app.py

from transformers import pipeline
from functools import partial

import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns

from sentence_transformers import SentenceTransformer
import torch
import tqdm

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
import penman
from collections import Counter, defaultdict
import networkx as nx
from networkx.drawing.nx_agraph import pygraphviz_layout

from transformers import pipeline
from functools import partial

import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns

from sentence_transformers import SentenceTransformer
import torch
import tqdm

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
import penman
from collections import Counter, defaultdict
import networkx as nx
from networkx.drawing.nx_agraph import pygraphviz_layout

class FramingLabels:
    def __init__(self, base_model, candidate_labels, batch_size=16):
        device = "cuda:0" if torch.cuda.is_available() else "cpu"
        self.base_pipeline = pipeline("zero-shot-classification", model=base_model, device=device)
        self.candidate_labels = candidate_labels
        self.classifier = partial(self.base_pipeline, candidate_labels=candidate_labels, multi_label=True, batch_size=batch_size)

    def order_scores(self, dic):
        indices_order = [dic["labels"].index(l) for l in self.candidate_labels]
        scores_ordered = np.array(dic["scores"])[indices_order].tolist()
        return scores_ordered

    def get_ordered_scores(self, sequence_to_classify):
        if type(sequence_to_classify) == list:
            res = []
            for out in tqdm.tqdm(self.classifier(sequence_to_classify)):
                res.append(out)
        else:
            res = self.classifier(sequence_to_classify)
        if type(res) == list:
            scores_ordered = list(map(self.order_scores, res))
            scores_ordered = list(map(list, zip(*scores_ordered)))  # reorder
        else:
            scores_ordered = self.order_scores(res)
        return scores_ordered

    def get_label_names(self):
        label_names = [l.split(":")[0].split(" ")[0] for l in self.candidate_labels]
        return label_names

    def __call__(self, sequence_to_classify):
        scores = self.get_ordered_scores(sequence_to_classify)
        label_names = self.get_label_names()
        return dict(zip(label_names, scores))

    def visualize(self, name_to_score_dict, threshold=0.5, **kwargs):
        fig, ax = plt.subplots()

        cp = sns.color_palette()

        scores_ordered = list(name_to_score_dict.values())
        label_names = list(name_to_score_dict.keys())

        colors = [cp[0] if s > 0.5 else cp[1] for s in scores_ordered]
        ax.barh(label_names[::-1], scores_ordered[::-1], color=colors[::-1], **kwargs)

        return fig, ax

class FramingDimensions:
    def __init__(self, base_model, dimensions, pole_names):
        self.encoder = SentenceTransformer(base_model)
        self.dimensions = dimensions
        self.dim_embs = self.encoder.encode(dimensions)
        self.pole_names = pole_names
        self.axis_names = list(map(lambda x: x[0] + "/" + x[1], pole_names))
        axis_embs = []
        for pole1, pole2 in pole_names:
            p1 = self.get_dimension_names().index(pole1)
            p2 = self.get_dimension_names().index(pole2)
            axis_emb = self.dim_embs[p1] - self.dim_embs[p2]
            axis_embs.append(axis_emb)
        self.axis_embs = np.stack(axis_embs)

    def get_dimension_names(self):
        dimension_names = [l.split(":")[0].split(" ")[0] for l in self.dimensions]
        return dimension_names

    def __call__(self, sequence_to_align):
        embs = self.encoder.encode(sequence_to_align)
        scores = embs @ self.axis_embs.T
        named_scores = dict(zip(self.pole_names, scores.T))
        return named_scores

    def visualize(self, align_scores_df, **kwargs):
        name_left = align_scores_df.columns.map(lambda x: x[1])
        name_right = align_scores_df.columns.map(lambda x: x[0])
        bias = align_scores_df.mean()
        color = ["b" if x > 0 else "r" for x in bias]
        inten = (align_scores_df.var().fillna(0)+0.001)*50_000
        bounds = bias.abs().max()*1.1

        fig = plt.figure()
        ax = fig.add_subplot(111)
        plt.scatter(x=bias, y=name_left, s=inten, c=color)
        plt.axvline(0)
        plt.xlim(-bounds, bounds)
        plt.gca().invert_yaxis()
        axi = ax.twinx()
        axi.set_ylim(ax.get_ylim())
        axi.set_yticks(ax.get_yticks(), labels=name_right)
        return fig

class FramingStructure:
    def __init__(self, base_model, roles=None):
        device = "cuda:0" if torch.cuda.is_available() else "cpu"
        pipe2 = pipeline("text2text-generation", base_model, device=device, max_length=300)

    def __call__(self, sequence_to_translate):
        res = self.translator(sequence_to_translate)
        def try_decode(x):
            try:
                return penman.decode(x["generated_text"])
            except:
                return None
        graphs = list(filter(lambda item: item is not None, [try_decode(x) for x in res]))
        return graphs

    def visualize(self, decoded_graphs, min_node_threshold=1, **kwargs):
        cnt = Counter()

        for gen_text in decoded_graphs:
            amr = gen_text.triples
            amr = list(filter(lambda x: x[2] is not None, amr))
            amr = list(map(lambda x: (x[0], x[1].replace(":", ""), x[2]), amr))
            def trim_distinction_end(x):
                x = x.split("_")[0]
                return x
            amr = list(map(lambda x: (trim_distinction_end(x[0]), x[1], trim_distinction_end(x[2])), amr))
            cnt.update(amr)

        G = nx.DiGraph()

        color_map = defaultdict(lambda: "k", {
            "ARG0": "y",
            "ARG1": "r",
            "ARG2": "g",
            "ARG3": "b"
        })

        for entry, num in cnt.items():
            if not G.has_node(entry[0]):
                G.add_node(entry[0], weight=0)
            if not G.has_node(entry[2]):
                G.add_node(entry[2], weight=0)
            G.nodes[entry[0]]["weight"] += num
            G.nodes[entry[2]]["weight"] += num
            G.add_edge(entry[0], entry[2], role=entry[1], weight=num, color=color_map[entry[1]])

        G_sub = nx.subgraph_view(G, filter_node=lambda n: G.nodes[n]["weight"] >= min_node_threshold)

        node_sizes = [x * 100 for x in nx.get_node_attributes(G_sub,'weight').values()]
        edge_colors = nx.get_edge_attributes(G_sub,'color').values()

        fig = plt.figure()

        pos = pygraphviz_layout(G_sub, prog="dot")
        nx.draw_networkx(G_sub, pos, node_size=node_sizes, edge_color=edge_colors)
        nx.draw_networkx_labels(G_sub, pos)
        nx.draw_networkx_edge_labels(G_sub, pos, edge_labels=nx.get_edge_attributes(G_sub, "role"))
        return fig

# Specify the models
base_model_1 = "facebook/bart-large-mnli"
base_model_2 = 'all-mpnet-base-v2'
base_model_3 = "Iseratho/model_parse_xfm_bart_base-v0_1_0"
# https://homes.cs.washington.edu/~nasmith/papers/card+boydstun+gross+resnik+smith.acl15.pdf
candidate_labels = [
    "Economic: costs, benefits, or other financial implications",
    "Capacity and resources: availability of physical, human or financial resources, and capacity of current systems",
    "Morality: religious or ethical implications",
    "Fairness and equality: balance or distribution of rights, responsibilities, and resources",
    "Legality, constitutionality and jurisprudence: rights, freedoms, and authority of individuals, corporations, and government",
    "Policy prescription and evaluation: discussion of specific policies aimed at addressing problems",
    "Crime and punishment: effectiveness and implications of laws and their enforcement",
    "Security and defense: threats to welfare of the individual, community, or nation",
    "Health and safety: health care, sanitation, public safety",
    "Quality of life: threats and opportunities for the individual’s wealth, happiness, and well-being",
    "Cultural identity: traditions, customs, or values of a social group in relation to a policy issue",
    "Public opinion: attitudes and opinions of the general public, including polling and demographics",
    "Political: considerations related to politics and politicians, including lobbying, elections, and attempts to sway voters",
    "External regulation and reputation: international reputation or foreign policy of the U.S.",
    "Other: any coherent group of frames not covered by the above categories",
]

# https://osf.io/xakyw
dimensions = [
    "Care: ...acted with kindness, compassion, or empathy, or nurtured another person.",
    "Harm: ...acted with cruelty, or hurt or harmed another person/animal and caused suffering.",
    "Fairness: ...acted in a fair manner, promoting equality, justice, or rights.",
    "Cheating: ...was unfair or cheated, or caused an injustice or engaged in fraud.",
    "Loyalty: ...acted with fidelity, or as a team player, or was loyal or patriotic.",
    "Betrayal: ...acted disloyal, betrayed someone, was disloyal, or was a traitor.",
    "Authority: ...obeyed, or acted with respect for authority or tradition.",
    "Subversion: ...disobeyed or showed disrespect, or engaged in subversion or caused chaos.",
    "Sanctity: ...acted in a way that was wholesome or sacred, or displayed purity or sanctity.",
    "Degredation: ...was depraved, degrading, impure, or unnatural.",
]
pole_names = [
    ("Care", "Harm"),
    ("Fairness", "Cheating"),
    ("Loyalty", "Betrayal"),
    ("Authority", "Subversion"),
    ("Sanctity", "Degredation"),
]

framing_label_model = FramingLabels(base_model_1, candidate_labels)
framing_dimen_model = FramingDimensions(base_model_2, dimensions, pole_names)
framing_struc_model = FramingStructure(base_model_3)

import pandas as pd

async def framing_single(text):
    fig1, _ = framing_label_model.visualize(framing_label_model(text))
    fig2 = framing_dimen_model.visualize(pd.DataFrame({k: [v] for k, v in framing_dimen_model(text).items()}))
    fig3 = framing_struc_model.visualize(framing_struc_model(text))

    return fig1, fig2, fig3

example_list = ["In 2021, doctors prevented the spread of the virus by vaccinating with Pfizer.",
                "We must fight for our freedom.",
                "The government prevents our freedom.",
                "They prevent the spread.",
                "We fight the virus.",
                "I believe that we should act now. There is no time to waste."
                ]

demo = gr.Interface(fn=framing_single,
                           title="FrameFinder",
                    inputs=gr.Textbox(label="Text to analyze."),
                    description="A simple tool that helps you find (discover and detect) frames in text.",
                    examples=example_list,
                    article="Check out the preliminary article in the [Web Conference Symposium](https://dl.acm.org/doi/pdf/10.1145/3543873.3587534), will be updated to currently in review article after publication.",
                    outputs=[gr.Plot(label="Label"),
                             gr.Plot(label="Dimensions"),
                             gr.Plot(label="Structure")
                            ])

demo.launch()