Upload 4 files

app.py

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+from tensorflow import keras
+from keras.preprocessing import image
+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
+
+
+#keras.applications.resnet50.ResNet50
+
+#VGGFace(model='resnet50',include_top=False,input_shape=(224,224,3),pooling='avg')
+
+#st.text("Hello Welcome")
+detector = MTCNN()
+model = keras.applications.resnet50.ResNet50(
+    include_top=False,
+    input_shape=(224,224,3),
+    pooling='avg',
+    weights='imagenet'
+)
+feature_list = pickle.load(open('embedding.pkl', 'rb'))
+filenames = pickle.load(open('filenames.pkl', 'rb'))
+
+filenames = [sub.replace('/kaggle/input/bollywood-celeb-localized-face-dataset/', 'https://technirmitisoftwares.com/img_data/data/') for sub in filenames]
+
+def save_uploaded_image(uploaded_image):
+    try:
+        with open(uploaded_image.name, 'wb') as f:
+            f.write(uploaded_image.getbuffer())
+        return True
+    except:
+        return False
+
+
+def extract_features(img_path, model, detector):
+    img = cv2.imread(img_path)
+    results = detector.detect_faces(img)
+
+    x, y, width, height = results[0]['box']
+
+    face = img[y:y + height, x:x + width]
+
+    #  extract its features
+    image = Image.fromarray(face)
+    image = image.resize((224, 224))
+
+    face_array = np.asarray(image)
+
+    face_array = face_array.astype('float32')
+
+    expanded_img = np.expand_dims(face_array, axis=0)
+    preprocessed_img = keras.applications.resnet50.preprocess_input(expanded_img)
+    result = model.predict(preprocessed_img).flatten()
+    return result
+
+
+def recommend(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])
+
+    index_pos = sorted(list(enumerate(similarity)), reverse=True, key=lambda x: x[1])[0][0]
+    return index_pos
+
+    
+
+st.title('Which bollywood celebrity are you?')
+
+uploaded_image = st.file_uploader('Choose an image')
+
+if uploaded_image is not None:
+    # save the image in a directory
+    if save_uploaded_image(uploaded_image):
+        display_image = Image.open(uploaded_image)
+        st.header("Image Uploded!, Processing...")
+        #st.image(display_image)
+
+        # extract the features
+        features = extract_features(uploaded_image.name, model, detector)
+        #st.text(features)
+        #st.text(features.shape)
+
+        # recommend
+        index_pos = recommend(feature_list,features)
+        predicted_actor = filenames[index_pos]
+        #st.header(predicted_actor)
+        # display
+        col1,col2 = st.columns(2)
+
+        with col1:
+            st.header('Your uploaded image')
+            st.image(display_image,width=150)
+        with col2:
+            st.header("Look Like: " + predicted_actor.split("/")[7])
+            st.image(filenames[index_pos],width=150)

embedding.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c7f487b833163517d66a9a234f933104d4e4e10a5e9db757aa3098fc81b8407a
+size 71477400

filenames.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:96eb16a7402a2c5dc1c7bd55fef34d672cb25c0cd9f0e8538bca606a50453d32
+size 1309928

requirements.txt

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+mtcnn
+tensorflow
+keras
+Keras
+keras-vggface
+Keras_Vggface
+keras_applications
+Keras_Applications
+numpy
+scikit-learn