import gradio as gr
import moviepy.video.io.ImageSequenceClip
from PIL import Image
from pydub import AudioSegment
from moviepy.editor import ImageSequenceClip, VideoFileClip, AudioFileClip
import numpy as np
import os
from mutagen.mp3 import MP3
import cv2
from dotenv import load_dotenv
from transformers import pipeline, AutoProcessor, AutoModel
import torch
import soundfile as sf

# Load environment variables
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")

def resize(img_list):
    resize_img_list = []
    for item in img_list:
        im = Image.open(item)
        imResize = im.resize((256, 256), Image.LANCZOS)
        resize_img_list.append(np.array(imResize))
    return resize_img_list

def text2speech(text):
    # Using Microsoft's SpeechT5 model instead of FastSpeech2
    processor = AutoProcessor.from_pretrained("microsoft/speecht5_tts")
    model = AutoModel.from_pretrained("microsoft/speecht5_tts")
    
    # Preprocessing text input
    inputs = processor(text=text, return_tensors="pt")
    
    # Generate speech with default speaker embedding
    speaker_embeddings = torch.zeros((1, model.config.speaker_embedding_size))
    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings)
    
    # Save as flac file
    sf.write("speech_output.flac", speech.numpy(), samplerate=16000)
    return "speech_output.flac"

def merge_audio_video(entities_num, resize_img_list, text_input):
    speech = text2speech(text_input)
    wav_audio = AudioSegment.from_file(speech, "flac")
    wav_audio.export("audio.mp3", format="mp3")
    audio_length = int(MP3("audio.mp3").info.length)
    fps = entities_num / audio_length  
    fps = float(format(fps, '.5f'))
    
    clip = ImageSequenceClip(resize_img_list, fps=fps)
    clip.write_videofile('my_vid_tmp.mp4')
    
    videoclip = VideoFileClip('my_vid_tmp.mp4')
    audioclip = AudioFileClip('audio.mp3')
    mergedclip = videoclip.set_audio(audioclip)
    
    return mergedclip

def engine(text_input):
    ner = gr.Interface.load("huggingface/flair/ner-english-ontonotes-large", api_key=HF_TOKEN)
    entities = ner(text_input)
    entities = [tupl for tupl in entities if None not in tupl]
    entities_num = len(entities)
    img_list = []
    
    latentdiffusion = gr.Interface.load("spaces/multimodalart/latentdiffusion", api_key=HF_TOKEN)
    
    for ent in entities:
        img = latentdiffusion(ent[0], '50', '256', '256', '1', 10)[0]
        img_list.append(img)
    
    resize_img_list = resize(img_list)
    mergedclip = merge_audio_video(entities_num, resize_img_list, text_input)
    mergedclip.write_videofile('mergedvideo.mp4')
    
    return 'mergedvideo.mp4'

app = gr.Interface(
    fn=engine, 
    inputs=gr.Textbox(lines=5, label="Input Text"), 
    outputs=gr.Video(label='Final Merged Video'), 
    description="<div>🎭🎞️🍿 AI Movie Maker - Comedy 🎬 🧠 🎨</div>",
    examples=[
        ["Two space marines take up arms to save the planet from an alien invasion. These two dashing strong men play a comedic role in the science fiction movie of the future where even Barnaby bunny is willing to join their wacky gang of space marines to save the planet with good looks and comedy."]
    ],
    title="AI Pipeline Multi Model 🎭🎞️🍿 Movie Maker 🎬 🧠 🎨",
    article="<br><div></div>"
).launch(debug=True)