application file

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+
+
+def interpret_pred(pred):
+    low_bond = -6.748472
+    high_bound = 6.7176056
+    result = "IA" if pred.argmax(dim=-1).item() == 1 else "Humain"
+    pred_value = pred[0][1].item()
+    interpreted_pred = (pred_value - low_bond) / (high_bound - low_bond)
+    is_ai_percent = round(100 * interpreted_pred)
+    return result, is_ai_percent
+
+
+def main(text_sentence):
+    import torch
+    from transformers import AutoTokenizer, AutoModelForSequenceClassification
+    from transformers import Trainer, TrainingArguments, EarlyStoppingCallback
+
+    barthez_tokenizer = AutoTokenizer.from_pretrained("moussaKam/barthez")
+    model = AutoModelForSequenceClassification.from_pretrained("Anvil-ML/detecteur-ia")
+
+    input_ids = torch.tensor(
+        [barthez_tokenizer.encode(text_sentence, add_special_tokens=True)]
+    )
+
+    predict = model.forward(input_ids)[0]
+
+    result = (
+        "Résultat : {}.\nCe texte a {}% de chances d'avoir été généré par de l'IA"
+        .format(interpret_pred(predict)[0], interpret_pred(predict)[1])
+    )
+
+    return result
+
+
+iface = gr.Interface(fn=main, inputs="text", outputs="text")
+iface.launch()