app.py

import streamlit as st
from transformers import pipeline
from PIL import Image
import os

# Load the image classification pipeline
@st.cache_resource
def load_image_classification_pipeline():
    """
    Load the image classification pipeline using a pretrained model.
    """
    return pipeline("image-classification", model="Shresthadev403/food-image-classification")

pipe_classification = load_image_classification_pipeline()

# Load the BLOOM model for ingredient generation
@st.cache_resource
def load_bloom_pipeline():
    """
    Load the BLOOM model for ingredient generation.
    """
    return pipeline("text-generation", model="bigscience/bloom-1b7")

pipe_bloom = load_bloom_pipeline()

# Function to generate ingredients using BLOOM
def get_ingredients_bloom(food_name):
    """
    Generate a list of ingredients for the given food item using BLOOM.
    Returns a clean, comma-separated list of ingredients.
    """
    prompt = (
        f"Generate a list of the main ingredients used to prepare {food_name}. "
        "Respond only with a concise, comma-separated list of ingredients, without any additional text, explanations, or placeholders. "
        "For example, if the food is pizza, respond with 'cheese, tomato sauce, bread, olive oil, basil'."
    )
    try:
        response = pipe_bloom(prompt, max_length=50, num_return_sequences=1)
        generated_text = response[0]["generated_text"].strip()
        
        # Post-process the response