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import streamlit as st
import numpy as np
import time
import tensorflow as tf
from utils import load_prepare_image_tf, model_pred_tf, fetch_recipe, load_prepare_image_pt, model_pred_pt
from FoodNoFood import food_not_food
from PIL import Image
import sys
sys.path.insert(1, 'Api Data')
from RecipeData import fetchRecipeData
IMG_SIZE = (224, 224)
model_V1 = 'Seefood_model_v1.tflite'
model_V2 = 'Seefood_model_V2.tflite'
ViT_model = 'ViT-101-1.pt'
@st.cache(show_spinner=False)
def model_prediction(model, img_file, rescale, model_tensor_type):
if model_tensor_type == 'TF':
img = load_prepare_image_tf(img_file, IMG_SIZE, rescale=rescale)
prediction = model_pred_tf(model, img)
sorceCode, recipe_data = fetchRecipeData(prediction)
elif model_tensor_type == 'Pt':
img = load_prepare_image_pt(img_file)
prediction = model_pred_pt(img, model)
print(prediction)
sorceCode, recipe_data = fetchRecipeData(prediction)
return prediction, sorceCode, recipe_data
def main():
st.set_page_config(
page_title="SeeFood",
page_icon="🍔",
layout="wide",
initial_sidebar_state="expanded"
)
st.title('SeeFood🍔')
st.write('Upload a food image and get the recipe for that food and other details of that food')
col1, col2 = st.columns(2)
with col1:
# image uploading button
uploaded_file = st.file_uploader("Choose a file")
selected_model = st.selectbox('Select Model',( 'ViT Model', 'model 1', 'model 2'), index=0)
if uploaded_file is not None:
uploaded_img = uploaded_file.read()
pil_img = Image.open(uploaded_file)
col2.image(uploaded_file, width=700)
# butoon to make predictions
predict = st.button('Get Recipe!')
if predict:
with st.spinner("Analyzing Image 🕵️♂️"):
food_cat = food_not_food(pil_img)
if food_cat == 'food':
if uploaded_file is not None:
with st.spinner('Please Wait 👩🍳'):
# setting model and rescalling
if selected_model in ['model 1', 'model 2']:
if selected_model == 'model 2':
pred_model = model_V2
pred_rescale = True
elif selected_model == 'model 1':
pred_model = model_V1
pred_rescale = False
# makeing prediction and fetching food recipe form api
food, source_code, recipe_data = model_prediction(pred_model, uploaded_img, pred_rescale, 'TF')
elif selected_model == 'ViT Model':
pred_model = ViT_model
pred_rescale = True
# makeing prediction and fetching food recipe form api
food, source_code, recipe_data = model_prediction(pred_model, pil_img, pred_rescale, 'Pt')
# asssigning caleoric breakdown data
percent_Protein = recipe_data['percentProtein']
percent_fat = recipe_data['percentFat']
percent_carbs = recipe_data['percentCarbs']
# food name message
col1.success(f"It's an {food}")
if source_code == 200:
# desplay food recipe
st.header(recipe_data['title']+" Recipe")
col3, col4 = st.columns(2)
with col3:
# Ingridents of recipie
st.subheader('Ingredients')
# st.info(recipe_data['ingridents'])
for i in recipe_data['ingridents']:
st.info(f"{i}")
# Inctuction for recipe
with col4:
st.subheader('Instructions')
st.info(recipe_data['instructions'])
# st.subheader('Caloric Breakdown')
'''
## Caloric Breakdown
'''
st.success(f'''
* Protien: {percent_Protein}%
* Fat: {percent_fat}%
* Carbohydrates: {percent_carbs}%
''')
else:
st.error('Something went wrong please try again :(')
elif food_cat == 'not food':
with col1:
st.warning('Invalid Image Please Add Food Image 👨🔧')
else:
st.warning('Please Upload Image')
if __name__=='__main__':
main() |