app.py

from langchain.llms import HuggingFacePipeline
import torch

from components import pexels, utils
import os, gc
import gradio as gr
from transformers import VitsModel, AutoTokenizer, pipeline
import torch
from transformers import pipeline

text_cls = pipeline("text-classification", "ivanlau/language-detection-fine-tuned-on-xlm-roberta-base")

model = VitsModel.from_pretrained("facebook/mms-tts-ind")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-ind")

model2 = VitsModel.from_pretrained("facebook/mms-tts-eng")
tokenizer2 = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")


pexels_api_key = os.getenv('pexels_api_key')

def pred(product_name, orientation):
    if orientation == "Shorts/Reels/TikTok (1080 x 1920)":
        orientation = "potrait"
        height = 1920
        width = 1080
    elif orientation == "Youtube Videos (1920 x 1080)":
        orientation = "landscape"
        height = 1080
        width = 1920
    else :
        orientation = "square"
        height = 1080
        width = 1080
    folder_name, sentences, length_speech = pexels.generate_videos(product_name, pexels_api_key, orientation, height, width, model, tokenizer, model2, tokenizer2, text_cls)
    gc.collect()
    utils.combine_videos(folder_name, length_speech)

    vid = os.path.join(folder_name,"Final_Ad_Video.mp4")
    spe = "x.wav"

    utils.combine_audio_video(folder_name, vid, spe, sentences, length_speech)
    return ["\n".join(sentences), os.path.join(folder_name, "new_filename.mp4")]
        #{'video':os.path.join(folder_name, "Final_Ad_Video.mp4"),
       # 'captions':"\n".join(sentences)}
    

with gr.Blocks() as demo:
    gr.Markdown(
        """
        # Content [Video] Generator
        Create a short video based on your text input using AI
        ### Note : the video generation takes about 2-4 minutes 
        """
    )
    dimension = gr.Dropdown(
            ["Shorts/Reels/TikTok (1080 x 1920)", "Facebook/Youtube Videos (1920 x 1080)", "Square (1080 x 1080)"], 
            label="Video Dimension", info="Choose dimension"
        )
    product_name = gr.Textbox(label="text story", lines=15, max_lines=100)
    captions = gr.Textbox(label="captions")
    video = gr.Video()
    btn = gr.Button("Submit")
    btn.click(pred, inputs=[product_name, dimension], outputs=[captions,video])
    
    

demo.launch()