Update app.py

app.py

@@ -1,26 +1,51 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
-model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-wikiSQL")
-
-def generate_sql(query):
-    input_text = "translate English to SQL: " + query
-    inputs = tokenizer(input_text, return_tensors="pt", padding=True)
-    outputs = model.generate(**inputs, max_length=512)
-    sql_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return sql_query
-
-# Create a Gradio interface
-interface = gr.Interface(
-    fn=generate_sql,
-    inputs=gr.Textbox(lines=2, placeholder="Enter your natural language query here..."),
-    outputs="text",
-    title="NL to SQL with T5",
-    description="This model converts natural language queries into SQL. Enter your query!"
-)
-
-# Launch the app
-if __name__ == "__main__":
-    interface.launch()
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration, AdamW
+from torch.utils.data import DataLoader, Dataset
+from tqdm import tqdm
+
+# Define your dataset class
+class SpiderDataset(Dataset):
+    def __init__(self, encodings, labels):
+        self.encodings = encodings
+        self.labels = labels
+
+    def __getitem__(self, idx):
+        return {'input_ids': self.encodings[idx], 'labels': self.labels[idx]}
+
+    def __len__(self):
+        return len(self.encodings)
+
+# Load your preprocessed Spider dataset
+train_encodings = # Your preprocessed input encodings for training
+train_labels = # Your preprocessed labels for training
+
+# Create a PyTorch dataset and dataloader
+train_dataset = SpiderDataset(train_encodings, train_labels)
+train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)
+
+# Load the pre-trained T5 model
+model = T5ForConditionalGeneration.from_pretrained('t5-base')
+tokenizer = T5Tokenizer.from_pretrained('t5-base')
+
+# Move the model to the GPU if available
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+
+# Set up the optimizer
+optimizer = AdamW(model.parameters(), lr=5e-5)
+
+# Fine-tune the model
+model.train()
+for epoch in range(3):  # Number of epochs
+    for batch in tqdm(train_loader):
+        optimizer.zero_grad()
+        input_ids = batch['input_ids'].to(device)
+        labels = batch['labels'].to(device)
+        outputs = model(input_ids=input_ids, labels=labels)
+        loss = outputs.loss
+        loss.backward()
+        optimizer.step()
+
+# Save the fine-tuned model
+model.save_pretrained('your_model_directory')
+tokenizer.save_pretrained('your_model_directory')