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Browse files- Dockerfile +1 -1
- __pycache__/query.cpython-310.pyc +0 -0
- gsql_app.py +14 -21
- query.py +3 -0
- requirements.txt +4 -2
Dockerfile
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@@ -20,4 +20,4 @@ WORKDIR $HOME/app
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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-
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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RUN python gsql_app.py
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__pycache__/query.cpython-310.pyc
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Binary file (208 Bytes). View file
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gsql_app.py
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@@ -1,18 +1,16 @@
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WHERE text LIKE '%curls%';''';
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import duckdb
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import pandas as pd
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import gradio as gr
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from datasets import load_dataset
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import tempfile
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import
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max_rows = 20
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df_display_kwargs = dict(
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wrap = True,
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max_rows = max_rows,
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type = "pandas",
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row_count = 3,
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col_count = 4,
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)
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@@ -24,13 +22,6 @@ dataset_choices = [
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def apply_sql(input_table, sql_query):
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# Use regex to extract the table name from the SQL query
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match = re.search(r"\bFROM\s+(\w+)", sql_query, re.IGNORECASE)
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if match:
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table_name = match.group(1)
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sql_query = sql_query.replace(table_name, "input_table")
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output_df = duckdb.query(sql_query).to_df()
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return output_df
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@@ -39,18 +30,18 @@ def display_dataset(dataset_id):
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dataset = load_dataset(dataset_id, split="train")
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df = dataset.to_pandas()
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def upload_dataset(dataset_file):
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if dataset_file is None:
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return None, None
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df = pd.read_csv(dataset_file.name)
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return df, df
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def process_dataset(full_dataset, sql_query):
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@@ -69,8 +60,7 @@ theme = gr.themes.Soft(
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neutral_hue="slate",
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)
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-
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with gr.Blocks(analytics_enabled=False, theme=theme) as demo:
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full_dataset = gr.State()
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with gr.Column():
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@@ -123,3 +113,6 @@ with gr.Blocks(analytics_enabled=False, theme=theme) as demo:
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)
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demo.load(**toggle_dark_mode_args)
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dark_mode_btn.click(**toggle_dark_mode_args)
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import duckdb
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import pandas as pd
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import gradio as gr
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from datasets import load_dataset
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import tempfile
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from query import sql_query
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max_rows = 20
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max_cols = None
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df_display_kwargs = dict(
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wrap = True,
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row_count = 3,
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col_count = 4,
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)
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def apply_sql(input_table, sql_query):
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output_df = duckdb.query(sql_query).to_df()
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return output_df
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dataset = load_dataset(dataset_id, split="train")
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df = dataset.to_pandas()
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display_df = df.iloc[:max_rows, :max_cols]
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return display_df, df
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def upload_dataset(dataset_file):
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if dataset_file is None:
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return None, None
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df = pd.read_csv(dataset_file.name).iloc[:max_rows, :max_cols]
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display_df = df.iloc[:max_rows, :max_cols]
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return display_df, df
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def process_dataset(full_dataset, sql_query):
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neutral_hue="slate",
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)
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with gr.Blocks(theme=theme) as demo:
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full_dataset = gr.State()
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with gr.Column():
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)
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demo.load(**toggle_dark_mode_args)
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dark_mode_btn.click(**toggle_dark_mode_args)
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if __name__ == "__main__":
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demo.launch(server_name="0.0.0.0", server_port=7860)
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query.py
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sql_query='''
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SELECT * FROM input_table WHERE text LIKE '%the rock%'
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'''
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requirements.txt
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gradio==3.
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gradio==3.33.1
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pandas>=2.0
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duckdb>=0.8.0
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datasets
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