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import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from PIL import Image
import requests
# Load the image classification pipeline
@st.cache_resource
def load_image_classification_pipeline():
return pipeline("image-classification", model="Shresthadev403/food-image-classification")
pipe_classification = load_image_classification_pipeline()
# Load the Meta-Llama model and tokenizer for text generation
@st.cache_resource
def load_llama_pipeline():
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
return pipeline("text-generation", model=model, tokenizer=tokenizer)
pipe_llama = load_llama_pipeline()
# Function to generate ingredients using Meta-Llama
def get_ingredients(food_name):
prompt = f"List the main ingredients typically used to prepare {food_name}:"
response = pipe_llama(prompt, max_length=50, num_return_sequences=1)
return response[0]['generated_text']
# Streamlit app
st.title("Food Image Classification with Ingredients Generation")
st.write("Upload an image to classify the type of food and get its ingredients!")
# 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']
confidence = predictions[0]['score']
st.subheader("Top Prediction")
st.write(f"**{top_food}** with confidence {confidence:.2f}")
# Generate and display ingredients for the top prediction
st.subheader("Ingredients")
try:
ingredients = get_ingredients(top_food)
st.write(ingredients)
except Exception as e:
st.write("Could not generate ingredients. Please try again later.") |