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Update app.py
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app.py
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import os
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os.system('pip install gradio==2.3.5b0')
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os.system('pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+${CUDA}.html')
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import gradio as gr
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from transformers import pipeline
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import pandas as pd
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table = pd.DataFrame()
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tqa = pipeline(task="table-question-answering", model="google/tapas-base-finetuned-wtq")
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def chat(message):
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history = gr.get_state() or []
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global table
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if message.startswith('http'):
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table = pd.read_csv(message)
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table = table.astype(str)
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response = 'thank you to give me a dataset... now you can ask questions about it'
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elif table.empty:
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response = 'Hi! You still have not given me the url of a dataset in csv format. Send a url of a csv file and then ask as many questions as you want about it. If you want to talk about another dataset, just send a new link.'
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else:
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response = tqa(table=table, query=message)["answer"]
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history.append((message, response))
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gr.set_state(history)
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html = "<div class='chatbot'>"
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for user_msg, resp_msg in history:
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html += f"<div class='user_msg'>{user_msg}</div>"
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html += f"<div class='resp_msg'>{resp_msg}</div>"
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html += "</div>"
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return html
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iface = gr.Interface(chat, "text", "html", css="""
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.chatbox {display:flex;flex-direction:column}
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.user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
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.user_msg {background-color:cornflowerblue;color:white;align-self:start}
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.resp_msg {background-color:lightgray;align-self:self-end}
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""", allow_screenshot=False, allow_flagging=False)
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if __name__ == "__main__":
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iface.launch(debug=True)
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