Update app.py

app.py

@@ -1,23 +1,26 @@
-# Use a pipeline as a high-level helper
 from transformers import pipeline
 import gradio as gr
-# importer joblib
 import joblib
-model__=joblib.load('model.joblib')
-tokenizer__=joblib.load("tokenizer.joblib")
+
+# Configuration de base
+# model_name = "facebook/bart-base"
+# output_dir = "./bart_blagueur_model"
+# # Tokenizer et modèle
+# tokenizer = BartTokenizer.from_pretrained(model_name)
+# model = BartForConditionalGeneration.from_pretrained(model_name)
+model__ = joblib.load("model.joblib")
+tokenizer_ = joblib.load("tokenizer.joblib")
+
 def blagueur(prompt):
-    inputs = tokenizer__(prompt, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
+    inputs = tokenizer_(prompt, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
     outputs = model__.generate(
         input_ids=inputs["input_ids"],
         attention_mask=inputs["attention_mask"],
         max_length=64,
         num_beams=5,
         do_sample=True,
-        temperature=0.9
-    )
-    return tokenizer__.decode(outputs[0], skip_special_tokens=True)
-
-
+        temperature=0.9)
+    return tokenizer_.decode(outputs[0], skip_special_tokens=True)
 # Configuration de l'interface Gradio
 demo = gr.Interface(
     fn=blagueur,
@@ -30,7 +33,4 @@ demo = gr.Interface(
         ["Dis-moi une blague sur les animaux"],
         ["Blague sur les informaticiens"]
     ],
-    theme='shivi/calm_seafoam'
-)
-
-demo.launch()
+    theme='shivi/calm_seafoam'