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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a5831e0b-d99b-4f34-a65e-97f5d09f00ec",
"metadata": {},
"outputs": [],
"source": [
"# import required libraries\n",
"from transformers import Pix2StructProcessor, Pix2StructForConditionalGeneration\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6881b277-9511-4460-a0aa-19b8d9e61fdf",
"metadata": {},
"outputs": [],
"source": [
"# pipeline function with default values\n",
"def query(image, user_question):\n",
" \"\"\"\n",
" image: single image or batch of images;\n",
" question: user prompt question;\n",
" \"\"\"\n",
" # select model from hugging face\n",
" model_name = \"google/deplot\"\n",
" # set preprocessor for current model\n",
" processor = Pix2StructProcessor.from_pretrained(model_name)\n",
" # load pre-trained model\n",
" model = Pix2StructForConditionalGeneration.from_pretrained(model_name)\n",
" # process the inputs for prediction\n",
" inputs = processor(images=image, text=user_question, return_tensors=\"pt\")\n",
" # save the results\n",
" predictions = model.generate(**inputs, max_new_tokens=512)\n",
" # save output\n",
" result = processor.decode(predictions[0], skip_special_tokens=True)\n",
" # process the results for output table\n",
" outs = [x.strip() for x in result.split(\"<0x0A>\")]\n",
" # create an empty list\n",
" nested = list()\n",
" # loop for splitting the data\n",
" for data in outs:\n",
" if \"|\" in data:\n",
" nested.append([x.strip() for x in data.split(\"|\")])\n",
" else:\n",
" nested.append(data)\n",
" # return the converted output\n",
" return nested"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "04526adc-1ce4-48c6-b635-13bf506ed862",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cache from 'C:\\Users\\faiza\\huggingface\\Group Project\\gradio_cached_examples\\14' directory. If method or examples have changed since last caching, delete this folder to clear cache.\n",
"\n",
"Running on local URL: http://127.0.0.1:7860\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
}
],
"source": [
"# Interface framework to customize the io page \n",
"ui = gr.Interface(title=\"Chart Q/A\",\n",
" fn=query,\n",
" inputs=[gr.Image(label=\"Upload Here\", type=\"pil\"), gr.Textbox(label=\"Question?\")],\n",
" outputs=\"list\",\n",
" examples=[[\"./samples/sample1.png\", \"Generate underlying data table of the figure\"], \n",
" [\"./samples/sample2.png\", \"Is the sum of all 4 places greater than Laos?\"]],\n",
" # [\"./samples/sample3.webp\", \"What are the 2020 net sales?\"]],\n",
" cache_examples=True,\n",
" allow_flagging='never')\n",
"\n",
"ui.queue(api_open=False)\n",
"ui.launch(inline=False, share=False, debug=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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