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import gradio as gr |
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import torch |
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import numpy as np |
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import networkx as nx |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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from tqdm import tqdm |
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def _make_logits_consistent(x, R): |
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c_out = x.unsqueeze(1) + 10 |
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c_out = c_out.expand(len(x), R.shape[1], R.shape[1]) |
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R_batch = R.expand(len(x), R.shape[1], R.shape[1]).to(x.device) |
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final_out, _ = torch.max(R_batch * c_out, dim=2) |
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return final_out - 10 |
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def persuasion_labels(text): |
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model_dir = "models" |
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G = nx.DiGraph() |
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edges = [ |
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("ROOT", "Logos"), |
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("Logos", "Repetition"), ("Logos", "Obfuscation, Intentional vagueness, Confusion"), ("Logos", "Reasoning"), ("Logos", "Justification"), |
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("Justification", "Slogans"), ("Justification", "Bandwagon"), ("Justification", "Appeal to authority"), ("Justification", "Flag-waving"), ("Justification", "Appeal to fear/prejudice"), |
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("Reasoning", "Simplification"), |
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("Simplification", "Causal Oversimplification"), ("Simplification", "Black-and-white Fallacy/Dictatorship"), ("Simplification", "Thought-terminating cliché"), |
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("Reasoning", "Distraction"), |
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("Distraction", "Misrepresentation of Someone's Position (Straw Man)"), ("Distraction", "Presenting Irrelevant Data (Red Herring)"), ("Distraction", "Whataboutism"), |
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("ROOT", "Ethos"), |
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("Ethos", "Appeal to authority"), ("Ethos", "Glittering generalities (Virtue)"), ("Ethos", "Bandwagon"), ("Ethos", "Ad Hominem"), ("Ethos", "Transfer"), |
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("Ad Hominem", "Doubt"), ("Ad Hominem", "Name calling/Labeling"), ("Ad Hominem", "Smears"), ("Ad Hominem", "Reductio ad hitlerum"), ("Ad Hominem", "Whataboutism"), |
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("ROOT", "Pathos"), |
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("Pathos", "Exaggeration/Minimisation"), ("Pathos", "Loaded Language"), ("Pathos", "Appeal to (Strong) Emotions"), ("Pathos", "Appeal to fear/prejudice"), ("Pathos", "Flag-waving"), ("Pathos", "Transfer") |
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] |
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G.add_edges_from(edges) |
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tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") |
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model = AutoModelForSequenceClassification.from_pretrained(model_dir) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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A = nx.to_numpy_array(G).transpose() |
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R = np.zeros(A.shape) |
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np.fill_diagonal(R, 1) |
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g = nx.DiGraph(A) |
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for i in range(len(A)): |
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descendants = list(nx.descendants(g, i)) |
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if descendants: |
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R[i, descendants] = 1 |
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R = torch.tensor(R).transpose(1, 0).unsqueeze(0) |
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encoding = tokenizer.encode_plus( |
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text, |
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add_special_tokens=True, |
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max_length=128, |
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return_token_type_ids=False, |
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padding="max_length", |
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truncation=True, |
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return_attention_mask=True, |
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return_tensors="pt", |
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) |
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with torch.no_grad(): |
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outputs = model( |
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input_ids=encoding["input_ids"].to(device), |
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attention_mask=encoding["attention_mask"].to(device), |
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) |
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logits = _make_logits_consistent(outputs.logits, R) |
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logits[:, 0] = -1.0 |
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logits = logits > 0.0 |
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complete_predicted_hierarchy = np.array(G.nodes)[logits[0].cpu().nonzero()].flatten().tolist() |
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child_only_labels = [] |
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for label in complete_predicted_hierarchy: |
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if not list(G.successors(label)): |
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child_only_labels.append(label) |
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return complete_predicted_hierarchy, child_only_labels |
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def launch_interface(): |
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iface = gr.Interface( |
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fn=persuasion_labels, |
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inputs=gr.Textbox(lines=5, placeholder="Enter your text here..."), |
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outputs=[ |
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gr.Textbox(label="Complete Hierarchical Label List"), |
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gr.Textbox(label="Child-only Label List") |
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], |
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title="Persuasion Labels", |
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description="Enter your text and get the persuasion labels.", |
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) |
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iface.launch(share=True) |
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if __name__ == "__main__": |
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launch_interface() |
