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import os
import time
import csv
import datetime
import gradio
import schedule
from gradio import utils
import huggingface_hub
from pathlib import Path
from src.models.bert import BERTClassifier
from src.utils.utilities import Utility
model = BERTClassifier(model_name='jeevavijay10/nlp-goemotions-bert')
classes = Utility().read_emotion_list()
hf_token = os.getenv("HF_TOKEN")
dataset_dir = "logs"
headers = ["input", "output", "timestamp", "elapsed"]
repo = huggingface_hub.Repository(
local_dir=dataset_dir,
clone_from="https://huggingface.co/datasets/jeevavijay10/senti-pred-gradio",
token=hf_token,
)
repo.git_pull(lfs=True)
def log_record(vals):
log_file = Path(dataset_dir) / "data.csv"
is_new = not Path(log_file).exists()
with open(log_file, "a", newline="", encoding="utf-8") as csvfile:
writer = csv.writer(csvfile)
if is_new:
writer.writerow(utils.sanitize_list_for_csv(headers))
writer.writerow(utils.sanitize_list_for_csv(vals))
schedule.run_pending()
print(f"Last Sync: {job.last_run}")
def predict(sentence):
timestamp = datetime.datetime.now().isoformat()
start_time = time.time()
predictions = model.evaluate([sentence])
elapsed_time = time.time() - start_time
output = classes[predictions[0]]
print(f"Sentence: {sentence} \nPrediction: {predictions[0]} - {output}")
log_record([sentence, output, timestamp, str(elapsed_time)])
return output
def sync_logs():
print(f"Repo Clean: {repo.is_repo_clean()}")
if not repo.is_repo_clean():
repo.git_add()
repo.git_commit()
repo.git_pull(lfs=True)
result = repo.git_push()
# result = repo.push_to_hub()
print(result)
job = schedule.every(5).minutes.do(sync_logs)
print("Scheduler engaged")
gradio.Interface(
fn=predict,
inputs="text",
outputs="text",
allow_flagging='never'
).launch()
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