Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -24,7 +24,6 @@ generator = pipeline("text2text-generation", model="google/flan-t5-base")
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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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@@ -44,29 +43,40 @@ with gr.Blocks() as nps:
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category_list.append(new_category.strip()) # Add new category
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return category_list
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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(
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category_components.append(gr.Row())
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return category_components
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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(
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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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@@ -79,9 +89,7 @@ with gr.Blocks() as nps:
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file.seek(0) # Reset file pointer
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import io
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if file.name.endswith('.csv'):
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file.seek(0) # Reset file pointer
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custom_df = pd.read_csv(file, encoding='utf-8')
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custom_df = pd.read_csv(io.StringIO(content))
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else:
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return "Error: Uploaded file is not a CSV."
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if 'customer_comment' not in custom_df.columns:
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@@ -94,10 +102,12 @@ with gr.Blocks() as nps:
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uploaded_file.change(fn=load_data, inputs=uploaded_file, outputs=output)
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template_btn.click(fn=lambda: "Using Template Dataset", outputs=output)
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def use_template():
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return ["Product Experience", "Customer Support", "Price of Service", "Other"]
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template_btn.click(fn=use_template, outputs=category_boxes)
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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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def classify_comments(category_boxes):
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sentiments = []
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categories = []
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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_list.append(new_category.strip()) # Add new category
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return category_list
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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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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(
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fn=lambda x=cat: remove_category(x, categories),
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inputs=[],
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outputs=category_boxes
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)
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category_components.append(gr.Row())
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return category_components
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category_boxes = gr.State([]) # Store category input boxes as state
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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(
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fn=add_category,
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inputs=[category_boxes, category_input],
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outputs=category_boxes
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)
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category_boxes.change(
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fn=display_categories,
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inputs=category_boxes,
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outputs=category_column
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)
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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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file.seek(0) # Reset file pointer
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import io
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if file.name.endswith('.csv'):
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custom_df = pd.read_csv(file, encoding='utf-8')
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else:
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return "Error: Uploaded file is not a CSV."
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if 'customer_comment' not in custom_df.columns:
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uploaded_file.change(fn=load_data, inputs=uploaded_file, outputs=output)
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template_btn.click(fn=lambda: "Using Template Dataset", outputs=output)
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def use_template():
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return ["Product Experience", "Customer Support", "Price of Service", "Other"]
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template_btn.click(fn=use_template, outputs=category_boxes)
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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(share=True)
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