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import streamlit as st
from transformers import pipeline
from PIL import Image
import google.generativeai as palm
import os
# Set up the Google API Key (add this as a secret in Hugging Face Spaces)
os.environ["GOOGLE_API_KEY"] = st.secrets["GOOGLE_API_KEY"]
palm.configure(api_key=os.environ["GOOGLE_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 Google Gemini
def get_ingredients_google(food_name):
"""
Generate a list of ingredients for the given food item using Google Gemini AI.
"""
prompt = f"List the main ingredients typically used to prepare {food_name}."
try:
response = palm.generate_text(prompt=prompt)
if response and "candidates" in response:
return response["candidates"][0]["output"]
else:
return "No ingredients found."
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**: Google Gemini")
# 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_google(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.") |