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from langchain.llms import HuggingFacePipeline |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM |
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from components import caption_chain, tag_chain |
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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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model = AutoModelForSeq2SeqLM.from_pretrained("declare-lab/flan-alpaca-large") |
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tokenizer = AutoTokenizer.from_pretrained("declare-lab/flan-alpaca-large") |
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pipe = pipeline( |
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'text2text-generation', |
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model=model, |
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tokenizer= tokenizer, |
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max_length=120 |
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) |
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local_llm = HuggingFacePipeline(pipeline=pipe) |
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llm_chain = caption_chain.chain(llm=local_llm) |
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sum_llm_chain = tag_chain.chain(llm=local_llm) |
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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 = pexels.generate_videos(product_name, pexels_api_key, orientation, height, width, llm_chain, sum_llm_chain) |
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gc.collect() |
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utils.combine_videos(folder_name) |
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return ["\n".join(sentences), os.path.join(folder_name, "Final_Ad_Video.mp4")] |
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with gr.Blocks() as demo: |
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gr.Markdown( |
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""" |
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# Ads Generator |
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Create video ads based on your product name 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="product name") |
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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() |
