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import os
import gradio as gr
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from youtube_transcript_api import YouTubeTranscriptApi
# def load_data(file_obj):
# """
# Load data from the file object of the gr.File() inputs
# """
# path = file_obj.name
# with open(path, "r") as f:
# data = f.read()
# return data
def preprocessing(data):
texts = list()
i = 0
if len(data) <= i+3000:
texts = data
else:
while len(data[i:]) != 0:
if len(data[i:]) > 3000:
string = str(data[i:i+3000])
texts.append(string)
i = i + 2800
else:
string = str(data[i:])
texts.append(string)
break
return texts
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
peft_model_id = "sooolee/flan-t5-base-cnn-samsum-lora"
config = PeftConfig.from_pretrained(peft_model_id)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, device_map='auto') # load_in_8bit=True,
model = PeftModel.from_pretrained(model, peft_model_id, device_map='auto')
def summarize(video_id):
# transcript = load_data(file_obj)
dict = YouTubeTranscriptApi.get_transcript(video_id)
transcript = ""
for i in range(len(dict)):
transcript += dict[i]['text']
texts = preprocessing(transcript)
inputs = tokenizer(texts, return_tensors="pt", padding=True, )
with torch.no_grad():
output_tokens = model.generate(input_ids=inputs["input_ids"].to(device), max_new_tokens=60, do_sample=True, top_p=0.9)
outputs = tokenizer.batch_decode(output_tokens.detach().cpu().numpy(), skip_special_tokens=True)
return outputs
gr.Interface(
fn=summarize,
title = 'Summarize Transcripts',
# inputs = gr.File(file_types=["text"], label="Upload a text file.", interactive=True),
inputs = gr.Textbox(label="Video_ID", interactive=True),
outputs = gr.Textbox(label="Summary", max_lines=120, interactive=False),
).launch(debug=True)
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