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import streamlit as st
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import numpy as np
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import time
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import tensorflow as tf
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from utils import load_prepare_image, model_pred, fetch_recipe
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import sys
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from RecipeData import fetchRecipeData
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IMG_SIZE = (224, 224)
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model_V1 = 'Seefood_model_v1.tflite'
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model_V2 = 'Seefood_model_V2.tflite'
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@st.cache()
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def model_prediction(model, img_file, rescale):
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img = load_prepare_image(img_file, IMG_SIZE, rescale=rescale)
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prediction = model_pred(model, img)
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sorceCode, recipe_data = fetchRecipeData(prediction)
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return prediction, sorceCode, recipe_data
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def main():
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st.set_page_config(
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page_title="SeeFood",
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page_icon="🍔",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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st.title('SeeFood🍔')
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st.write('Upload a food image and get the recipe for that food and other details of that food')
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col1, col2 = st.columns(2)
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with col1:
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uploaded_file = st.file_uploader("Choose a file")
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selected_model = st.selectbox('Select Model',('model 1', 'model 2'), index=1)
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if uploaded_file is not None:
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uploaded_img = uploaded_file.read()
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col2.image(uploaded_file, width=500)
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predict = st.button('Get Recipe!')
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if predict:
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if uploaded_file is not None:
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with st.spinner('Please Wait 👩🍳'):
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if selected_model == 'model 2':
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pred_model = model_V2
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pred_rescale = True
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else:
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pred_model = model_V1
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pred_rescale = False
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food, source_code, recipe_data = model_prediction(pred_model, uploaded_img, pred_rescale)
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percent_Protein = recipe_data['percentProtein']
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percent_fat = recipe_data['percentFat']
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percent_carbs = recipe_data['percentCarbs']
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col1.success(f"It's an {food}")
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if source_code == 200:
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st.header(recipe_data['title']+" Recipe")
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col3, col4 = st.columns(2)
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with col3:
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st.subheader('Ingredients')
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for i in recipe_data['ingridents']:
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st.info(f"{i}")
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with col4:
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st.subheader('Instructions')
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st.info(recipe_data['instructions'])
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'''
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## Caloric Breakdown
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'''
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st.success(f'''
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* Protien: {percent_Protein}%
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* Fat: {percent_fat}%
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* Carbohydrates: {percent_carbs}%
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''')
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else:
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st.error('Something went wrong please try again :(')
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else:
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st.warning('Please Upload Image')
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if __name__=='__main__':
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main() |
