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from langchain.llms import HuggingFacePipeline
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM
from components import caption_chain, tag_chain
from components import pexels, utils
import os, gc
import gradio as gr
model = AutoModelForSeq2SeqLM.from_pretrained("declare-lab/flan-alpaca-gpt4-xl")
tokenizer = AutoTokenizer.from_pretrained("declare-lab/flan-alpaca-gpt4-xl")
pipe = pipeline(
'text2text-generation',
model=model,
tokenizer= tokenizer,
max_length=120
)
local_llm = HuggingFacePipeline(pipeline=pipe)
llm_chain = caption_chain.chain(llm=local_llm)
sum_llm_chain = tag_chain.chain(llm=local_llm)
pexels_api_key = os.getenv('pexels_api_key')
def pred(product_name, orientation):
if orientation == "Shorts/Reels/TikTok (1080 x 1920)":
orientation = "potrait"
height = 1080
width = 1920
elif orientation == "Youtube Videos (1920 x 1080)":
orientation = "landscape"
height = 1920
width = 1080
else :
orientation = "square"
height = 1080
width = 1080
folder_name, sentences = pexels.generate_videos(product_name, pexels_api_key, orientation, height, width, llm_chain, sum_llm_chain)
gc.collect()
utils.combine_videos(folder_name)
return ["\n".join(sentences), os.path.join(folder_name, "Final_Ad_Video.mp4")]
#{'video':os.path.join(folder_name, "Final_Ad_Video.mp4"),
# 'captions':"\n".join(sentences)}
with gr.Blocks() as demo:
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="product name")
captions = gr.Textbox(label="captions")
video = gr.Video()
btn = gr.Button("Submit")
btn.click(pred, inputs=[product_name, dimension], outputs=[captions,video])
demo.launch() |