| import gradio as gr |
| import os |
| import numpy as np |
| import tensorflow as tf |
| import keras |
| import keras_cv |
| from keras.models import load_model |
| import cv2 |
|
|
|
|
| def image_predict(img_): |
| model = load_model('efficientnet_b0.keras') |
| |
| img = cv2.resize(img_, dsize = [224, 224]) |
| img = img / 255.0 |
| img = np.expand_dims(img, axis = 0) |
|
|
| pred = model.predict(img, verbose = 1) |
| pred = np.argmax(pred, axis = 1) |
|
|
| classes = ['angry', 'happy', 'neutral', 'sad', 'suprised', 'tired'] |
| if pred == 0: |
| answer = f"Facial Expression detected is: {classes[0].capitalize()}" |
| elif pred == 1: |
| answer = f"Facial Expression detected is: {classes[1].capitalize()}" |
| elif pred == 2: |
| answer = f"Facial Expression detected is: {classes[2].capitalize()}" |
| elif pred == 3: |
| answer = f"Facial Expression detected is: Depressed" |
| elif pred == 4: |
| answer = f"Facial Expression detected is: {classes[4].capitalize()}" |
| elif pred == 5: |
| answer = f"Facial Expression detected is: {classes[5].capitalize()}" |
|
|
| return answer |
|
|
|
|
| with gr.Blocks() as demo: |
| image_ = gr.Image(label = 'Input Image to be predicted') |
| output = gr.Textbox(label = 'Prediction') |
| btn = gr.Button('Predict') |
| btn.click(fn = image_predict, inputs = [image_], outputs = output) |
|
|
| demo.launch(share = False) |
