import gradio as gr
import os
import cv2
import numpy as np
import imutils
from keras.preprocessing.image import img_to_array
from keras.models import load_model

# Load the pre-trained models and define parameters
detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml'
emotion_model_path = 'model4_0.83/model4_entire_model.h5'
face_detection = cv2.CascadeClassifier(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
EMOTIONS = ['neutral', 'happiness', 'surprise', 'sadness', 'anger', 'disgust', 'fear', 'contempt', 'unknown']


# face_detector_mtcnn = MTCNN()
classifier = load_model(emotion_model_path)

def predict_emotion(image):

    faces = face_detection(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()