ba_app.py

import streamlit as st
import keras
import tensorflow as tf 
from sklearn.neighbors import NearestNeighbors
from tensorflow.keras.preprocessing import image as imgx
import numpy as np
import pandas as pd
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
import pickle
from numpy.linalg import norm
from PIL import Image
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg


df = pd.read_csv('w_urls.csv')

st.title("I'll Bake What She's Having!")
st.markdown("### Scan or upload an image below by clicking the browse files button. You'll then be able to take a photo if on a phone.")


feature_list = pickle.load(open('data/featuresRESNET_mymodel.pickle', 'rb'))
filenames = pickle.load(open('data/filenames.pickle', 'rb'))
model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))


def extract_features(imgin, model):
    input_shape = (224, 224, 3)
    #img = imgx.load_img(imgin, target_size=(input_shape[0], input_shape[1]))
    img_array = imgx.img_to_array(imgin)
    expanded_img_array = np.expand_dims(img_array, axis=0)
    preprocessed_img = preprocess_input(expanded_img_array)
    features = model.predict(preprocessed_img)
    flattened_features = features.flatten()
    normalized_features = flattened_features / norm(flattened_features)
    return normalized_features

def get_knn(img_array):
    neighbors = NearestNeighbors(n_neighbors=20, algorithm='brute', metric='euclidean').fit(feature_list)
    distances, indices = neighbors.kneighbors([extract_features(img_array,model)])
    return distances, indices


uploaded_file = st.file_uploader("Choose an image...")
if uploaded_file is not None:
    image = Image.open(uploaded_file)
    #t_im = cv2.imread('/Users/timothydooley/Documents/ds/metis/repos/ba_scrape/ba_images/“tater_tots”_with_spicy_mayonnaise.jpg')
    #rsized = cv2.resize(src =t_im, dsize=(224,224))
    rsized = image.resize((224,224))
    distances, indices = get_knn(rsized)
    st.image(image, caption='Uploaded Image.', use_column_width=True)
    st.write("") 
    listed = list(indices[0])
    for num in range(10):
        title = df[df.file == filenames[listed[num]].replace('/Users/timothydooley/Documents/ds/metis/repos/ba_scrape/ba_images/', '').replace('.jpg', '')].name.to_string(index = False).strip()
        get_url = df[df.file == filenames[listed[num]].replace('/Users/timothydooley/Documents/ds/metis/repos/ba_scrape/ba_images/', '').replace('.jpg', '')].url.to_string(index = False).strip()
        final_url = f"www.bonappetit.com{get_url}"
        concat_url = f"[{title}]({final_url})"
        st.markdown(f"## {concat_url}", unsafe_allow_html=True)
            
        st.image(filenames[listed[num]], use_column_width = True)


    st.write()