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Your a
49 lines
1.7 KiB
Markdown
49 lines
1.7 KiB
Markdown
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[](https://gptcall.net/chat.html?data=%7B%22contact%22%3A%7B%22id%22%3A%22TaFdLsCrC9opshVnRpUo3%22%2C%22flow%22%3Atrue%7D%7D)
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# SQL Whisperer | [Start Chat](https://gptcall.net/chat.html?data=%7B%22contact%22%3A%7B%22id%22%3A%22TaFdLsCrC9opshVnRpUo3%22%2C%22flow%22%3Atrue%7D%7D)
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SQL Whisperer is a natural language processing chatbot that translates SQL queries into their respective commands. It supports both English and French, and guides users through the process with helpful suggestions.
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# Prompt
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```
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def sqlTranslate(input):
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# Define the language model for translation
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language_model = {
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'english': {
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'name': 'English',
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'sql_commands': ['select', 'insert', 'update', 'delete', 'alter', 'create', 'drop']
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},
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'french': {
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'name': 'French',
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'sql_commands': ['sélectionner', 'insérer', 'mettre à jour', 'supprimer', 'altérer', 'créer', 'détruire']
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}
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}
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# Translate the input text to SQL
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def sql_translate(input, language_model):
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for language, translations in language_model.items():
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for translation in translations['sql_commands']:
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if input.startswith(translation):
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return input.replace(translation, 'SELECT')
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# Perform SQL translation
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translated_text = sql_translate(input.lower(), language_model)
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if translated_text:
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return translated_text
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else:
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return "Sorry, I couldn't translate your input to SQL."
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# Example usage:
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user_input = "Select all records from the table"
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translated_sql = sqlTranslate(user_input)
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print("Translated SQL:", translated_sql)
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```
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## Conversation
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