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(frame):
    frame = imutils.resize(frame, width=300)
    gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
    faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
                                            minNeighbors=5, minSize=(30, 30),
                                            flags=cv2.CASCADE_SCALE_IMAGE)

    frame_clone = frame.copy()
    if len(faces) > 0:
        faces = sorted(faces, reverse=True,
                       key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
        (fX, fY, fW, fH) = faces
        
        # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
        # the ROI for classification via the CNN
        roi = gray[fY:fY + fH, fX:fX + fW]
        roi = cv2.resize(roi, (48, 48))
        roi = roi.astype("float") / 255.0
        roi = img_to_array(roi)
        roi = np.expand_dims(roi, axis=0)

        preds = emotion_classifier.predict(roi)[0]
        label = EMOTIONS[preds.argmax()]

        # Overlay a box over the detected face
        cv2.putText(frame_clone, label, (fX, fY - 10),
                    cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1)
        cv2.rectangle(frame_clone, (fX, fY), (fX + fW, fY + fH),
                      (238, 164, 64), 2)

    else:
        label = "Can't find your face"

    return label




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/chandler.jpeg"),
        os.path.join(os.path.dirname(__file__), "images/janice.jpeg"),
        os.path.join(os.path.dirname(__file__), "images/joey.jpeg"),
        os.path.join(os.path.dirname(__file__), "images/phoebe.jpeg"),
        os.path.join(os.path.dirname(__file__), "images/rachel_monica.jpeg"),
        os.path.join(os.path.dirname(__file__), "images/ross.jpeg"),
        os.path.join(os.path.dirname(__file__), "images/gunther.jpeg")
     
    ],
    title = "Whatchu feeling?",
    theme = "shivi/calm_seafoam"
)
    


if __name__ == "__main__":
    demo.launch()