_label=False, visible=False, interactive=False) for i in range(max_textboxes-3)]                
+        
+        text_ouput_file = gr.File(label='File Output', file_types=['csv'])
+        
+        gr.Markdown("## Test Examples")
+        with gr.Row():
+            with gr.Column():
+                gr.Examples(
+                    examples=[['./examples/text/spam.txt', 'New Line'],['./examples/text/sentiment.txt', 'New Line'],['./examples/text/emotion.txt', 'New Line']],
+                    fn=parseText,
+                    inputs=[text_upload_file, delimeter_dropdown],
+                    outputs=text_boxes_texbox,
+                    cache_examples=True
+                )
+    
+    # event listeners
+    text_upload_button.click(fn=parseText, inputs=[text_upload_file, delimeter_dropdown], outputs=text_boxes_texbox)
+    
+    text_boxes_slider.change(fn=change_textboxes, inputs=text_boxes_slider, outputs=text_boxes_texbox)
+    text_boxes_slider.change(fn=change_textboxes, inputs=text_boxes_slider, outputs=text_output_boxes)
+    text_boxes_slider.change(fn=change_textboxes, inputs=text_boxes_slider, outputs=text_output_annotations_boxes)
+    text_boxes_slider.change(fn=change_textboxes, inputs=text_boxes_slider, outputs=text_output_confidence_boxes)
+    
+    text_submit_button.click(fn=annotateText, inputs=[text_boxes_slider, annotation_radio, model_dropdown] + text_boxes_texbox, outputs=[text_ouput_file]+text_output_boxes + text_output_annotations_boxes+text_output_confidence_boxes)
+    
+    
+    with gr.Tab("Image"):
+        with gr.Row():
+            gr.Markdown("## Coming Soon!")
+#         with gr.Row():
+#                 file_image = gr.File(file_count=['directory'],file_types=['image'], label='File Upload')
+#                 image = gr.Image()
+#         with gr.Row():
+#                 radio_image = gr.Radio(choices=['Object Detection'], label='Annotation')
+#                 models_image = gr.Dropdown(choices=['DETR'], label='Model')            
+#         with gr.Row():
+#             button_image = gr.Button('Submit')
+#         with gr.Row():
+#             output_image = gr.File(label='File Output', file_types=['image'])
+    
+
+    # image tab event listeners
+#     button_image.click(fn=doImage, inputs=[file_image, radio_image], outputs=output_image)
+    
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+transformers
+torch