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 import os 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(): folder_name, sentences = pexels.generate_videos("Bluetooth Earphone", pexel_api_key, 1920, 1080) pexels.combine_videos(folder_name) return { 'video':folder_name, 'captions':sentences.join("\n") } with gr.Blocks() as demo: textbox = gr.Textbox("Product Name") captions = gr.Textbox() video = gr.Video() btn = gr.Button("Submit") btn.click(pred, inputs=textbox, outputs=[captions,video]) demo.launch()