Update app.py

app.py

@@ -1,150 +1,159 @@
-from unittest import result
-from tensorflow.keras.applications.vgg16 import preprocess_input
-from keras_vggface.vggface import VGGFace
-from src.utils.all_utils import read_yaml,  create_dir
-import pickle
-from sklearn.metrics.pairwise import cosine_similarity
-import streamlit as st
-from PIL import Image
-import os
-import cv2
-from mtcnn import MTCNN
-import numpy as np
-import base64
-from static.load_css import local_css
-
-
-local_css("static/style.css")
-
-def get_base64(bin_file):
-    with open(bin_file, 'rb') as f:
-        data = f.read()
-    return base64.b64encode(data).decode()
-
-def set_background(png_file):
-    bin_str = get_base64(png_file)
-    page_bg_img = '''
-    <style>
-    body {
-    background-image: url("data:image/png;base64,%s");
-    background-size: cover;
-    }
-    </style>
-    ''' % bin_str
-    st.markdown(page_bg_img, unsafe_allow_html=True)
-
-set_background('artifacts/images.jpg')
-
-
-config = read_yaml("config/config.yaml")
-params = read_yaml('params.yaml')
-
-artifacts = config['artifacts']
-artifacts_dirs = artifacts['artifacts_dir']
-
-upload_image_dir = artifacts['upload_image_dir']
-upload_path = os.path.join(artifacts_dirs, upload_image_dir)
-
-pickle_format_dirs = artifacts['pickle_format_data_dir']
-img_pickle_file_name = artifacts['img_pickle_file_name']
-pickle_actor_name = artifacts['pickle_actor_names']
-
-pickle_dir_path = os.path.join(artifacts_dirs, pickle_format_dirs)
-pickle_file = os.path.join(pickle_dir_path, img_pickle_file_name)
-pickle_actor = os.path.join(pickle_dir_path, pickle_actor_name)
-
-feature_extractor_dir = artifacts['feature_extraction_dir']
-extracted_feature_name = artifacts['extracted_features_name']
-
-feature_extractor_path = os.path.join(artifacts_dirs, feature_extractor_dir)
-feature_name = os.path.join(feature_extractor_path, extracted_feature_name)
-
-data = params['base']['data_path']
-
-model_name = params['base']['BASE_MODEL']
-include_top = params['base']['include_top']
-pooling = params['base']['pooling']
-
-detector = MTCNN()
-model = VGGFace(model= model_name, include_top=include_top, input_shape=(244,244,3), pooling=pooling)
-
-filenames = pickle.load(open(pickle_file, 'rb'))
-feature_list = pickle.load(open(feature_name, 'rb'))
-actor_names = pickle.load(open(pickle_actor, 'rb'))
-
-def extracted_features(img_path, model, detector):
-    img = cv2.imread(img_path)
-    result = detector.detect_faces(img)
-
-    x,y,width, heigth = result[0]['box']
-    face = img[y:y+heigth, x:x+width]
-    image = Image.fromarray(face)
-    image= image.resize((244,244))
-
-    face_array = np.asarray(image)
-    face_array = face_array.astype('float32')
-
-    expanded_img = np.expand_dims(face_array, axis= 0)
-    preprocess_img = preprocess_input(expanded_img)
-    result= model.predict(preprocess_img).flatten()
-
-    return result
-
-def recommed(feature_list, features):
-    similarity = []
-    for i in range(len(feature_list)):
-        similarity.append(cosine_similarity(features.reshape(1,-1), feature_list[i].reshape(1,-1))[0][0])
-
-
-    result = sorted(list(enumerate(similarity)), reverse=True, key=lambda x: x[1])[0]
-    index_pos = result[0]
-    percentage = result[1]
-
-    return index_pos, percentage
-
-def save_upload_image(upload_image):
-    try:
-        create_dir([upload_path])
-        with open(os.path.join(upload_path, upload_image.name), 'wb') as f:
-            f.write(upload_image.getbuffer())
-        return True
-    except:
-        return False
-
-main_title = "<center><div><p class='highlight grey' style='font-size:47px'><span class='bold'>Guess your look alike celebrity</span></span></div></center>"
-st.markdown(main_title, unsafe_allow_html=True)
-
-
-uploaded_image = st.file_uploader('Choose a image')
-if uploaded_image is not None:
-    if save_upload_image(uploaded_image):
-
-        display_image = Image.open(uploaded_image)
-        resized_display_img = display_image.resize((260,320), Image.ANTIALIAS)
-    
-
-        upload_image_path = os.path.join(upload_path, uploaded_image.name) 
-        features = extracted_features(upload_image_path, model, detector)
-        img_path, percentage = recommed(feature_list, features)
-        actor_path = filenames[img_path]
-        
-
-        predicted_actor =   " ".join(actor_path.split('\\')[2].split('_'))
-
-        actor_root_name = actor_path.split('\\')[2]
-        pred_actor_path = os.path.join(data,actor_root_name, '1.jpg' )
-
-        pred_actor_image = Image.open(pred_actor_path)
-        resized_actor_img = pred_actor_image.resize((260,320), Image.ANTIALIAS)
-
-        st.header(f'You look like {predicted_actor} with {np.round(percentage*100,0)}% similarity')
-
-        col1, col2 = st.beta_columns(2)
-
-        with col1:
-            st.markdown("Thats you")
-            st.image(resized_display_img)
-
-        with col2:
-            st.markdown("Your look alike celelb")
-            st.image(resized_actor_img)
+from unittest import result
+from tensorflow.keras.applications.vgg16 import preprocess_input
+from tensorflow.python.keras.utils.layer_utils import get_source_inputs
+from tensorflow.python.keras.layers import (
+    Flatten, Dense, Input, GlobalAveragePooling2D,
+    GlobalMaxPooling2D, Activation, Conv2D, MaxPooling2D, 
+    BatchNormalization, AveragePooling2D, Reshape, Permute, multiply
+)
+from tensorflow.python.keras.models import Model
+from tensorflow.python.keras import backend as K
+from tensorflow.python.keras.utils.data_utils import get_file
+from keras_vggface.vggface import VGGFace
+from src.utils.all_utils import read_yaml,  create_dir
+import pickle
+from sklearn.metrics.pairwise import cosine_similarity
+import streamlit as st
+from PIL import Image
+import os
+import cv2
+from mtcnn import MTCNN
+import numpy as np
+import base64
+from static.load_css import local_css
+
+
+local_css("static/style.css")
+
+def get_base64(bin_file):
+    with open(bin_file, 'rb') as f:
+        data = f.read()
+    return base64.b64encode(data).decode()
+
+def set_background(png_file):
+    bin_str = get_base64(png_file)
+    page_bg_img = '''
+    <style>
+    body {
+    background-image: url("data:image/png;base64,%s");
+    background-size: cover;
+    }
+    </style>
+    ''' % bin_str
+    st.markdown(page_bg_img, unsafe_allow_html=True)
+
+set_background('artifacts/images.jpg')
+
+
+config = read_yaml("config/config.yaml")
+params = read_yaml('params.yaml')
+
+artifacts = config['artifacts']
+artifacts_dirs = artifacts['artifacts_dir']
+
+upload_image_dir = artifacts['upload_image_dir']
+upload_path = os.path.join(artifacts_dirs, upload_image_dir)
+
+pickle_format_dirs = artifacts['pickle_format_data_dir']
+img_pickle_file_name = artifacts['img_pickle_file_name']
+pickle_actor_name = artifacts['pickle_actor_names']
+
+pickle_dir_path = os.path.join(artifacts_dirs, pickle_format_dirs)
+pickle_file = os.path.join(pickle_dir_path, img_pickle_file_name)
+pickle_actor = os.path.join(pickle_dir_path, pickle_actor_name)
+
+feature_extractor_dir = artifacts['feature_extraction_dir']
+extracted_feature_name = artifacts['extracted_features_name']
+
+feature_extractor_path = os.path.join(artifacts_dirs, feature_extractor_dir)
+feature_name = os.path.join(feature_extractor_path, extracted_feature_name)
+
+data = params['base']['data_path']
+
+model_name = params['base']['BASE_MODEL']
+include_top = params['base']['include_top']
+pooling = params['base']['pooling']
+
+detector = MTCNN()
+model = VGGFace(model= model_name, include_top=include_top, input_shape=(244,244,3), pooling=pooling)
+
+filenames = pickle.load(open(pickle_file, 'rb'))
+feature_list = pickle.load(open(feature_name, 'rb'))
+actor_names = pickle.load(open(pickle_actor, 'rb'))
+
+def extracted_features(img_path, model, detector):
+    img = cv2.imread(img_path)
+    result = detector.detect_faces(img)
+
+    x,y,width, heigth = result[0]['box']
+    face = img[y:y+heigth, x:x+width]
+    image = Image.fromarray(face)
+    image= image.resize((244,244))
+
+    face_array = np.asarray(image)
+    face_array = face_array.astype('float32')
+
+    expanded_img = np.expand_dims(face_array, axis= 0)
+    preprocess_img = preprocess_input(expanded_img)
+    result= model.predict(preprocess_img).flatten()
+
+    return result
+
+def recommed(feature_list, features):
+    similarity = []
+    for i in range(len(feature_list)):
+        similarity.append(cosine_similarity(features.reshape(1,-1), feature_list[i].reshape(1,-1))[0][0])
+
+
+    result = sorted(list(enumerate(similarity)), reverse=True, key=lambda x: x[1])[0]
+    index_pos = result[0]
+    percentage = result[1]
+
+    return index_pos, percentage
+
+def save_upload_image(upload_image):
+    try:
+        create_dir([upload_path])
+        with open(os.path.join(upload_path, upload_image.name), 'wb') as f:
+            f.write(upload_image.getbuffer())
+        return True
+    except:
+        return False
+
+main_title = "<center><div><p class='highlight grey' style='font-size:47px'><span class='bold'>Guess your look alike celebrity</span></span></div></center>"
+st.markdown(main_title, unsafe_allow_html=True)
+
+
+uploaded_image = st.file_uploader('Choose a image')
+if uploaded_image is not None:
+    if save_upload_image(uploaded_image):
+
+        display_image = Image.open(uploaded_image)
+        resized_display_img = display_image.resize((260,320), Image.ANTIALIAS)
+    
+
+        upload_image_path = os.path.join(upload_path, uploaded_image.name) 
+        features = extracted_features(upload_image_path, model, detector)
+        img_path, percentage = recommed(feature_list, features)
+        actor_path = filenames[img_path]
+        
+
+        predicted_actor =   " ".join(actor_path.split('\\')[2].split('_'))
+
+        actor_root_name = actor_path.split('\\')[2]
+        pred_actor_path = os.path.join(data,actor_root_name, '1.jpg' )
+
+        pred_actor_image = Image.open(pred_actor_path)
+        resized_actor_img = pred_actor_image.resize((260,320), Image.ANTIALIAS)
+
+        st.header(f'You look like {predicted_actor} with {np.round(percentage*100,0)}% similarity')
+
+        col1, col2 = st.beta_columns(2)
+
+        with col1:
+            st.markdown("Thats you")
+            st.image(resized_display_img)
+
+        with col2:
+            st.markdown("Your look alike celelb")
+            st.image(resized_actor_img)