| import torch | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| import gradio as gr | |
| tokenizer = T5Tokenizer.from_pretrained('t5-small') | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = T5ForConditionalGeneration.from_pretrained('cssupport/t5-small-awesome-text-to-sql') | |
| model = model.to(device) | |
| model.eval() | |
| def generate_sql(input_prompt): | |
| inputs = tokenizer(input_prompt, padding=True, truncation=True, return_tensors="pt").to(device) | |
| with torch.no_grad(): | |
| outputs = model.generate(**inputs, max_length=512) | |
| generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return generated_sql | |
| def gradio_interface(tables, query): | |
| input_prompt = f"tables:\n{tables}\nquery for:{query}" | |
| return generate_sql(input_prompt) | |
| iface = gr.Interface( | |
| fn=gradio_interface, | |
| inputs=[ | |
| gr.Textbox(lines=5, label="Context Tables", placeholder="Enter table definitions here..."), | |
| gr.Textbox(lines=2, label="Query Description", placeholder="Enter your SQL query here...") | |
| ], | |
| outputs=gr.Textbox(label="Generated SQL Query", placeholder=""), | |
| title="Text to SQL Generator", | |
| examples=[ | |
| ["CREATE TABLE student_course_attendance (student_id VARCHAR); CREATE TABLE students (student_id VARCHAR);", "List the id of students who never attends courses?"] | |
| ] | |
| ) | |
| iface.launch() | |