Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -20,15 +20,14 @@ classifier = pipeline("text-classification", model="distilbert/distilbert-base-u
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generator = pipeline("text2text-generation", model="google/flan-t5-base")
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# Function to classify customer comments
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# https://huggingface.co/docs/hub/spaces-zerogpu
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@spaces.GPU
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def classify_comments():
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sentiments = []
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categories = []
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results = []
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for comment in df['customer_comment']:
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sentiment = classifier(comment)[0]['label']
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category_list = [box
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category_str = ', '.join([cat.strip() for cat in category_list])
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prompt = f"What category best describes this comment? '{comment}' Please answer using only the name of the category: {category_str}."
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category = generator(prompt, max_length=30)[0]['generated_text']
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@@ -48,54 +47,27 @@ with gr.Blocks() as nps:
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category_boxes = gr.State([]) # Store category input boxes as state
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def display_categories(categories):
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category_column.children = [] # Clear previous categories
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for i, cat in enumerate(categories):
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with category_column:
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with gr.Row():
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gr.Markdown(f"- {cat}")
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remove_btn = gr.Button("X", elem_id=f"remove_{i}", interactive=True)
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remove_btn.click(fn=lambda x=cat: remove_category(x, categories), inputs=[], outputs=category_boxes)
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category_column.children = [] # Reset children to clear previous categories
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for i, cat in enumerate(categories):
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with category_column:
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with gr.Row():
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gr.Markdown(f"- {cat}")
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remove_btn = gr.Button("X", elem_id=f"remove_{i}", interactive=True)
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remove_btn.click(fn=lambda x=cat: remove_category(x, categories), inputs=[], outputs=category_boxes)
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category_components = []
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for i, cat in enumerate(categories):
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with gr.Row():
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gr.Markdown(f"- {cat}")
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remove_btn = gr.Button("X", elem_id=f"remove_{i}", interactive=True)
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remove_btn.click(fn=lambda x=cat: remove_category(x, categories), inputs=[], outputs=category_boxes)
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return category_components
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with gr.Row():
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category_input = gr.Textbox(label="New Category", placeholder="Enter category name")
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add_category_btn = gr.Button("Add Category")
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add_category_btn.click(fn=add_category, inputs=[category_boxes, category_input], outputs=category_boxes)
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category_boxes.change(fn=display_categories, inputs=category_boxes, outputs=category_column)
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category_list.remove(category) # Remove selected category
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return category_list
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components = []
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for i, cat in enumerate(categories):
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row = gr.Row([
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gr.Markdown(f"- {cat}"),
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gr.Button("X", elem_id=f"remove_{i}", interactive=True).click(fn=lambda x=cat: remove_category(x, categories), inputs=[], outputs=category_boxes)
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])
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components.append(row)
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return components
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for i, cat in enumerate(categories):
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row = gr.Row([
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gr.Textbox(value=cat, label=f"Category {i+1}", interactive=True),
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gr.Button("X", elem_id=f"remove_{i}")
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])
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components.append(row)
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return components
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category_column = gr.Column()
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add_category_btn.click(fn=add_category, inputs=[category_boxes, category_input], outputs=category_boxes)
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category_boxes.change(fn=display_categories, inputs=category_boxes, outputs=category_column)
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category_boxes.change(fn=display_categories, inputs=category_boxes, outputs=category_column)
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uploaded_file = gr.File(label="Upload CSV", type="filepath")
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template_btn = gr.Button("Use Template")
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gr.Markdown("# NPS Comment Categorization")
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@@ -128,4 +100,4 @@ with gr.Blocks() as nps:
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category_boxes.change(fn=display_categories, inputs=category_boxes, outputs=category_column)
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classify_btn.click(fn=classify_comments, inputs=category_boxes, outputs=output)
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nps.launch()
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generator = pipeline("text2text-generation", model="google/flan-t5-base")
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# Function to classify customer comments
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@spaces.GPU
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def classify_comments(category_boxes):
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sentiments = []
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categories = []
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results = []
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for comment in df['customer_comment']:
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sentiment = classifier(comment)[0]['label']
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category_list = [box for box in category_boxes if box.strip() != '']
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category_str = ', '.join([cat.strip() for cat in category_list])
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prompt = f"What category best describes this comment? '{comment}' Please answer using only the name of the category: {category_str}."
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category = generator(prompt, max_length=30)[0]['generated_text']
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category_boxes = gr.State([]) # Store category input boxes as state
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def display_categories(categories):
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category_components = []
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for i, cat in enumerate(categories):
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with gr.Row():
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gr.Markdown(f"- {cat}")
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remove_btn = gr.Button("X", elem_id=f"remove_{i}", interactive=True)
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remove_btn.click(fn=lambda x=cat: remove_category(x, categories), inputs=[], outputs=category_boxes)
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category_components.append(gr.Row())
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return category_components
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def remove_category(category, category_list):
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category_list.remove(category) # Remove selected category
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return category_list
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category_column = gr.Column()
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with gr.Row():
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category_input = gr.Textbox(label="New Category", placeholder="Enter category name")
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add_category_btn = gr.Button("Add Category")
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add_category_btn.click(fn=add_category, inputs=[category_boxes, category_input], outputs=category_boxes)
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category_boxes.change(fn=display_categories, inputs=category_boxes, outputs=category_column)
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uploaded_file = gr.File(label="Upload CSV", type="filepath")
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template_btn = gr.Button("Use Template")
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gr.Markdown("# NPS Comment Categorization")
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category_boxes.change(fn=display_categories, inputs=category_boxes, outputs=category_column)
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classify_btn.click(fn=classify_comments, inputs=category_boxes, outputs=output)
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nps.launch()
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