Upload folder using huggingface_hub
Browse files- Dockerfile +1 -1
- gsql_app.py +124 -0
- requirements.txt +4 -2
Dockerfile
CHANGED
@@ -20,4 +20,4 @@ WORKDIR $HOME/app
|
|
20 |
# Copy the current directory contents into the container at $HOME/app setting the owner to the user
|
21 |
COPY --chown=user . $HOME/app
|
22 |
|
23 |
-
|
|
|
20 |
# Copy the current directory contents into the container at $HOME/app setting the owner to the user
|
21 |
COPY --chown=user . $HOME/app
|
22 |
|
23 |
+
RUN python gsql_app.py
|
gsql_app.py
ADDED
@@ -0,0 +1,124 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
sql_query='SELECT * FROM input_table WHERE text LIKE '%rock%''
|
2 |
+
import duckdb
|
3 |
+
import pandas as pd
|
4 |
+
import gradio as gr
|
5 |
+
from datasets import load_dataset
|
6 |
+
import tempfile
|
7 |
+
import re
|
8 |
+
|
9 |
+
|
10 |
+
max_rows = 20
|
11 |
+
df_display_kwargs = dict(
|
12 |
+
wrap = True,
|
13 |
+
max_rows = max_rows,
|
14 |
+
type = "pandas",
|
15 |
+
row_count = 3,
|
16 |
+
col_count = 4,
|
17 |
+
)
|
18 |
+
|
19 |
+
dataset_choices = [
|
20 |
+
"rotten_tomatoes",
|
21 |
+
"sciq",
|
22 |
+
]
|
23 |
+
|
24 |
+
def apply_sql(input_table, sql_query):
|
25 |
+
|
26 |
+
# Use regex to extract the table name from the SQL query
|
27 |
+
match = re.search(r"\bFROM\s+(\w+)", sql_query, re.IGNORECASE)
|
28 |
+
if match:
|
29 |
+
table_name = match.group(1)
|
30 |
+
|
31 |
+
sql_query = sql_query.replace(table_name, "input_table")
|
32 |
+
|
33 |
+
output_df = duckdb.query(sql_query).to_df()
|
34 |
+
|
35 |
+
return output_df
|
36 |
+
|
37 |
+
def display_dataset(dataset_id):
|
38 |
+
|
39 |
+
dataset = load_dataset(dataset_id, split="train")
|
40 |
+
df = dataset.to_pandas()
|
41 |
+
return df, df
|
42 |
+
|
43 |
+
def upload_dataset(dataset_file):
|
44 |
+
|
45 |
+
if dataset_file is None:
|
46 |
+
return None, None
|
47 |
+
|
48 |
+
print(dataset_file.name)
|
49 |
+
|
50 |
+
df = pd.read_csv(dataset_file.name)
|
51 |
+
|
52 |
+
return df, df
|
53 |
+
|
54 |
+
|
55 |
+
def process_dataset(full_dataset, sql_query):
|
56 |
+
input_table = full_dataset
|
57 |
+
output_df = duckdb.query(sql_query).to_df()
|
58 |
+
|
59 |
+
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
|
60 |
+
file_path = temp_file.name
|
61 |
+
output_df.to_csv(file_path)
|
62 |
+
|
63 |
+
return output_df, file_path
|
64 |
+
|
65 |
+
|
66 |
+
theme = gr.themes.Soft(
|
67 |
+
primary_hue="blue",
|
68 |
+
neutral_hue="slate",
|
69 |
+
)
|
70 |
+
|
71 |
+
|
72 |
+
with gr.Blocks(analytics_enabled=False, theme=theme) as demo:
|
73 |
+
full_dataset = gr.State()
|
74 |
+
|
75 |
+
with gr.Column():
|
76 |
+
with gr.Row().style(equal_height=True):
|
77 |
+
|
78 |
+
with gr.Column(variant="panel"):
|
79 |
+
|
80 |
+
with gr.Row():
|
81 |
+
dark_mode_btn = gr.Button("Dark Mode", variant="primary")
|
82 |
+
load_dataset_button = gr.Button("Load HF Dataset", variant="secondary")
|
83 |
+
|
84 |
+
dataset_selector = gr.Dropdown(label="HF Dataset", choices=dataset_choices, value=dataset_choices[0])
|
85 |
+
|
86 |
+
|
87 |
+
with gr.Column(variant="compact"):
|
88 |
+
|
89 |
+
with gr.Row():
|
90 |
+
sql_query_btn = gr.Button("Apply SQL Query", variant="secondary")
|
91 |
+
download_dataset_btn = gr.Button("Download Queried Dataset", variant="primary")
|
92 |
+
|
93 |
+
sql_query_comp = gr.Code(language=None, label="SQL Query", lines=3, value=sql_query)
|
94 |
+
|
95 |
+
with gr.Row().style(equal_height=True):
|
96 |
+
upload_dataset_comp = gr.File(label="Upload Dataset")
|
97 |
+
download_dataset_comp = gr.File(label="Download Dataset")
|
98 |
+
|
99 |
+
with gr.Column(variant="panel"):
|
100 |
+
input_df_display = gr.Dataframe(**df_display_kwargs, label=f"Input Dataframe (Truncated to first {max_rows} Rows)")
|
101 |
+
|
102 |
+
output_df_display = gr.Dataframe(**df_display_kwargs, label=f"Output Dataframe (Truncated to first {max_rows} Rows)")
|
103 |
+
|
104 |
+
load_dataset_button.click(fn=display_dataset, inputs=[dataset_selector], outputs=[input_df_display, full_dataset])
|
105 |
+
upload_dataset_comp.change(fn=upload_dataset, inputs=[upload_dataset_comp], outputs=[input_df_display, full_dataset])
|
106 |
+
|
107 |
+
sql_query_btn.click(fn=apply_sql, inputs=[input_df_display, sql_query_comp], outputs=[output_df_display])
|
108 |
+
|
109 |
+
download_dataset_btn.click(fn=process_dataset, inputs=[full_dataset, sql_query_comp], outputs=[output_df_display, download_dataset_comp])
|
110 |
+
|
111 |
+
toggle_dark_mode_args = dict(
|
112 |
+
fn=None,
|
113 |
+
inputs=None,
|
114 |
+
outputs=None,
|
115 |
+
_js="""() => {
|
116 |
+
if (document.querySelectorAll('.dark').length) {
|
117 |
+
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
|
118 |
+
} else {
|
119 |
+
document.querySelector('body').classList.add('dark');
|
120 |
+
}
|
121 |
+
}""",
|
122 |
+
)
|
123 |
+
demo.load(**toggle_dark_mode_args)
|
124 |
+
dark_mode_btn.click(**toggle_dark_mode_args)
|
requirements.txt
CHANGED
@@ -1,2 +1,4 @@
|
|
1 |
-
gradio==3.
|
2 |
-
|
|
|
|
|
|
1 |
+
gradio==3.33.1
|
2 |
+
pandas>=2.01
|
3 |
+
duckdb>=0.8.0
|
4 |
+
datasets
|