import numpy as np
import pandas as pd # type: ignore
import os
import keras
import tensorflow as tf
from tensorflow.keras.models import load_model
import pymongo
import streamlit as st
from sentence_transformers import SentenceTransformer
from langchain.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
from langchain_core.messages import HumanMessage, SystemMessage
from PIL import Image
import json
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import textwrap
import plotly.graph_objects as go
st.set_page_config(
page_title="Food Chain",
page_icon="🍴",
layout="wide"
)
# Main App
if "theme_mode" not in st.session_state:
st.session_state.theme_mode = st.get_option("theme.base")
# Check for changes in theme mode
current_theme_mode = st.get_option("theme.base")
if current_theme_mode != st.session_state.theme_mode:
st.session_state.theme_mode = current_theme_mode
st.experimental_rerun()
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
mongo_uri = os.getenv("MONGO_URI_RAG_RECIPE")
@st.cache_resource
def loadEmbedding():
embedding = SentenceTransformer("thenlper/gte-large")
return embedding
embedding = loadEmbedding()
def getEmbedding(text):
if not text.strip():
print("Text was empty")
return []
encoded = embedding.encode(text)
return encoded.tolist()
# Connect to MongoDB
def get_mongo_client(mongo_uri):
try:
client = pymongo.MongoClient(mongo_uri)
print("Connection to MongoDB successful")
return client
except pymongo.errors.ConnectionFailure as e:
print(f"Connection failed: {e}")
return None
if not mongo_uri:
print("MONGO_URI not set in env")
mongo_client = get_mongo_client(mongo_uri)
mongo_db = mongo_client['recipes']
mongo_collection = mongo_db['recipesCollection']
def vector_search(user_query, collection):
query_embedding = getEmbedding(user_query)
if query_embedding is None:
return "Invalid query or embedding gen failed"
vector_search_stage = {
"$vectorSearch": {
"index": "vector_index",
"queryVector": query_embedding,
"path": "embedding",
"numCandidates": 150, # Number of candidate matches to consider
"limit": 4 # Return top 4 matches
}
}
unset_stage = {
"$unset": "embedding" # Exclude the 'embedding' field from the results
}
project_stage = {
"$project": {
"_id": 0, # Exclude the _id field
"name": 1,
"minutes": 1,
"tags": 1,
"n_steps": 1,
"description": 1,
"ingredients": 1,
"n_ingredients": 1,
"formatted_nutrition": 1,
"formatted_steps": 1,
"score": {
"$meta": "vectorSearchScore" # Include the search score
}
}
}
pipeline = [vector_search_stage, unset_stage, project_stage]
results = mongo_collection.aggregate(pipeline)
return list(results)
def mongo_retriever(query):
print("mongo retriever query: ", query)
documents = vector_search(query, mongo_collection)
print("Documents Retrieved: ", documents)
return documents
template = """
You are an assistant for generating results based on user questions.
Use the provided context to generate a result based on the following JSON format:
{{
"name": "Recipe Name",
"minutes": 0,
"tags": [
"tag1",
"tag2",
"tag3"
],
"n_steps": 0,
"description": "A GENERAL description of the recipe goes here.",
"ingredients": [
"0 tablespoons ingredient1",
"0 cups ingredient2",
"0 teaspoons ingredient3"
],
"n_ingredients": 0,
"formatted_nutrition": [
"Calorie : per serving",
"Total Fat : % daily value",
"Sugar : % daily value",
"Sodium : % daily value",
"Protein : % daily value",
"Saturated Fat : % daily value",
"Total Carbohydrate : % daily value"
],
"formatted_steps": [
"1. Step 1 of the recipe.",
"2. Step 2 of the recipe.",
"3. Step 3 of the recipe."
]
}}
Instructions:
1. Focus on the user's specific request and avoid irrelevant ingredients or approaches.
2. Do not return anything other than the JSON.
3. Base the response on simple, healthy, and accessible ingredients and techniques.
4. Rewrite the description in third person
5. Include the ingredient amounts and say them in the steps.
6. If the query makes no sense when trying to connection to a real dish, return []
7. RETURN NOTHING BUT THE JSON
When choosing a recipe from the context, FOLLOW these instructions:
1. The recipe should be makeable from scratch, using only proper ingredients and not other dishes or pre-made recipes
2. If the recipes from the context makes sense but do not match {question}, generate an amazing, specific recipe for {question}
with precise steps and measurements. Take some inspiration from context if availab.e
3. Following the above template.
4. If the query makes no sense when trying to connection to a real dish, return []
5. RETURN NOTHING BUT THE JSON
Context: {context}
Question: {question}
"""
custom_rag_prompt = ChatPromptTemplate.from_template(template)
llm = ChatOpenAI(
model_name="hf:meta-llama/Llama-3.3-70B-Instruct",
api_key = os.environ.get('GLHF_API_KEY'),
base_url = 'https://glhf.chat/api/openai/v1',
temperature=0.2)
rag_chain = (
{"context": mongo_retriever, "question": RunnablePassthrough()}
| custom_rag_prompt
| llm
| StrOutputParser()
)
def get_response(query):
if query:
print("get_response query: ", query)
return rag_chain.invoke(query)
return ""
##############################################
# Classifier
img_size = 224
@st.cache_resource
def loadModel():
model = load_model('efficientnet-fine-d1.keras')
return model
model = loadModel()
class_names = [
"apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare", "beet_salad",
"beignets", "bibimbap", "bread_pudding", "breakfast_burrito", "bruschetta", "caesar_salad",
"cannoli", "caprese_salad", "carrot_cake", "ceviche", "cheese_plate", "cheesecake", "chicken_curry",
"chicken_quesadilla", "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder",
"club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes", "deviled_eggs", "donuts",
"dumplings", "edamame", "eggs_benedict", "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras",
"french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice", "frozen_yogurt",
"garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich", "grilled_salmon", "guacamole", "gyoza",
"hamburger", "hot_and_sour_soup", "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna",
"lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup", "mussels",
"nachos", "omelette", "onion_rings", "oysters", "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck",
"pho", "pizza", "pork_chop", "poutine", "prime_rib", "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake",
"risotto", "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits", "spaghetti_bolognese",
"spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake", "sushi", "tacos", "takoyaki", "tiramisu",
"tuna_tartare", "waffles"
]
def classifyImage(input_image):
input_image = input_image.resize((img_size, img_size))
input_array = tf.keras.utils.img_to_array(input_image)
# Add a batch dimension
input_array = tf.expand_dims(input_array, 0) # (1, 224, 224, 3)
predictions = model.predict(input_array)[0]
print(f"Predictions: {predictions}")
# Sort predictions to get top 5
top_indices = np.argsort(predictions)[-5:][::-1]
# Prepare the top 5 predictions with their class names and percentages
top_predictions = [(class_names[i], predictions[i] * 100) for i in top_indices]
for i, (class_name, confidence) in enumerate(top_predictions, 1):
print(f"{i}. Predicted {class_name} with {confidence:.1f}% Confidence")
return top_predictions
def capitalize_after_number(input_string):
# Split the string on the first period
if ". " in input_string:
num, text = input_string.split(". ", 1)
return f"{num}. {text.capitalize()}"
return input_string
##############################################
#for displaying RAG recipe response
def display_response(response):
"""
Function to format a JSON response into Streamlit's `st.write()` format.
"""
if response == "[]" or "":
st.write("No recipes found :(")
return
if isinstance(response, str):
# Convert JSON string to dictionary if necessary
response = json.loads(response)
with st.container(height=800):
st.write(f"**Name**: {response['name'].capitalize()}")
st.write(f"**Preparation Time**: {response['minutes']} minutes")
st.write(f"**Description**: {response['description'].capitalize()}")
st.write(f"**Tags**: {', '.join(response['tags'])}")
st.write("### Ingredients")
st.write(", ".join([ingredient.capitalize() for ingredient in response['ingredients']]))
st.write(f"**Total Ingredients**: {response['n_ingredients']}")
st.write("### Nutrition Information (per serving)")
st.write(", ".join(response['formatted_nutrition']))
st.write(f"Number of Steps: {response['n_steps']}")
st.write("### Steps")
for step in response['formatted_steps']:
st.write(capitalize_after_number(step))
# st.write(f"Name: {response['name'].capitalize()}")
# st.write(f"Preparation Time: {response['minutes']} minutes")
# st.write(f"Description: {response['description'].capitalize()}")
# st.write(f"Tags: {', '.join(response['tags'])}")
# st.write("### Ingredients")
# st.write(", ".join([ingredient.capitalize() for ingredient in response['ingredients']]))
# st.write(f"Total Ingredients: {response['n_ingredients']}")
# st.write("### Nutrition Information (per serving)")
# st.write(", ".join(response['formatted_nutrition']))
# st.write(f"Number of Steps: {response['n_steps']}")
# st.write("### Steps")
# for step in response['formatted_steps']:
# st.write(capitalize_after_number(step))
def display_dishes_in_grid(dishes, cols=3):
rows = len(dishes) // cols + int(len(dishes) % cols > 0)
for i in range(rows):
cols_data = dishes[i*cols:(i+1)*cols]
cols_list = st.columns(len(cols_data))
for col, dish in zip(cols_list, cols_data):
with col:
st.sidebar.write(dish.replace("_", " ").capitalize())
def display_prediction_graph(class_names, confidences):
# Create a list of labels and values from the predictions dictionary
values = [str(round(value, 1)) + "%" for value in confidences]
# Wrap class names if they are too long
class_names = [textwrap.fill(class_name, width=10) for class_name in class_names]
# Determine the top prediction
class_names.reverse()
# Determine the top prediction
values.reverse()
top_prediction = class_names[-1]
# Create a horizontal bar chart
fig = go.Figure(go.Bar(
x=values,
y=class_names,
orientation='h',
marker=dict(color='orange'),
text=values, # Display values on the bars
textposition='inside' # Position the text inside the bars
))
# Update layout for better appearance
fig.update_layout(
title=f"Prediction: {top_prediction}",
margin=dict(l=20, r=20, t=60, b=20),
xaxis=dict(
showgrid=False, # No grid lines for the x-axis
ticks='', # No x-axis ticks
showticklabels=False # No x-axis tick labels
),
yaxis=dict(
showgrid=False # No grid lines for the y-axis
),
plot_bgcolor='rgba(0,0,0,0)', # No background color for the plot area
paper_bgcolor='rgba(0,0,0,0)', # No background color for the paper area
font=dict() # Default font color
)
# Display the chart in Streamlit
st.plotly_chart(fig)
# #Streamlit
#Left sidebar title
st.sidebar.markdown(
"Food-Chain
",
unsafe_allow_html=True
)
st.sidebar.write("Upload an image and/or enter a query to get started! Explore our trained dish types listed below for guidance.")
st.sidebar.markdown('### Food Classification')
uploaded_image = st.sidebar.file_uploader("Choose an image:", type="jpg")
st.sidebar.markdown('### RAG Recipe')
query = st.sidebar.text_area("Enter your query (optional):", height=100)
recipe_submit = st.sidebar.button(label='Chain Recipe', icon=':material/link:', use_container_width=True)
# gap
st.sidebar.markdown("
", unsafe_allow_html=True)
st.sidebar.markdown("### Dish Database")
selected_dish = st.sidebar.selectbox(
"Search for a dish that our model can classify:",
options=class_names,
index=0
)
# Main title
st.title("Welcome to FOOD CHAIN!")
with st.expander("**What is FOOD CHAIN?**"):
st.markdown(
"""
The project aims to use machine learning and computer vision techniques to analyze food images
and identify them. By using diverse datasets, the model will learn to recognize dishes based on
visual features. Our project aims to inform users about what it is they are eating, including
potential nutritional value and an AI generated response on how their dish might have been prepared.
We want users to have an easy way to figure out what their favorite foods contain, to know any
allergens in the food and to better connect to the food around them. This tool can also tell users
the calories of their dish, they can figure out the nutrients with only a few steps!
Thank you for using our project!
Made by the Classify Crew: [Contact List](https://linktr.ee/classifycrew)
"""
)
#################
sample_RAG = {
"name": "Cinnamon Sugar Baked Donuts",
"minutes": 27,
"tags": [
"30-minutes-or-less",
"time-to-make",
"course",
"cuisine",
"preparation",
"occasion",
"north-american",
"healthy",
"desserts",
"american",
"dietary",
"comfort-food",
"taste-mood"
],
"n_steps": 10,
"description": "A delightful treat with a crusty sugar-cinnamon coating, perfect for a weekend breakfast or snack. Leftovers freeze well.",
"ingredients": [
"1 cup flour",
"1 teaspoon baking powder",
"1 teaspoon cinnamon",
"1/2 teaspoon nutmeg",
"1/4 teaspoon mace",
"1/4 teaspoon salt",
"1/2 cup sugar",
"1 egg",
"1/2 cup milk",
"2 tablespoons butter, melted",
"1 teaspoon vanilla",
"1/4 cup brown sugar"
],
"n_ingredients": 12,
"formatted_nutrition": [
"Calorie : 302.9 per serving",
"Total Fat : 11.0 % daily value",
"Sugar : 154.0 % daily value",
"Sodium : 9.0 % daily value",
"Protein : 7.0 % daily value",
"Saturated Fat : 22.0 % daily value",
"Total Carbohydrate : 18.0 % daily value"
],
"formatted_steps": [
"1. Mix all dry ingredients in a medium-size bowl",
"2. In a smaller bowl, beat the egg",
"3. Mix the egg with milk and melted butter",
"4. Add vanilla to the mixture",
"5. Stir the milk mixture into the dry ingredients until just combined, being careful not to overmix",
"6. Pour the batter into a greased donut baking tin, filling approximately 3/4 full",
"7. Mix cinnamon into brown sugar and sprinkle over the donuts",
"8. Drizzle or spoon melted butter over the top of each donut",
"9. Bake in a 350-degree oven for 17 minutes",
"10. Enjoy!"
]
}
col1, col2 = st.columns(2)
with col1:
st.title("Image Classification")
if not uploaded_image:
placeholder = Image.open("dish-placeholder.jpg")
st.image(placeholder, caption="Placeholder Image.", use_container_width=True)
sample_class_names = ['Donuts', 'Onion Rings', 'Beignets', 'Churros', 'Cup Cakes']
sample_confidences = [98.1131911277771, 1.3879689387977123, 0.12678804341703653, 0.05296396557241678, 0.04436225863173604]
display_prediction_graph(sample_class_names, sample_confidences)
else:
# Open and display image
input_image = Image.open(uploaded_image)
st.image(input_image, caption="Uploaded Image.", use_container_width=True)
with col2:
st.title('RAG Recipe')
if not recipe_submit:
display_response(sample_RAG)
# Image Classification Section
if recipe_submit and uploaded_image:
with col1:
predictions = classifyImage(input_image)
print("Predictions: ", predictions)
# graph variables
fpredictions = ""
class_names = []
confidences = []
# Show the top predictions with percentages
# st.write("Top Predictions:")
for class_name, confidence in predictions:
fpredictions += f"{class_name}: {confidence:.1f}%,"
class_name = class_name.replace("_", " ")
class_name = class_name.title()
# st.markdown(f"*{class_name}*: {confidence:.2f}%")
class_names.append(class_name)
confidences.append(confidence)
print(fpredictions)
display_prediction_graph(class_names, confidences)
# call openai to pick the best classification result based on query
openAICall = [
SystemMessage(
content = "You are a helpful assistant that identifies the best match between classified food items and a user's request based on provided classifications and keywords."
),
HumanMessage(
content = f"""
Based on the following image classification with percentages of each food:
{fpredictions}
And the following user request:
{query}
1. If the user's query relates to any of the classified predictions (even partially or conceptually), select the most relevant dish from the predictions.
2. If the query does not align with the predictions, disregard them and suggest a dish that best matches the user's query.
3. Consider culture, ingredients, cooking steps, etc.
4. Return in the format: [dish]
5. ONLY return the name of the dish in brackets.
Example 1:
Predictions: apple pie: 50%, cherry tart: 30%, vanilla ice cream: 20%
User query: pumpkin
YOUR Response: [pumpkin pie]
Example 2:
Predictions: spaghetti: 60%, lasagna: 30%, salad: 10%
User query: pasta with layers
YOUR Response: [lasagna]
Example 3:
Predictions: sushi: 70%, sashimi: 20%, ramen: 10%
User query: noodles
YOUR Response: [ramen]
"""
),
]
with col2, st.spinner("Generating..."):
if query:
# Call the OpenAI API
openAIresponse = llm.invoke(openAICall)
print("AI CALL RESPONSE: ", openAIresponse.content, "END AI CALL RESONSE")
RAGresponse = get_response(openAIresponse.content + " " + query)
else:
RAGresponse = get_response(predictions[0][0])
print("RAGresponse: ", RAGresponse)
display_response(RAGresponse)
elif recipe_submit and query:
with col2, st.spinner("Generating..."):
response = get_response(query)
print(response)
display_response(response)
else:
st.warning('Please input an image or a query.', icon="🍕")