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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_tf, model_pred_tf, fetch_recipe, load_prepare_image_pt, model_pred_pt |
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from FoodNoFood import food_not_food |
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from PIL import Image |
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import sys |
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sys.path.insert(1, 'Api Data') |
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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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ViT_model = 'ViT-101-1.pt' |
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@st.cache(show_spinner=False) |
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def model_prediction(model, img_file, rescale, model_tensor_type): |
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if model_tensor_type == 'TF': |
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img = load_prepare_image_tf(img_file, IMG_SIZE, rescale=rescale) |
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prediction = model_pred_tf(model, img) |
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sorceCode, recipe_data = fetchRecipeData(prediction) |
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elif model_tensor_type == 'Pt': |
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img = load_prepare_image_pt(img_file) |
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prediction = model_pred_pt(img, model) |
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print(prediction) |
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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',( 'ViT Model', 'model 1', 'model 2'), index=0) |
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if uploaded_file is not None: |
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uploaded_img = uploaded_file.read() |
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pil_img = Image.open(uploaded_file) |
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col2.image(uploaded_file, width=700) |
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predict = st.button('Get Recipe!') |
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if predict: |
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with st.spinner("Analyzing Image 🕵️♂️"): |
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food_cat = food_not_food(pil_img) |
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if food_cat == 'food': |
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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 in ['model 1', 'model 2']: |
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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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elif selected_model == 'model 1': |
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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, 'TF') |
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elif selected_model == 'ViT Model': |
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pred_model = ViT_model |
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pred_rescale = True |
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food, source_code, recipe_data = model_prediction(pred_model, pil_img, pred_rescale, 'Pt') |
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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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elif food_cat == 'not food': |
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with col1: |
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st.warning('Invalid Image Please Add Food Image 👨🔧') |
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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() |
