app.py

import gradio as gr
import ffmpeg
from pathlib import Path
import os
import ast
import json
import base64
import requests
import moviepy.editor as mp
from PIL import Image, ImageSequence
import cv2


API_URL = "https://api-inference.huggingface.co/models/facebook/wav2vec2-base-960h"
headers = {"Authorization": "Bearer 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"}
#HF_TOKEN = os.environ["HF_TOKEN"]
#headers = {"Authorization": f"Bearer {HF_TOKEN}"}


def generate_transcripts(in_video): #generate_gifs(in_video, gif_transcript):
    print("********* Inside generate_transcripts() **********")
    #convert video to audio
    print(f" input video is : {in_video}")
    
    video_path = Path("./ShiaLaBeouf.mp4")
    audio_memory, _ = ffmpeg.input(video_path).output('-', format="wav", ac=1, ar='16k').overwrite_output().global_args('-loglevel', 'quiet').run(capture_stdout=True)
    
    #Getting transcripts using wav2Vec2 huggingface hosted accelerated inference
    #sending audio file in request along with stride and chunk length information
    model_response = query_api(audio_memory)
    
    #model response has both - transcripts as well as character timestamps or chunks
    print(f"model_response is : {model_response}")
    transcription = model_response["text"].lower()
    chnk = model_response["chunks"]
    
    #creating lists from chunks to consume downstream easily
    timestamps = [[chunk["text"].lower(), chunk["timestamp"][0], chunk["timestamp"][1]]
              for chunk in chnk]
    
    #getting words and word timestamps
    words, words_timestamp = get_word_timestamps(timestamps)
    print(f"Total words in the audio transcript is:{len(words)}, transcript word list is :{words}, type of words is :{type(words)} ") 
    print(f"Total Word timestamps derived fromcharacter timestamp are :{len(words_timestamp)}, Word timestamps are :{words_timestamp}")
    
    return transcription, words, words_timestamp
    
    
def generate_gifs(gif_transcript, words, words_timestamp):
    print("********* Inside generate_gifs() **********")
    
    #creating list from input gif transcript 
    gif = "don't let your dreams be dreams"
    #gif = gif_transcript
    giflist = gif.split()
    
    #getting gif indexes from the generator
    # Converting string to list
    words = ast.literal_eval(words)
    words_timestamp = ast.literal_eval(words_timestamp)
    print(f"words is :{words}")
    print(f"type of words is :{type(words)}")
    print(f"length of words is :{len(words)}")
    print(f"giflist is :{giflist}")
    #print(f"haystack and needle function returns value as : {list(get_gif_word_indexes(words, giflist))}")
    #indx_tmp = [num for num in get_gif_word_indexes(words, giflist)]
    #print(f"index temp is : {indx_tmp}")
    giflist_indxs = list(list(get_gif_word_indexes(words, giflist))[0])
    print(f"giflist_indxs is : {giflist_indxs}")
    #getting start and end timestamps for a gif video
    start_seconds, end_seconds = get_gif_timestamps(giflist_indxs, words_timestamp)
    print(f"start_seconds, end_seconds  are : ({start_seconds}, {end_seconds})")
    #generated .gif image
    im, gif_img, gif_vid, vid_cap = gen_moviepy_gif(start_seconds, end_seconds)
    im.save('./gifimage1.gif', save_all=True)
    #gif_img = gen_moviepy_gif(start_seconds, end_seconds)
    #gif_img = f"./gifimage.gif"
    #html_out = "<img src= '" + gif_img + "' alt='create a gif from video' width='100%'/>"
    #print("html out is :", html_out)        
    return vid_cap #gif_vid

    
#calling the hosted model
def query_api(audio_bytes: bytes):
    """
    Query for Huggingface Inference API for Automatic Speech Recognition task
    """
    print("********* Inside query_api() **********")
    payload = json.dumps({
        "inputs": base64.b64encode(audio_bytes).decode("utf-8"),
        "parameters": {
            "return_timestamps": "char",
            "chunk_length_s": 10,
            "stride_length_s": [4, 2]
        },
        "options": {"use_gpu": False}
    }).encode("utf-8")

    response = requests.request(
        "POST", API_URL, headers=headers, data=payload)
    json_reponse = json.loads(response.content.decode("utf-8"))
    print(f"json_reponse is :{json_reponse}")
    return json_reponse


#getting word timestamps from character timestamps
def get_word_timestamps(timestamps): 
  words, word = [], []
  letter_timestamp, word_timestamp, words_timestamp = [], [], []
  for idx,entry in enumerate(timestamps):
    word.append(entry[0])
    letter_timestamp.append(entry[1])
    if entry[0] == ' ':
      words.append(''.join(word))
      word_timestamp.append(letter_timestamp[0])
      word_timestamp.append(timestamps[idx-1][2])
      words_timestamp.append(word_timestamp)
      word, word_timestamp, letter_timestamp = [], [], []

  words = [word.strip() for word in words]
  return words, words_timestamp


#getting index of gif words in main transcript
def get_gif_word_indexes(total_words_list, gif_words_list):  
    if not gif_words_list:
        print("THIS IS 1")
        return
    # just optimization
    COUNT=0
    lengthgif_words_list = len(gif_words_list)
    print("THIS IS 2")
    firstgif_words_list = gif_words_list[0]
    print("THIS IS 3")
    
    print(f"total_words_list is :{total_words_list}")
    print(f"length of total_words_list is :{len(total_words_list)}")
    print(f"gif_words_list is :{gif_words_list}")
    print(f"length of gif_words_list is :{len(gif_words_list)}")
    
    for idx, item in enumerate(total_words_list):
        COUNT+=1
        #print("COUNT IS :", COUNT)
        if item == firstgif_words_list:
            print("THIS IS 5")
            if total_words_list[idx:idx+lengthgif_words_list] == gif_words_list:
                print("THIS IS 6")
                print(f"value 1 is: {range(idx, idx+lengthgif_words_list)}")
                print(f"value of tuple is : {tuple(range(idx, idx+lengthgif_words_list))}")
                yield tuple(range(idx, idx+lengthgif_words_list))


#getting start and end timestamps for gif transcript
def get_gif_timestamps(giflist_indxs, words_timestamp):
  print(f"******** Inside get_gif_timestamps() **********")
  #giflist_indxs = list(list(get_gif_word_indexes(words, giflist))[0])
  min_idx = min(giflist_indxs)
  max_idx = max(giflist_indxs)
  print(f"min_idx is :{min_idx}")
  print(f"max_idx is :{max_idx}")
  
  print(f"type of words_timestamp is :{type(words_timestamp)}")
  gif_words_timestamp = words_timestamp[min_idx : max_idx+1]
  print(f"words_timestamp is :{words_timestamp}")
  print(f"gif_words_timestamp is :{gif_words_timestamp}")
  
  start_seconds, end_seconds = gif_words_timestamp[0][0], gif_words_timestamp[-1][-1]
  print(f"start_seconds, end_seconds are :{start_seconds},{end_seconds}")
  
  return start_seconds, end_seconds


#extracting the  video and building and serving a .gif image
def gen_moviepy_gif(start_seconds, end_seconds):
  print("******** inside moviepy_gif () ***************")
  video_path = "./ShiaLaBeouf.mp4"
  video = mp.VideoFileClip(video_path) #.resize(0.3)
  final_clip = video.subclip(start_seconds, end_seconds)
  #final_clip.write_videofile("gifimage.mp4")
  print("I am here now")

  #gifclip = VideoFileClip("gifimage.mp4")
  final_clip.write_gif("./gifimage.gif") #, program='ffmpeg', tempfiles=True, fps=15, fuzz=3)
  final_clip.close()
  
  print("pretty good")
  gif_img = mp.VideoFileClip("gifimage.gif")
  print(gif_img)
  gif_img.write_videofile("gifimage.mp4")
  gif_vid = mp.VideoFileClip("gifimage.mp4")
  
  im = Image.open("gifimage.gif")
  vid_cap = cv2.VideoCapture('gifimage.mp4')

  print("At the very end")
  return im, gif_img, gif_vid, vid_cap


# showing gif
#gif.ipython_display()


sample_video = ['./ShiaLaBeouf.mp4']
sample_vid = gr.Video(label='Video file')  #for displaying the example
examples = gr.components.Dataset(components=[sample_vid], samples=[sample_video], type='values')


demo = gr.Blocks()

with demo:
    gr.Markdown("""This app is still a work in progress..""")
    with gr.Row():
        input_video = gr.Video(label="Upload a Video", visible=True)  #for incoming video
        text_transcript = gr.Textbox(label="Transcripts", lines = 10, interactive = True )  #to generate and display transcriptions for input video
        text_words = gr.Textbox(visible=False)
        text_wordstimestamps = gr.Textbox(visible=False)
        text_gif_transcript = gr.Textbox(label="Transcripts", placeholder="Copy paste transcripts here to create GIF image" , lines = 3, interactive = True ) #to copy paste required gif transcript
        #out_gif = gr.HTML(label="Generated GIF from transcript selected", show_label=True)
        
        examples.render()
        def load_examples(video):  #to load sample video into input_video upon clicking on it
            print("****** inside load_example() ******")
            print("in_video is : ", video[0])
            return video[0]
        
        examples.click(load_examples, examples, input_video) 

    with gr.Row():
        button_transcript = gr.Button("Generate transcripts")
        button_gifs = gr.Button("Create Gif")
        
        #def load_gif():
        #    print("****** inside load_gif() ******")
        #    #created embedding  width='560' height='315' 
        #    html_out = "<img src='./gifimage.gif' />"
        #    print(f"html output is : {html_out}")
        #    return 
    with gr.Row():
        #out_gif = gr.HTML(label="Generated GIF from transcript selected", show_label=True)
        #gr.Markdown(""" [] """)
        out_gif = gr.Video()
        
    button_transcript.click(generate_transcripts, input_video, [text_transcript, text_words, text_wordstimestamps ])
    button_gifs.click(generate_gifs, [text_gif_transcript, text_words, text_wordstimestamps], out_gif )
    
   
demo.launch(debug=True)