{result[250:]}
IMDB search
""") + with gr.Row(): + with gr.Column(scale=1): + result_list = gr.Dataframe(type="array", visible=False, col_count=1) + with gr.Column(scale=13): + query = gr.Textbox(lines=1, max_lines=1, placeholder="Searchโฆ", label="") + with gr.Column(scale=1): + with gr.Row(scale=1): + pass + with gr.Row(scale=1): + submit_btn = gr.Button("๐", elem_id="b").style(full_width=False) + with gr.Row(scale=1): + pass + + with gr.Row(): + with gr.Column(scale=1): + pass + with gr.Column(scale=13): + c = gr.HTML(label="Results") + with gr.Row(): + # left = gr.Button(value="โ", elem_id="b", visible=False).style(full_width=True) + page_1 = gr.Button(value="1", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True) + page_2 = gr.Button(value="2", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True) + page_3 = gr.Button(value="3", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True) + page_4 = gr.Button(value="4", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True) + page_5 = gr.Button(value="5", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True) + page_6 = gr.Button(value="6", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True) + page_7 = gr.Button(value="7", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True) + page_8 = gr.Button(value="8", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True) + page_9 = gr.Button(value="9", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True) + page_10 = gr.Button(value="10", elem_id="b", visible=PAGINATION_VISIBLE).style(full_width=True) + # right = gr.Button(value="โถ", elem_id="b", visible=False).style(full_width=True) + with gr.Column(scale=1): + pass + query.submit(fn=page_0, inputs=[query], outputs=[c, result_list]) + submit_btn.click(page_0, inputs=[query], outputs=[c, result_list]) + with gr.Box(visible=False): + nums = [gr.Number(i, visible=False, precision=0) for i in range(NUM_PAGES)] + page_1.click(fn=page_i, inputs=[nums[0], result_list], outputs=[c, result_list]) + page_2.click(fn=page_i, inputs=[nums[1], result_list], outputs=[c, result_list]) + page_3.click(fn=page_i, inputs=[nums[2], result_list], outputs=[c, result_list]) + page_4.click(fn=page_i, inputs=[nums[3], result_list], outputs=[c, result_list]) + page_5.click(fn=page_i, inputs=[nums[4], result_list], outputs=[c, result_list]) + page_6.click(fn=page_i, inputs=[nums[5], result_list], outputs=[c, result_list]) + page_7.click(fn=page_i, inputs=[nums[6], result_list], outputs=[c, result_list]) + page_8.click(fn=page_i, inputs=[nums[7], result_list], outputs=[c, result_list]) + page_9.click(fn=page_i, inputs=[nums[8], result_list], outputs=[c, result_list]) + page_10.click(fn=page_i, inputs=[nums[9], result_list], outputs=[c, result_list]) +demo.launch(enable_queue=True, debug=True) \ No newline at end of file diff --git a/data/.gitkeep b/data/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 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{http://www.aclweb.org/anthology/P11-1015}\n}\n", + "config_name": "plain_text", + "dataset_size": 133190302, + "description": "Large Movie Review Dataset.\nThis is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.", + "download_checksums": { + "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz": { + "num_bytes": 84125825, + "checksum": "c40f74a18d3b61f90feba1e17730e0d38e8b97c05fde7008942e91923d1658fe" + } + }, + "download_size": 84125825, + "features": { + "text": { + "dtype": "string", + "_type": "Value" + }, + "docid": { + "dtype": "int64", + "_type": "Value" + } + }, + "homepage": "http://ai.stanford.edu/~amaas/data/sentiment/", + "license": "", + "size_in_bytes": 217316127, + "splits": { + "train": { + "name": "train", + "num_bytes": 33432823, + "num_examples": 25000, + "dataset_name": "imdb" + }, + "test": { + "name": "test", + "num_bytes": 32650685, + "num_examples": 25000, + "dataset_name": "imdb" + }, + "unsupervised": { + "name": "unsupervised", + "num_bytes": 67106794, + "num_examples": 50000, + "dataset_name": "imdb" + } + }, + "task_templates": [], + "version": { + "version_str": "1.0.0", + "description": "", + "major": 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