app.py

import gradio as gr
import pandas as pd
from transformers import pipeline

# Initialize model pipelines
classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
generator = pipeline("text2text-generation", model="google/flan-t5-base")

# Initialize an empty DataFrame
df = pd.DataFrame()

# Function to classify customer comments
def classify_comments(categories):
    if df.empty:
        return "No data loaded."
    if not categories:
        return "No categories defined."

    sentiments = []
    comment_categories = []
    for comment in df['customer_comment']:
        sentiment = classifier(comment)[0]['label']
        category_str = ', '.join(categories)
        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']
        sentiments.append(sentiment)
        comment_categories.append(category)
    df['comment_sentiment'] = sentiments
    df['comment_category'] = comment_categories
    return df[['customer_comment', 'comment_sentiment', 'comment_category']].to_html(index=False)

# Function to load data from uploaded file
def load_data(file):
    global df
    if file is not None:
        try:
            df = pd.read_csv(file.name)
            if 'customer_comment' not in df.columns:
                return "Error: Uploaded CSV must contain a column named 'customer_comment'"
            return "Custom CSV loaded successfully!"
        except Exception as e:
            return f"Error loading CSV: {e}"
    else:
        return "No file uploaded."

# Function to add a new category
def add_category(categories, new_category):
    if new_category.strip() and new_category not in categories:
        categories.append(new_category.strip())
    return categories, gr.update(value="")  # Clear the input box after adding

# Function to remove a category
def remove_category(categories, category_to_remove):
    categories = [cat for cat in categories if cat != category_to_remove]
    return categories

# Function to display categories with remove buttons
def display_categories(categories):
    category_elements = []
    for category in categories:
        with gr.Row() as category_row:
            gr.Markdown(f"- {category}")
            remove_button = gr.Button("Remove")
            remove_button.click(fn=remove_category, inputs=[gr.State(categories), gr.State(category)], outputs=gr.State(categories), queue=False)
        category_elements.append(category_row)
    return category_elements

# Gradio Interface
with gr.Blocks() as nps:
    gr.Markdown("# NPS Comment Categorization")

    with gr.Row():
        category_input = gr.Textbox(label="New Category", placeholder="Enter category name")
        add_category_btn = gr.Button("Add Category")

    category_column = gr.Column()
    categories = gr.State([])  # Initialize an empty list for categories

    add_category_btn.click(fn=add_category, inputs=[categories, category_input], outputs=[categories, category_input], queue=False)
    categories.change(fn=display_categories, inputs=categories, outputs=category_column, queue=False)

    uploaded_file = gr.File(label="Upload CSV", type="file")
    upload_output = gr.HTML()
    uploaded_file.change(fn=load_data, inputs=uploaded_file, outputs=upload_output, queue=False)

    classify_btn = gr.Button("Classify Comments")
    output = gr.HTML()
    classify_btn.click(fn=classify_comments, inputs=categories, outputs=output, queue=False)

nps.launch()