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import streamlit as st
from transformers import pipeline
from PIL import Image
from huggingface_hub import InferenceClient
import os
# Hugging Face API key
API_KEY = st.secrets["HF_API_KEY"]
# Initialize the Hugging Face Inference Client
client = InferenceClient(api_key=API_KEY)
# 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()
# Function to generate ingredients using Hugging Face Inference Client
def get_ingredients_qwen(food_name):
"""
Generate a list of ingredients for the given food item using Qwen NLP model.
Returns a clean, comma-separated list of ingredients.
"""
messages = [
{
"role": "user",
"content": f"List only the main ingredients for {food_name}. "
f"Respond in a concise, comma-separated list without any extra text or explanations."
}
]
try:
completion = client.chat.completions.create(
model="Qwen/Qwen2.5-Coder-32B-Instruct",
messages=messages,
max_tokens=50
)
generated_text = completion.choices[0].message["content"].strip()
return generated_text
except Exception as e:
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**: Qwen/Qwen2.5-Coder-32B-Instruct")
# 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_qwen(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.") |