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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import the function in main.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"sys.path.append(os.path.dirname(os.path.dirname(os.getcwd())))\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"from climateqa.engine.talk_to_data.main import ask_vanna\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a human query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# query = \"Compare the winter and summer precipitation in 2050 in Marseille\"\n",
"# query = \"What is the impact of climate in Bordeaux?\"\n",
"# query = \"what is the number of days where the temperature above 35 in 2050 in Marseille\"\n",
"# query = \"Quelle sera la température à Marseille sur les prochaines années ?\"\n",
"# query = \"Comment vont évoluer les températures à Marseille ?\"\n",
"query = \"Comment vont évoluer les températures à marseille ?\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Call the function ask vanna, it gives an output of a the sql query and the dataframe of the result (tuple)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sql_query, df, fig = ask_vanna(query)\n",
"print(df.head())\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "climateqa",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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