app.py

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


classifier = pipeline("zero-shot-classification",
                          model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli")

def classify_menu(menu_items):
    
    test_list = menu_items.split("\n")
    

    #classify them
    results = classifier(test_list, ['Vegetarian', 'meat'])
    
    labels = [result["labels"][0] for result in results] 
    veg_score = labels.count("Vegetarian")
    
    # Separate items into vegetarian and meat categories
    vegetarian_items = []
    meat_items = []
    for item, label in zip(test_list, labels):
        if label == "Vegetarian":
            vegetarian_items.append(f"🥦 {item}")
        else:
            meat_items.append(f"🍖 {item}")

    num_items = len(test_list)
    # Format the output with a vegetarian score and categorized lists
    output = f"🌱 Vegetarian Score: {veg_score} out of {num_items} items \n\n"
    output += "🥦 Vegetarian Options:\n" + "\n".join(vegetarian_items) + "\n\n"
    output += "🍖 Meat Options:\n" + "\n".join(meat_items)

    return output

iface = gr.Interface(fn=classify_menu, inputs=gr.Textbox(
        lines=8, 
        placeholder= "Input menu items as {menu} : {description} and ensure every new line is a new menu item.\n\nExample:\nSpicy Sesame Leaf Tofu Patties : Minced Oyster Mushrooms And Tofu Wrapped In Sesame Leaves\nMulligatawny : A Spicy Favorite Of Anglo-Indians Made Of Chicken, Red Lentils"), outputs="text")

iface.launch()