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()

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:
        # Generate response from the model
        response = pipe_bloom(prompt, max_new_tokens=50, num_return_sequences=1)
        generated_text = response[0]["generated_text"].strip()
        
        # Post-process the response
        ingredients = generated_text.split(":")[-1].strip()  # Handle cases like "Ingredients: ..."
        ingredients = ingredients.replace(".", "").strip()  # Remove periods and extra spaces

        # Validate the response to ensure no placeholders
        if "ingredient1" in ingredients.lower() or "example" in ingredients.lower():
            return "No valid ingredients found. Try again with a different food."

        return ingredients
    except Exception as e:
        # Handle any errors that occur during the process
        return f"Error generating ingredients: {e}"
# Streamlit app setup
st.title("Food Image Recognition with Ingredients")

# Add banner image
st.image("IR_IMAGE.png", caption="Food Recognition Model", use_column_width=True)

# Sidebar for model information
st.sidebar.title("Model Information")
st.sidebar.write("**Image Classification Model**: Shresthadev403/food-image-classification")
st.sidebar.write("**LLM for Ingredients**: bigscience/bloom-1b7")

# Upload image
uploaded_file = st.file_uploader("Choose a food image...", type=["jpg", "png", "jpeg"])

if uploaded_file is not None:
    # Display the uploaded image
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)
    st.write("Classifying...")

    # Make predictions
    predictions = pipe_classification(image)

    # Display only the top prediction
    top_food = predictions[0]['label']
    st.header(f"Food: {top_food}")

    # Generate and display ingredients for the top prediction
    st.subheader("Ingredients")
    try:
        ingredients = get_ingredients_bloom(top_food)
        st.write(ingredients)
    except Exception as e:
        st.error(f"Error generating ingredients: {e}")

# Footer
st.sidebar.markdown("Created with ❤️ using Streamlit and Hugging Face.")