app.py

import os
import re
import time
from pathlib import Path

from relik.retriever import GoldenRetriever

from relik.retriever.indexers.inmemory import InMemoryDocumentIndex
from relik.retriever.indexers.document import DocumentStore
from relik.retriever import GoldenRetriever
from relik.reader.pytorch_modules.span import RelikReaderForSpanExtraction
import requests
import streamlit as st
from spacy import displacy
from streamlit_extras.badges import badge
from streamlit_extras.stylable_container import stylable_container
import logging

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
logger = logging.getLogger()

# RELIK = os.getenv("RELIK", "localhost:8000/api/entities")

import random

from relik.inference.annotator import Relik
from relik.inference.data.objects import (
    AnnotationType,
    RelikOutput,
    Span,
    TaskType,
    Triples,
)

def get_random_color(ents):
    colors = {}
    random_colors = generate_pastel_colors(len(ents))
    for ent in ents:
        colors[ent] = random_colors.pop(random.randint(0, len(random_colors) - 1))
    return colors


def floatrange(start, stop, steps):
    if int(steps) == 1:
        return [stop]
    return [
        start + float(i) * (stop - start) / (float(steps) - 1) for i in range(steps)
    ]


def hsl_to_rgb(h, s, l):
    def hue_2_rgb(v1, v2, v_h):
        while v_h < 0.0:
            v_h += 1.0
        while v_h > 1.0:
            v_h -= 1.0
        if 6 * v_h < 1.0:
            return v1 + (v2 - v1) * 6.0 * v_h
        if 2 * v_h < 1.0:
            return v2
        if 3 * v_h < 2.0:
            return v1 + (v2 - v1) * ((2.0 / 3.0) - v_h) * 6.0
        return v1

    # if not (0 <= s <= 1): raise ValueError, "s (saturation) parameter must be between 0 and 1."
    # if not (0 <= l <= 1): raise ValueError, "l (lightness) parameter must be between 0 and 1."

    r, b, g = (l * 255,) * 3
    if s != 0.0:
        if l < 0.5:
            var_2 = l * (1.0 + s)
        else:
            var_2 = (l + s) - (s * l)
        var_1 = 2.0 * l - var_2
        r = 255 * hue_2_rgb(var_1, var_2, h + (1.0 / 3.0))
        g = 255 * hue_2_rgb(var_1, var_2, h)
        b = 255 * hue_2_rgb(var_1, var_2, h - (1.0 / 3.0))

    return int(round(r)), int(round(g)), int(round(b))


def generate_pastel_colors(n):
    """Return different pastel colours.

    Input:
        n (integer) : The number of colors to return

    Output:
        A list of colors in HTML notation (eg.['#cce0ff', '#ffcccc', '#ccffe0', '#f5ccff', '#f5ffcc'])

    Example:
        >>> print generate_pastel_colors(5)
        ['#cce0ff', '#f5ccff', '#ffcccc', '#f5ffcc', '#ccffe0']
    """
    if n == 0:
        return []

    # To generate colors, we use the HSL colorspace (see http://en.wikipedia.org/wiki/HSL_color_space)
    start_hue = 0.0  # 0=red    1/3=0.333=green   2/3=0.666=blue
    saturation = 1.0
    lightness = 0.9
    # We take points around the chromatic circle (hue):
    # (Note: we generate n+1 colors, then drop the last one ([:-1]) because
    # it equals the first one (hue 0 = hue 1))
    return [
        "#%02x%02x%02x" % hsl_to_rgb(hue, saturation, lightness)
        for hue in floatrange(start_hue, start_hue + 1, n + 1)
    ][:-1]


def set_sidebar(css):
    with st.sidebar:
        st.markdown(f"<style>{css}</style>", unsafe_allow_html=True)
        st.image(
            "https://upload.wikimedia.org/wikipedia/commons/8/87/The_World_Bank_logo.svg",
            use_column_width=True,
        )
        st.markdown("### World Bank")
        st.markdown("### DIME")

def get_el_annotations(response):
    i_link_wrapper = "<link rel='stylesheet' href='https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css'><a href='https://developmentevidence.3ieimpact.org/taxonomy-search-detail/intervention/disaggregated-intervention/{}' style='color: #414141'> <span style='font-size: 1.0em; font-family: monospace'> Intervention {}</span></a>"
    o_link_wrapper = "<link rel='stylesheet' href='https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css'><a href='https://developmentevidence.3ieimpact.org/taxonomy-search-detail/intervention/disaggregated-outcome/{}' style='color: #414141'><span style='font-size: 1.0em; font-family: monospace'> Outcome: {}</span></a>"
    # swap labels key with ents
    ents = [
        {
            "start": l.start,
            "end": l.end,
            "label": i_link_wrapper.format(l.label[0].upper() + l.label[1:].replace("/", "%2").replace(" ", "%20").replace("&","%26"), l.label),
        } if io_map[l.label] == "intervention" else 
        {
            "start": l.start,
            "end": l.end,
            "label": o_link_wrapper.format(l.label[0].upper() + l.label[1:].replace("/", "%2").replace(" ", "%20").replace("&","%26"), l.label), 
        }
        for l in response.spans
    ]
    dict_of_ents = {"text": response.text, "ents": ents}
    label_in_text = set(l["label"] for l in dict_of_ents["ents"])
    options = {"ents": label_in_text, "colors": get_random_color(label_in_text)}
    return dict_of_ents, options



def get_retriever_annotations(response):
    el_link_wrapper = "<link rel='stylesheet' href='https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css'><a href='https://en.wikipedia.org/wiki/{}' style='color: #414141'><i class='fa-brands fa-wikipedia-w fa-xs'></i> <span style='font-size: 1.0em; font-family: monospace'> {}</span></a>"
    # swap labels key with ents
    ents = [l.text
        for l in response.candidates[TaskType.SPAN]
    ]
    dict_of_ents = {"text": response.text, "ents": ents}
    label_in_text = set(l for l in dict_of_ents["ents"])
    options = {"ents": label_in_text, "colors": get_random_color(label_in_text)}
    return dict_of_ents, options
    

def get_retriever_annotations_candidates(text, ents):
    el_link_wrapper = "<link rel='stylesheet' href='https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.2/css/all.min.css'><a href='https://en.wikipedia.org/wiki/{}' style='color: #414141'><i class='fa-brands fa-wikipedia-w fa-xs'></i> <span style='font-size: 1.0em; font-family: monospace'> {}</span></a>"
    # swap labels key with ents
    dict_of_ents = {"text": text, "ents": ents}
    label_in_text = set(l for l in dict_of_ents["ents"])
    options = {"ents": label_in_text, "colors": get_random_color(label_in_text)}
    return dict_of_ents, options

    
import json
io_map = {}
with open("/home/user/app/models/retriever/document_index/documents.jsonl", "r") as r:
    for line in r:
        element = json.loads(line)
        io_map[element["text"]] = element["metadata"]["type"]

            
import json
db_set = set()
with open("models/retriever/intervention/gpt/db/document_index/documents.jsonl", "r") as r:
    for line in r:
        element = json.loads(line)
        db_set.add(element["text"])

with open("models/retriever/outcome/gpt/db/document_index/documents.jsonl", "r") as r:
    for line in r:
        element = json.loads(line)
        db_set.add(element["text"])


@st.cache_resource()
def load_model():

    retriever_question = GoldenRetriever(
        question_encoder="/home/user/app/models/retriever/question_encoder",
        document_index="/home/user/app/models/retriever/document_index/questions"

    )

    retriever_intervention_gpt_taxonomy = GoldenRetriever(
        question_encoder="models/retriever/intervention/gpt+llama/taxonomy/question_encoder",
        document_index="models/retriever/intervention/gpt+llama/taxonomy/document_index"

    )


    
    retriever_intervention_gpt_db = GoldenRetriever(
        question_encoder="models/retriever/intervention/gpt+llama/db/question_encoder",
        document_index="models/retriever/intervention/gpt+llama/db/document_index"

    )

    
    retriever_outcome_gpt_taxonomy = GoldenRetriever(
        question_encoder="models/retriever/outcome/gpt+llama/taxonomy/question_encoder",
        document_index="models/retriever/outcome/gpt+llama/taxonomy/document_index"

    )

    
    retriever_outcome_gpt_db = GoldenRetriever(
        question_encoder="models/retriever/outcome/gpt+llama/db/question_encoder",
        document_index="models/retriever/outcome/gpt+llama/db/document_index"

    )


    reader = RelikReaderForSpanExtraction("/home/user/app/models/small-extended-large-batch",
                                        dataset_kwargs={"use_nme": True})

    relik_question = Relik(reader=reader, retriever=retriever_question, window_size="none", top_k=100, task="span", device="cpu", document_index_device="cpu")

    return [relik_question, retriever_intervention_gpt_db, retriever_outcome_gpt_db, retriever_intervention_gpt_taxonomy, retriever_outcome_gpt_taxonomy]

def set_intro(css):
    # intro

    st.markdown("# ImpactAI")
    st.image(
    "http://35.237.102.64/public/logo.png",
)
    st.markdown(
        "### 3ie taxonomy level 4 Intervention/Outcome candidate retriever with Entity Linking"
    )
    # st.markdown(
    #     "This is a front-end for the paper [Universal Semantic Annotator: the First Unified API "
    #     "for WSD, SRL and Semantic Parsing](https://www.researchgate.net/publication/360671045_Universal_Semantic_Annotator_the_First_Unified_API_for_WSD_SRL_and_Semantic_Parsing), which will be presented at LREC 2022 by "
    #     "[Riccardo Orlando](https://riccorl.github.io), [Simone Conia](https://c-simone.github.io/), "
    #     "[Stefano Faralli](https://corsidilaurea.uniroma1.it/it/users/stefanofaralliuniroma1it), and [Roberto Navigli](https://www.diag.uniroma1.it/navigli/)."
    # )
    
from datetime import datetime
from pathlib import Path
from huggingface_hub import HfApi, CommitScheduler
from uuid import uuid4

# Access token from environment variable
# api = HfApi()
# api.set_access_token(os.getenv("HF_TOKEN"))

JSON_DATASET_DIR = Path("json_demo_selected_io")
JSON_DATASET_DIR.mkdir(parents=True, exist_ok=True)
JSON_DATASET_PATH = JSON_DATASET_DIR / f"train-{uuid4()}.json"

scheduler = CommitScheduler(
    repo_id="demo-retriever",
    repo_type="dataset",
    folder_path=JSON_DATASET_DIR,
    path_in_repo="data",
    token=os.getenv("HF_TOKEN")
)


def write_candidates_to_file(text, candidates, selected_candidates):
    logger.info(f"Text: {text}\tCandidates: {str(candidates)}\tSelected Candidates: {str(selected_candidates)}\n")
    with scheduler.lock:
        with JSON_DATASET_PATH.open("a") as f:
            json.dump({"text": text, "Candidates": [candidate for candidate in candidates], "Selected Candidates": [candidate for candidate in selected_candidates], "datetime": datetime.now().isoformat()}, f)
            f.write("\n")

def run_client():
    with open(Path(__file__).parent / "style.css") as f:
        css = f.read()

    st.set_page_config(
        page_title="ImpactAI",
        page_icon="🦮",
        layout="wide",
    )
    set_sidebar(css)
    set_intro(css)

        # Radio button selection
    analysis_type = st.radio(
        "Choose analysis type:",
        options=["Retriever", "Entity Linking"],
        index=0  # Default to 'question'
    )

    selection_options = ["DB Intervention", "DB Outcome", "Taxonomy Intervention", "Taxonomy Outcome", "Top-k DB in Taxonomy Intervention", "Top-k DB in Taxonmy Outcome", ]
    
    if analysis_type == "Retriever":
        # Selection list using selectbox
        selection_list = st.selectbox(
            "Select an option:",
            options=selection_options
        )

    # text input
    text = st.text_area(
        "Enter Text Below:",
        value="",
        height=200,
        max_chars=1500,
    )

    with stylable_container(
        key="annotate_button",
        css_styles="""
            button {
                background-color: #a8ebff;
                color: black;
                border-radius: 25px;
            }
            """,
    ):
        submit = st.button("Annotate")
    # submit = st.button("Run")

    if "relik_model" not in st.session_state.keys():
        st.session_state["relik_model"] = load_model()
    relik_model = st.session_state["relik_model"][0]

    if 'candidates' not in st.session_state:
        st.session_state['candidates'] = []
    if 'selected_candidates' not in st.session_state:
        st.session_state['selected_candidates'] = []

    # ReLik API call
    if submit:

        if analysis_type == "Entity Linking":
            relik_model = st.session_state["relik_model"][0]
        else:
            model_idx = selection_options.index(selection_list)
            if selection_list == "Top-k DB in Taxonomy Intervention" or selection_list == "Top-k DB in Taxonmy Outcome":
                relik_model = st.session_state["relik_model"][model_idx-1]
            else:
                relik_model = st.session_state["relik_model"][model_idx+1]
            
            
        text = text.strip()
        if text:
            st.markdown("####")
            with st.spinner(text="In progress"):
                if analysis_type == "Entity Linking":
                    response = relik_model(text)
                    
                    dict_of_ents, options = get_el_annotations(response=response)
                    dict_of_ents_candidates, options_candidates = get_retriever_annotations(response=response)

                    st.markdown("#### Entity Linking")

                    display = displacy.render(
                        dict_of_ents, manual=True, style="ent", options=options
                    )
    
    
                    display = display.replace("\n", " ")
    
                    # heurstic, prevents split of annotation decorations
                    display = display.replace("border-radius: 0.35em;", "border-radius: 0.35em; white-space: nowrap;")
    
                    with st.container():
                        st.write(display, unsafe_allow_html=True)
                    candidate_text = "".join(f"<li style='color: black;'>Intervention: {candidate}</li>" if io_map[candidate] == "intervention" else f"<li style='color: black;'>Outcome: {candidate}</li>" for candidate in dict_of_ents_candidates["ents"][0:10])
                    text = """
                    <h2 style='color: black;'>Possible Candidates:</h2>
                    <ul style='color: black;'>
                    """ + candidate_text + "</ul>"
    
                    st.markdown(text, unsafe_allow_html=True)    
                else:
                    if selection_list == "Top-k DB in Taxonomy Intervention" or selection_list == "Top-k DB in Taxonomy Outcome":
                        response = relik_model.retrieve(text, k=50, batch_size=400, progress_bar=False)
                        candidates_text = [pred.document.text for pred in response[0] if pred.document.text in db_set]
                        candidates_text = candidates_text[:10]
                    else:
                        response = relik_model.retrieve(text, k=20, batch_size=400, progress_bar=False)
                        candidates_text = [pred.document.text for pred in response[0]] 
                        
        
                    if candidates_text:
                        st.session_state.candidates = candidates_text

                    else:
                        st.session_state.candidates = []
                        st.session_state.selected_candidates = []
                        st.markdown("<h2 style='color: black;'>No Candidates Found</h2>", unsafe_allow_html=True)
         
        else:
            st.error("Please enter some text.")
    
    # Ensure the candidates list is displayed even after interactions
    if st.session_state.candidates and analysis_type != "Entity Linking":
        dict_of_ents_candidates, options_candidates = get_retriever_annotations_candidates(text, st.session_state.candidates)
        st.markdown("<h2 style='color: black;'>Possible Candidates:</h2>", unsafe_allow_html=True)
        for candidate in dict_of_ents_candidates["ents"]:
            checked = candidate in st.session_state.selected_candidates
            if st.checkbox(candidate, key=candidate, value=checked):
                if candidate not in st.session_state.selected_candidates:
                    st.session_state.selected_candidates.append(candidate)
            else:
                if candidate in st.session_state.selected_candidates:
                    st.session_state.selected_candidates.remove(candidate)
    
        if st.button("Save Selected Candidates"):
            if write_candidates_to_file(text, dict_of_ents_candidates["ents"], st.session_state.selected_candidates):
                st.success("Selected candidates have been saved to file.")

 
if __name__ == "__main__":
    run_client()