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| import json | |
| import gradio as gr | |
| import os | |
| import random | |
| # load data | |
| folder_path="./data/fid-light-xl/" | |
| tasks = [ | |
| "fever", | |
| "hotpotqa", | |
| "nq", | |
| "trivia_qa", | |
| "structured_zeroshot", | |
| "trex", | |
| ] | |
| labels = [ | |
| "FEVER", | |
| "HotpotQA", | |
| "NQ", | |
| "TriviaQA", | |
| "zsRE", | |
| "T-REx", | |
| ] | |
| modes = ["Perfect (Both text & provenance are correct)", "Double Failure (Both text & provenance are wrong)", | |
| "Correct Text, Wrong Provenance (R-Precision < 1)", "Wrong Text, Correct Provenance (R-Precision == 1)"] | |
| modes_map = {modes[0]:"perfect", modes[1]:"double_failure", modes[2]:"wrong_passage", modes[3]:"wrong_text"} | |
| data = {} | |
| total_num_per_task = {} | |
| for task in tasks: | |
| data[task] = {} | |
| total_num_per_task[task] = 0 | |
| for mode in modes: | |
| data[task][mode] = [] | |
| with open(os.path.join(folder_path,"examples_"+task+"_"+modes_map[mode]+".json")) as f: | |
| data[task][mode] = json.load(f) | |
| total_num_per_task[task] += len(data[task][mode]) | |
| def render_examples(selected_mode): | |
| all_rendered=[] | |
| for i,task in enumerate(tasks): | |
| examples = random.sample(data[task][selected_mode], 10) | |
| rendered_examples = "## Statistics\nNumber of examples in this category: **"+str(len(data[task][selected_mode]))+"** ("+ \ | |
| str(round(len(data[task][selected_mode])/total_num_per_task[task]*100,2)) +"%)\n\n----\n" | |
| for example in examples: | |
| def render_prov(provenance): | |
| t="" | |
| for i, prov in enumerate(provenance): | |
| t+=f"**{i+1})** {prov['text']}\n\n" | |
| if len(provenance) == 0: | |
| t+="<< No provenance returned >>\n\n" | |
| return t | |
| rendered_examples += "#### Query \n"+example["query"]+ "\n"+ \ | |
| "#### Target Text"+ "\n" + "\n\n".join(example["target_text"])+ "\n" | |
| if "target_provenance" in example: | |
| rendered_examples+="#### Target Provenance"+ "\n"+ render_prov(example["target_provenance"])+ "\n" | |
| rendered_examples += "#### Output Text"+ "\n"+ str(example["output_text"])+ "\n"+ \ | |
| "#### Output Provenance"+ "\n"+ render_prov(example["output_provenance"])+ "\n"+ \ | |
| "\n----\n" | |
| all_rendered.append(rendered_examples) | |
| return all_rendered | |
| with gr.Blocks() as interface: | |
| gr.Markdown( | |
| "# FiD-Light Output Explorer \n"+ | |
| "This is a random data output explorer for the retrieval augmented generation model FiD-Light on six KILT tasks (showing static dev set results).\n\n"+ | |
| "*FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation \nSebastian Hofstätter, Jiecao Chen, Karthik Raman, Hamed Zamani* \n[https://arxiv.org/abs/2209.14290](https://arxiv.org/abs/2209.14290) \n\n" + | |
| "*Every time you click on a result split we load up a new set of 10 random examples from this split for all the tasks.*") | |
| #with gr.Accordion("Open for More!"): | |
| # gr.Markdown("Look at me...") | |
| #with gr.Accordion("Open for More!"): | |
| # gr.Markdown("Look at me...") | |
| selected = gr.Radio(modes, value=modes[0], label="Result Split",interactive=True) | |
| text_fields = [] | |
| init_data = render_examples(selected.value) | |
| for i,task in enumerate(tasks): | |
| with gr.Tab(labels[i]): | |
| text_fields.append(gr.Markdown(init_data[i])) | |
| selected.change(fn=render_examples, inputs=selected, outputs=text_fields) | |
| interface.launch() |