app and requirements

app.py

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+
+import gradio as gr
+from transformers import AutoModel, AutoTokenizer
+import json
+
+# Load model and tokenizer
+repo_id = "rasoultilburg/SocioCausaNet"
+model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+
+# Prediction function
+def predict(sentences, rel_mode="auto", rel_threshold=0.5, cause_decision="cls+span"):
+    results = model.predict(
+        sentences,
+        tokenizer=tokenizer,
+        rel_mode=rel_mode,
+        rel_threshold=rel_threshold,
+        cause_decision=cause_decision
+    )
+    return json.dumps(results, indent=2, ensure_ascii=False)
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Textbox(label="Sentences (comma-separated)", placeholder="Enter sentences"),
+        gr.Radio(["auto", "neural_only"], label="Relation Mode", value="auto"),
+        gr.Slider(0.0, 1.0, value=0.5, label="Relation Threshold"),
+        gr.Radio(["cls_only", "span_only", "cls+span"], label="Cause Decision", value="cls+span")
+    ],
+    outputs="text",
+    title="SocioCausaNet API",
+    description="Extract causal relations from text"
+)
+
+iface.launch()

requirements.txt

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+transformers>=4.30.0
+torch
+gradio