Curranj/GPT-SQL

1import openai2import gradio as gr3import pandas as pd4import sqlite35import os6openai.api_key = os.environ["Secret"]7 8#OpenAi call9def gpt3(texts):10    response = openai.Completion.create(11      engine="code-davinci-002",12      prompt= texts,13          temperature=0,14          max_tokens=750,15          top_p=1,16          frequency_penalty=0.0,17          presence_penalty=0.0,18          stop = (";", "/*", "</code>")19    )20    x = response.choices[0].text 21    22    return x23 24# Function to elicit sql response from model25 26 27 28# Function to elicit sql response from model29 30 31def greet(prompt, file = None):32 33    #get the file path from the file object34    file_path = file.name35 36    # read the file and get the column names37    if file_path:38        if file_path.endswith(".csv"):39            df = pd.read_csv(file_path)40            columns = " ".join(df.columns)41            42 43 44 45        elif file_path.endswith((".xls", ".xlsx")):46            df = pd.read_excel(file_path)47            columns = " ".join(df.columns)48        else:49            return "Invalid file type. Please provide a CSV or Excel file."50        51        # create a SQLite database in memory52        con = sqlite3.connect(":memory:")53        # extract the table name so it can be used in the SQL query54        # in order to get the table name, we need to remove the file extension55 56        table_name = os.path.splitext(os.path.basename(file_path.name))[0]57 58 59 60 61        62 63 64 65        # write the DataFrame to a SQL table66 67 68 69        df.to_sql(table_name, con)70    else:71        return "Please upload a file."72    txt= (f'''/*Prompt: {prompt}\nColumns: {columns}\nTable: {table_name}*/ \n —-SQL Code:\n''')73    sql = gpt3(txt)74    75    76    # execute the SQL query77    if con:78        df = pd.read_sql_query(sql, con)79        return sql, df80    else:81        return sql, None82        83 84 85 86 87 88 89#Code to set up Gradio UI90iface = gr.Interface(greet, 91                     inputs = ["text", ("file")], 92                     outputs = ["text",gr.Dataframe(type="pandas")],93                     title="Natural Language to SQL", 94                     description="Enter any prompt and get a SQL statement back! For better results, give it more context")95iface.launch()96