support dynamic categories

app.py

@@ -28,7 +28,9 @@ def classify_comments():
     results = []
     for comment in df['customer_comment']:
         sentiment = classifier(comment)[0]['label']
-        prompt = f"What category best describes this comment? '{comment}' Please answer using only the name of the category: Product Experience, Customer Support, Price of Service, Other."
+        category_list = [box.value for box in category_boxes if box.value.strip() != '']
+        category_str = ', '.join([cat.strip() for cat in category_list])
+        prompt = f"What category best describes this comment? '{comment}' Please answer using only the name of the category: {category_str}."
         category = generator(prompt, max_length=30)[0]['generated_text']
         categories.append(category)
         sentiments.append(sentiment)
@@ -38,6 +40,14 @@ def classify_comments():
 
 # Gradio Interface
 with gr.Blocks() as nps:
+    def add_category():
+        new_box = gr.Textbox(label="Category", placeholder="Enter a category")
+        category_boxes.append(new_box)
+        return new_box
+
+    add_category_btn.click(fn=add_category, outputs=category_boxes)
+    category_boxes = []  # Store category input boxes
+    add_category_btn = gr.Button("Add Category")
     uploaded_file = gr.File(label="Upload CSV", type="filepath")
     template_btn = gr.Button("Use Template")
     gr.Markdown("# NPS Comment Categorization")
@@ -64,6 +74,6 @@ with gr.Blocks() as nps:
 
     uploaded_file.change(fn=load_data, inputs=uploaded_file, outputs=output)
     template_btn.click(fn=lambda: "Using Template Dataset", outputs=output)
-    classify_btn.click(fn=classify_comments, outputs=output)
+    classify_btn.click(fn=classify_comments, inputs=[category_input], outputs=output)
 
 nps.launch()