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| # app.py | |
| import gradio as gr | |
| from transformers import T5ForConditionalGeneration, T5Tokenizer | |
| import torch | |
| import re | |
| MODEL_NAME = "michaperki/SQLToText" # Replace with your actual model repository | |
| # Load the tokenizer and model from Hugging Face Hub | |
| tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME) | |
| model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model.to(device) | |
| model.eval() | |
| def postprocess_output(text): | |
| """ | |
| Postprocesses the generated text to handle SQL aggregate functions like AVG, COUNT, etc., | |
| and SQL-like conditions such as 'price > 100'. It ensures proper punctuation and capitalization. | |
| """ | |
| # Handle SQL aggregate functions (case-insensitive) | |
| text = re.sub(r'\bAVG\((.*?)\)', r'average \1', text, flags=re.IGNORECASE) | |
| text = re.sub(r'\bCOUNT\(\*\)', 'number of records', text, flags=re.IGNORECASE) | |
| text = re.sub(r'\bSUM\((.*?)\)', r'sum of \1', text, flags=re.IGNORECASE) | |
| text = re.sub(r'\bMAX\((.*?)\)', r'maximum \1', text, flags=re.IGNORECASE) | |
| text = re.sub(r'\bMIN\((.*?)\)', r'minimum \1', text, flags=re.IGNORECASE) | |
| # Handle SQL-like conditions (e.g., 'price > 100') | |
| text = re.sub(r'(\b\w+\b) > (\d+)', r'\1 greater than \2', text, flags=re.IGNORECASE) | |
| text = re.sub(r'(\b\w+\b) < (\d+)', r'\1 less than \2', text, flags=re.IGNORECASE) | |
| text = re.sub(r'(\b\w+\b) >= (\d+)', r'\1 greater than or equal to \2', text, flags=re.IGNORECASE) | |
| text = re.sub(r'(\b\w+\b) <= (\d+)', r'\1 less than or equal to \2', text, flags=re.IGNORECASE) | |
| text = re.sub(r'(\b\w+\b) = (\d+)', r'\1 equal to \2', text, flags=re.IGNORECASE) | |
| # Ensure proper punctuation | |
| if text and text[-1] not in {'.', '?'}: | |
| # If the text ends with a question, keep it; otherwise, add a period | |
| if '?' in text: | |
| text += '?' | |
| else: | |
| text += '.' | |
| # Capitalize the first letter of the sentence | |
| text = text.capitalize() | |
| return text | |
| def translate_sql_to_text(sql_query): | |
| """ | |
| Translates a given SQL query into a natural language question. | |
| """ | |
| try: | |
| if not sql_query.strip(): | |
| return "Please enter a valid SQL query." | |
| # Prepare the input text for the model | |
| input_text = f"Convert the following SQL query to a natural language question: {sql_query}" | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device) | |
| # Generate the translated question | |
| with torch.no_grad(): | |
| generated_ids = model.generate( | |
| input_ids=input_ids, | |
| max_length=128, | |
| num_beams=5, | |
| early_stopping=True | |
| ) | |
| # Decode the generated tokens to a string | |
| translated_question = tokenizer.decode(generated_ids[0], skip_special_tokens=True) | |
| # Apply postprocessing | |
| translated_question = postprocess_output(translated_question) | |
| return translated_question | |
| except Exception as e: | |
| return f"Error during translation: {str(e)}" | |
| # Define the Gradio interface | |
| iface = gr.Interface( | |
| fn=translate_sql_to_text, | |
| inputs=gr.Textbox( | |
| lines=5, | |
| placeholder="Enter your SQL query here...", | |
| label="SQL Query" | |
| ), | |
| outputs=gr.Textbox(label="Translated Question"), | |
| title="SQL to Text Translator", | |
| description="Enter an SQL query, and the model will translate it into a natural language question.", | |
| examples=[ | |
| ["SELECT name FROM employees WHERE department = 'HR';"], | |
| ["SELECT COUNT(*) FROM orders WHERE status = 'shipped';"], | |
| ["SELECT AVG(salary) FROM employees WHERE department = 'Engineering';"], | |
| ["SELECT product_name, price FROM products WHERE price > 100 ORDER BY price DESC LIMIT 5;"], | |
| ["SELECT COUNT(*) FROM orders WHERE status = 'shipped';"] | |
| ], | |
| theme="default" | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |