gradio app version for huggingface space

app.py

@@ -0,0 +1,95 @@
+import gradio as gr
+import torch
+import numpy as np
+import networkx as nx
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from tqdm import tqdm
+
+def _make_logits_consistent(x, R):
+    c_out = x.unsqueeze(1) + 10
+    c_out = c_out.expand(len(x), R.shape[1], R.shape[1])
+    R_batch = R.expand(len(x), R.shape[1], R.shape[1]).to(x.device)
+    final_out, _ = torch.max(R_batch * c_out, dim=2)
+    return final_out - 10
+
+def persuasion_labels(text):
+    model_dir = "models"
+    # Initialize the graph and other necessary components
+    G = nx.DiGraph()
+    # Add edges to the graph
+    edges = [
+        ("ROOT", "Logos"), 
+            ("Logos", "Repetition"), ("Logos", "Obfuscation, Intentional vagueness, Confusion"), ("Logos", "Reasoning"), ("Logos", "Justification"), 
+	            ("Justification", "Slogans"), ("Justification", "Bandwagon"), ("Justification", "Appeal to authority"), ("Justification", "Flag-waving"), ("Justification", "Appeal to fear/prejudice"),
+	            ("Reasoning", "Simplification"), 
+	            	("Simplification", "Causal Oversimplification"), ("Simplification", "Black-and-white Fallacy/Dictatorship"), ("Simplification", "Thought-terminating cliché"),  
+                    ("Reasoning", "Distraction"),
+                    	("Distraction", "Misrepresentation of Someone's Position (Straw Man)"), ("Distraction", "Presenting Irrelevant Data (Red Herring)"), ("Distraction", "Whataboutism"),
+        ("ROOT", "Ethos"), 
+            ("Ethos", "Appeal to authority"), ("Ethos", "Glittering generalities (Virtue)"), ("Ethos", "Bandwagon"), ("Ethos", "Ad Hominem"), ("Ethos", "Transfer"), 
+                ("Ad Hominem", "Doubt"), ("Ad Hominem", "Name calling/Labeling"), ("Ad Hominem", "Smears"), ("Ad Hominem", "Reductio ad hitlerum"), ("Ad Hominem", "Whataboutism"), 
+        ("ROOT", "Pathos"), 
+            ("Pathos", "Exaggeration/Minimisation"), ("Pathos", "Loaded Language"), ("Pathos", "Appeal to (Strong) Emotions"), ("Pathos", "Appeal to fear/prejudice"), ("Pathos", "Flag-waving"), ("Pathos", "Transfer")
+    ]
+    G.add_edges_from(edges)
+    
+    tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
+    model = AutoModelForSequenceClassification.from_pretrained(model_dir)
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+
+    A = nx.to_numpy_array(G).transpose()
+    R = np.zeros(A.shape)
+    np.fill_diagonal(R, 1)
+    g = nx.DiGraph(A)
+    for i in range(len(A)):
+        descendants = list(nx.descendants(g, i))
+        if descendants:
+            R[i, descendants] = 1
+    R = torch.tensor(R).transpose(1, 0).unsqueeze(0)
+    
+    encoding = tokenizer.encode_plus(
+        text,
+        add_special_tokens=True,
+        max_length=128,
+        return_token_type_ids=False,
+        padding="max_length",
+        truncation=True,
+        return_attention_mask=True,
+        return_tensors="pt",
+    )
+
+    with torch.no_grad():
+        outputs = model(
+            input_ids=encoding["input_ids"].to(device),
+            attention_mask=encoding["attention_mask"].to(device),
+        )
+    logits = _make_logits_consistent(outputs.logits, R)
+    logits[:, 0] = -1.0
+    logits = logits > 0.0
+    complete_predicted_hierarchy = np.array(G.nodes)[logits[0].cpu().nonzero()].flatten().tolist()
+
+    # if any label doesn't have children, add them to the list
+    child_only_labels = []
+    for label in complete_predicted_hierarchy:
+        if not list(G.successors(label)):
+            child_only_labels.append(label)
+
+    return complete_predicted_hierarchy, child_only_labels
+
+def launch_interface():
+    iface = gr.Interface(
+        fn=persuasion_labels,
+        inputs=gr.Textbox(lines=5, placeholder="Enter your text here..."),
+        outputs=[
+            gr.Textbox(label="Complete Hierarchical Label List"),
+            gr.Textbox(label="Child-only Label List")
+        ],
+        title="Persuasion Labels",
+        description="Enter your text and get the persuasion labels.",
+    )
+    iface.launch(share=True)
+
+if __name__ == "__main__":
+    launch_interface()

requirements.txt

@@ -5,3 +5,4 @@ transformers
 tqdm
 sentencepiece
 protobuf
+gradio