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):
    # Convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Detect faces in the grayscale image
    faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
                                            minNeighbors=5, minSize=(30, 30),
                                            flags=cv2.CASCADE_SCALE_IMAGE)

    confidences = {}
    for (x, y, w, h) in faces:
        # Extract the region of interest (ROI)
        roi_gray = gray[y:y+h, x:x+w]
        roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)

        # Preprocess the ROI for prediction
        img = roi_gray.astype('float') / 255.0
        img = img_to_array(img)
        img = np.expand_dims(img, axis=0)

        # Make a prediction for the emotion
        prediction = emotion_classifier.predict(img)[0]
        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/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()