app.py

import gradio as gr
from sentence_transformers import SentenceTransformer
import pandas as pd
from rapidfuzz import fuzz, process

# Load the model
model_name = "sentence-transformers/all-MiniLM-L6-v2"
model = SentenceTransformer(model_name)

# Load CSV data
data_file = "Luxury_Products_Apparel_Data.csv"  # Ensure this file is uploaded
try:
    df = pd.read_csv(data_file, nrows=1000)  # Limit rows for testing
except FileNotFoundError:
    df = pd.DataFrame({
        "ProductName": ["Gucci Shoes", "Nike Sneakers", "Louis Vuitton Handbag"],
        "Category": ["Shoes", "Bags"],
        "SubCategory": ["Sneakers", "Totes"]
    })  # Fallback sample data

# Extract relevant fields
product_names = df["ProductName"].dropna().tolist()
categories = df["Category"].dropna().unique().tolist()
subcategories = df["SubCategory"].dropna().unique().tolist()

# Merge into one dataset for autocomplete
autocomplete_data = product_names + categories + subcategories

# Clean data by removing unnecessary characters
autocomplete_data = [str(item).strip('"') for item in autocomplete_data]

# Autocomplete function
def autocomplete(query):
    if not query.strip():
        return []  # Avoid empty queries

    # Fuzzy matching with typo tolerance
    matches = process.extract(query, autocomplete_data, scorer=fuzz.partial_ratio, limit=5)

    # Return list of suggestions (Gradio will display them in separate lines)
    return [match[0] for match in matches]

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("### Improved Autocomplete for Luxury Products")

    query = gr.Textbox(label="Start typing for autocomplete")
    autocomplete_output = gr.Textbox(label="Autocomplete Suggestions", lines=5, interactive=False)

    # Trigger autocomplete on change
    query.change(fn=autocomplete, inputs=query, outputs=autocomplete_output)

demo.launch()