Update app.py

app.py

@@ -1,16 +1,14 @@
+# Use a pipeline as a high-level helper
 from transformers import pipeline
-import gradio as gr
-import 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")
+pipe = pipeline("text2text-generation", model="ballatraore/ballatraore")
+blagueur = pipeline("text2text-generation", model="ballatraore/ballatraore")
 
+import gradio as gr
+# importer joblib
+import joblib
+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)
     outputs = model__.generate(
@@ -19,8 +17,11 @@ def blagueur(prompt):
         max_length=64,
         num_beams=5,
         do_sample=True,
-        temperature=0.9)
+        temperature=0.9
+    )
     return tokenizer__.decode(outputs[0], skip_special_tokens=True)
+
+
 # Configuration de l'interface Gradio
 demo = gr.Interface(
     fn=blagueur,
@@ -33,5 +34,7 @@ demo = gr.Interface(
         ["Dis-moi une blague sur les animaux"],
         ["Blague sur les informaticiens"]
     ],
-    theme='shivi/calm_seafoam')
-demo.lauch()
+    theme='shivi/calm_seafoam'
+)
+
+demo.launch()