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from langchain.llms import HuggingFacePipeline |
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import torch |
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from components import pexels, utils |
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import os, gc |
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import gradio as gr |
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from transformers import VitsModel, AutoTokenizer, pipeline |
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import torch |
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from transformers import pipeline |
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os.system("cat /etc/ImageMagick-6/policy.xml | sed 's/none/read,write/g'> /etc/ImageMagick-6/policy.xml") |
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text_cls = pipeline("text-classification", "ivanlau/language-detection-fine-tuned-on-xlm-roberta-base") |
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model = VitsModel.from_pretrained("facebook/mms-tts-ind") |
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tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-ind") |
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model2 = VitsModel.from_pretrained("facebook/mms-tts-eng") |
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tokenizer2 = AutoTokenizer.from_pretrained("facebook/mms-tts-eng") |
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pexels_api_key = os.getenv('pexels_api_key') |
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def pred(product_name, orientation): |
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if orientation == "Shorts/Reels/TikTok (1080 x 1920)": |
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orientation = "potrait" |
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height = 1920 |
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width = 1080 |
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elif orientation == "Youtube Videos (1920 x 1080)": |
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orientation = "landscape" |
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height = 1080 |
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width = 1920 |
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else : |
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orientation = "square" |
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height = 1080 |
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width = 1080 |
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folder_name, sentences, length_speech = pexels.generate_videos(product_name, pexels_api_key, orientation, height, width, model, tokenizer, model2, tokenizer2, text_cls) |
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gc.collect() |
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utils.combine_videos(folder_name, length_speech) |
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vid = os.path.join(folder_name,"Final_Ad_Video.mp4") |
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spe = "x.wav" |
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utils.combine_audio_video(folder_name, vid, spe, sentences, length_speech) |
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return ["\n".join(sentences), os.path.join(folder_name, "new_filename.mp4")] |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" |
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# Content [Video] Generator |
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Create a short video based on your text input using AI |
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### Note : the video generation takes about 2-4 minutes |
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""" |
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) |
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dimension = gr.Dropdown( |
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["Shorts/Reels/TikTok (1080 x 1920)", "Facebook/Youtube Videos (1920 x 1080)", "Square (1080 x 1080)"], |
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label="Video Dimension", info="Choose dimension" |
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) |
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product_name = gr.Textbox(label="text story", lines=15, max_lines=100) |
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captions = gr.Textbox(label="captions") |
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video = gr.Video() |
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btn = gr.Button("Submit") |
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btn.click(pred, inputs=[product_name, dimension], outputs=[captions,video]) |
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demo.launch() |
