Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Charger le modèle et le tokenizer
+model_name = "papasega/finetune_Distilbert_SST_Avalinguo_Fluency"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Fonction de prédiction
+def predict_fluency(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    logits = model(**inputs).logits
+    probs = torch.softmax(logits, dim=1)
+    label = torch.argmax(probs, dim=1).item()
+    if label == 0:
+        return "Low Fluency"
+    else:
+        return "High Fluency"
+
+iface = gr.Interface(fn=predict_fluency, 
+                     inputs="text", 
+                     outputs="text",
+                     description="Ce modèle est un modèle de classification de la fluence de l'utilisateur suivant le texte.",
+                           examples=[
+                                ["Engineer, Yeah, indeed, you know that the lady has a Phd. It's the 1st.", 
+                                 "Engineer, Yeah, indeed, you know that the lady has a Phd. It's the 1st."],
+                                [ "Oh, how was brown for you?", 
+                                 "Oh, how was brown for you?"],
+                                [ "Now they can.", 
+                                 "Now they can."],
+                                [ "But kind of plastics like growing more social consciousness, right?", 
+                                  "But kind of plastics like growing more social consciousness, right?"]
+                                      ]
+                     )
+
+
+iface.launch()