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import gradio as gr
import pandas as pd
from transformers import BertForSequenceClassification, Trainer, TrainingArguments, BertTokenizer
from datasets import Dataset
from huggingface_hub import HfApi
class CSVTrainer:
def __init__(self):
self.csv_files = []
self.model_dir = "./Personal"
self.repo_id = "luohoa97/PersonalBot-o"
def upload_and_train(self, csv_files):
if not csv_files:
return "Please upload at least one CSV file."
dataframes = [pd.read_csv(file.name) for file in csv_files]
combined_df = pd.concat(dataframes, ignore_index=True)
combined_df['text'] = combined_df.apply(lambda row: f"{row['Event']} in {row['Location']} on {row['Date']}, {row['Time']}", axis=1)
combined_df['labels'] = pd.factorize(combined_df['Category'])[0]
dataset = Dataset.from_pandas(combined_df[['text', 'labels']])
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
def tokenize_function(examples):
return tokenizer(examples['text'], padding='max_length', truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
tokenized_datasets.set_format("torch", columns=['input_ids', 'attention_mask', 'labels'])
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=len(combined_df['labels'].unique()))
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=16,
per_device_eval_batch_size=64,
warmup_steps=500,
weight_decay=0.01,
logging_dir='./logs',
logging_steps=10,
evaluation_strategy="epoch",
save_strategy="epoch"
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_datasets,
eval_dataset=tokenized_datasets
)
trainer.train()
model.save_pretrained(self.model_dir)
self.upload_to_huggingface()
return "Model uploaded to Hugging Face successfully!"
def upload_to_huggingface(self):
api = HfApi()
try:
api.create_repo(repo_id=self.repo_id)
except Exception as e:
print(f"Repo creation failed: {e}")
model = BertForSequenceClassification.from_pretrained(self.model_dir)
model.push_to_hub(self.repo_id)
trainer = CSVTrainer()
def gradio_interface(file):
return trainer.upload_and_train(file)
iface = gr.Interface(
fn=gradio_interface,
inputs=gr.inputs.File(label="Upload CSV Files", type="file", multiple=True),
outputs="text",
title="CSV Trainer",
description="Upload CSV files for training a BERT model."
)
iface.launch() |