app.py

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()