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import gradio as gr |
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import torch |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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model_path = "./" |
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tokenizer = T5Tokenizer.from_pretrained(model_path) |
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model = T5ForConditionalGeneration.from_pretrained(model_path) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = model.to(device) |
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model.eval() |
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def generate_sql(user_input): |
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inputs = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True).to(device) |
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with torch.no_grad(): |
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outputs = model.generate(**inputs, max_length=512) |
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return tokenizer.decode(outputs[0], skip_special_tokens=True) |
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gr.Interface( |
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fn=generate_sql, |
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inputs=gr.Textbox(lines=15, placeholder="Paste your full SQL prompt here..."), |
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outputs=gr.Textbox(label="Generated SQL Query"), |
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title="HISAB AI – Text2SQL Generator", |
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description="Paste your schema + user query + instructions. Model returns a PostgreSQL query." |
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).launch() |
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