import gradio as gr
import tensorflow as tf
from typing import List
import os
import cv2
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import legacy
from tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten
from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler
from moviepy.editor import VideoFileClip



def convert_mp4_to_mpg(input_file):
    if not os.path.exists(input_file):
        raise FileNotFoundError(f"Input file '{input_file}' not found.")

    video_clip = VideoFileClip(input_file)
    video_clip.write_videofile("output.mpg", codec='mpeg2video')
    video_clip.close()

def load_video(video_path):

    cap = cv2.VideoCapture(video_path)
    frames = []
    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))):
        ret, frame = cap.read()
        frame = tf.image.rgb_to_grayscale(frame)
        frames.append(frame[190:236,80:220,:])
    cap.release()

    mean = tf.math.reduce_mean(frames)
    std = tf.math.reduce_std(tf.cast(frames, tf.float32))
    return tf.cast((frames - mean), tf.float32) / std

def load_data(path: str):
    path = bytes.decode(path.numpy())
    #file_name = path.split('/')[-1].split('.')[0]
    # File name splitting for windows
    file_name = path.split('\\')[-1].split('.')[0]
    video_path = os.path.join('data','s1',f'{file_name}.mpg')
    alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')
    frames = load_video(video_path)


    return frames


vocab = [x for x in "abcdefghijklmnopqrstuvwxyz'?!123456789 "]
char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token="")
num_to_char = tf.keras.layers.StringLookup(
    vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True
)

model = Sequential()
model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))
model.add(Activation('relu'))
model.add(MaxPool3D((1,2,2)))

model.add(Conv3D(256, 3, padding='same'))
model.add(Activation('relu'))
model.add(MaxPool3D((1,2,2)))

model.add(Conv3D(75, 3, padding='same'))
model.add(Activation('relu'))
model.add(MaxPool3D((1,2,2)))

model.add(TimeDistributed(Flatten()))

model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
model.add(Dropout(.5))

model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
model.add(Dropout(.5))

model.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))
model.summary()

optimizer = legacy.Adam(learning_rate=0.001)  # Replace legacy.Adam with the appropriate legacy optimizer you used during training

model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])

#Loading weights 
model.load_weights('model/checkpoint')

def Predict(Video):
  convert_mp4_to_mpg(Video)
  sample = load_data(tf.convert_to_tensor("output.mpg"))
  yhat = model.predict(tf.expand_dims(sample, axis=0))
  decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()
  result=[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]
  return result[0].numpy().decode('utf-8')


interface = gr.Interface(fn=Predict, 
                        inputs="video",
                        outputs="text", 
                        title='Video Lip Reading')
                        

interface.launch(debug=True)