Add application file

app.py

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+import time
+import torch
+from transformers import BertForSequenceClassification, AdamW
+from torch.utils.data import DataLoader, TensorDataset
+from transformers import BertTokenizer
+import gradio as gr
+import pandas as pd
+import os
+import spaces
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(device)
+
+model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model.to(device)
+
+optimizer = AdamW(model.parameters(), lr=1e-5)
+
+global_data = None
+
+def load_data(file):
+    global global_data
+    df = pd.read_csv(file)
+    inputs = tokenizer(df['text'].tolist(), padding=True, truncation=True, return_tensors="pt")  # Mã hóa văn bản
+    labels = torch.tensor(df['lable'].tolist()).long()  #
+    global_data = TensorDataset(inputs['input_ids'], inputs['attention_mask'], labels)
+
+    print(global_data)
+
+def get_dataloader(start, end, batch_size=8):
+    global global_data
+    subset = torch.utils.data.Subset(global_data, range(start, end))
+    return DataLoader(subset, batch_size=batch_size)
+
+@spaces.GPU
+def train_batch(dataloader):
+    model.train()
+    start_time = time.time()
+
+    for step, batch in enumerate(dataloader):
+        input_ids, attention_mask, labels = batch
+        input_ids, attention_mask, labels = input_ids.to(device), attention_mask.to(device), labels.to(device)
+
+        optimizer.zero_grad()
+        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
+        loss = outputs.loss
+        loss.backward()
+        optimizer.step()
+
+        elapsed_time = time.time() - start_time
+        if elapsed_time > 55:  # Dừng trước 60 giây để lưu checkpoint
+            torch.save(model.state_dict(), "./checkpoint/model.pt")
+            return False, "Checkpoint saved. Training paused."
+
+    return True, "Batch training completed."
+
+
+def train_step(file=None):
+    if file:
+        load_data(file)
+    print(global_data)
+
+    start_idx = 0
+    batch_size = 8
+    total_samples = len(global_data)
+
+    counting = 0
+    while start_idx < total_samples:
+        print("Step:", counting)
+        counting = counting + 1
+        end_idx = min(start_idx + (batch_size * 10), total_samples)  # 10 batches per loop
+        dataloader = get_dataloader(start_idx, end_idx, batch_size)
+
+        success, message = train_batch(dataloader)
+        if not success:
+            return message
+
+        start_idx = end_idx
+        time.sleep(5)  # Nghỉ 5 giây giữa các phiên huấn luyện
+
+    torch.save(model.state_dict(), "./checkpoint/model.pt")
+    return "Training completed and model saved."
+
+
+if __name__ == "__main__":
+    iface = gr.Interface(
+        fn=train_step,
+        inputs=gr.File(label="Upload CSV"),
+        outputs="text"
+    )
+    iface.launch()