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  1. Dockerfile +12 -0
  2. app.py +32 -0
  3. requirements.txt +5 -0
Dockerfile ADDED
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+ FROM python:3.7-slim
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+
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+ WORKDIR /usr/src/tapas
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+
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+ COPY requirements.txt ./
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+
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+ RUN pip install -r requirements.txt \
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+ && rm -rf /root/.cache/pip
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+
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+ COPY . .
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+
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+ ENTRYPOINT ["python", "app.py"]
app.py ADDED
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+ from app.tapex import execute_query
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+ import gradio as gr
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+
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+
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+ def main():
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+ description = "Querying a csv using TAPEX model. You can ask a question about tabular data. TAPAS model " \
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+ "will produce the result. Finetuned TAPEX model runs on max 5000 rows and 20 columns data. " \
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+ "A sample data of shopify store sales is provided"
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+
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+ article = "<p style='text-align: center'><a href='https://unscrambl.com/' target='_blank'>Unscrambl</a> | <a href='https://huggingface.co/google/tapas-base-finetuned-wtq' target='_blank'>TAPAS Model</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=abaranovskij_tablequery' alt='visitor badge'></center>"
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+
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+ iface = gr.Interface(fn=execute_query,
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+ inputs=[gr.Textbox(label="Search query"),
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+ gr.File(label="CSV file")],
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+ outputs=[gr.JSON(label="Result"),
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+ gr.Dataframe(label="All data")],
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+ examples=[
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+ ["What is the highest order_amount?", "shopify.csv"],
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+ ["Which user_id has the highest order_amount?", "shopify.csv"],
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+ ["Which payment method was used the most?", "shopify.csv"]
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+ ],
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+ title="Table Question Answering (TAPEX)",
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+ description=description,
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+ article=article,
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+ allow_flagging='never')
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+ # Use this config when running on Docker
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+ # iface.launch(server_name="0.0.0.0", server_port=7000)
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+ iface.launch(enable_queue=True)
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+
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+
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+ if __name__ == "__main__":
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+ main()
requirements.txt ADDED
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+ gradio==3.15.0
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+ torch
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+ transformers==4.25.1
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+ tensorflow_probability
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+ jinja2