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import gradio as gr
import os
import cv2
import PIL
from PIL import Image
from mtcnn import MTCNN
import numpy as np
from tensorflow.keras.models import load_model
from keras.preprocessing.image import img_to_array
emotions = ['neutral','happiness','surprise','sadness','anger','disgust','fear','contempt','unknown']
#classifier = load_model("model_9.keras")
face_detector_mtcnn = MTCNN()
classifier = load_model("model_2_aug_nocall_entire_model.h5")
def predict_emotion(image):
faces = face_detector_mtcnn.detect_faces(image)
for face in faces:
x,y,w,h = face['box']
roi = image[y:y+h,x:x+w]
# Converting the region of interest to grayscale, and resize
roi_gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)
img = roi_gray.astype('float')/255.0
img = img_to_array(img)
img = np.expand_dims(img,axis=0)
prediction = classifier.predict(img)[0]
#top_indices = np.argsort(prediction)[-2:]
#top_emotion = top_indices[1]
#second_emotion = top_indices[0]
#label = emotions[top_emotion]
confidences = {emotions[i]: float(prediction[i]) for i in range(len(emotions))}
return confidences
demo = gr.Interface(
fn = predict_emotion,
inputs = gr.Image(type="numpy"),
outputs = gr.Label(num_top_classes=9),
#flagging_options=["blurry", "incorrect", "other"],
examples = [
os.path.join(os.path.dirname(__file__), "images/Image_1.jpg"),
os.path.join(os.path.dirname(__file__), "images/Image_2.jpg"),
os.path.join(os.path.dirname(__file__), "images/Image_3.jpg"),
os.path.join(os.path.dirname(__file__), "images/Image_4.jpg"),
os.path.join(os.path.dirname(__file__), "images/Image_5.jpg"),
os.path.join(os.path.dirname(__file__), "images/Image_6.jpg"),
os.path.join(os.path.dirname(__file__), "images/Image_7.jpg"),
os.path.join(os.path.dirname(__file__), "images/Image_8.jpg"),
os.path.join(os.path.dirname(__file__), "images/Image_9.jpg"),
os.path.join(os.path.dirname(__file__), "images/Image_10.jpg"),
],
title = "Whatchu feeling?",
theme = "shivi/calm_seafoam"
)
if __name__ == "__main__":
demo.launch() |