Update app.py

app.py

@@ -1,17 +1,19 @@
 import gradio as gr
 from transformers import pipeline
 
-classifier = pipeline("sentiment-analysis", model="Tirendaz/my_distilbert_model")
+examples=["This is wonderful movie!", "The movie was really bad; I didn't like it."]
 
-def text_classification(text):
-    result= classifier(text)
-    sentiment_label = result[0]['label']
-    sentiment_score = result[0]['score']
-    formatted_output = f"This sentiment is {sentiment_label} with the probability {sentiment_score*100:.2f}%"
-    return formatted_output
 
-examples=["This is wonderful movie!", "The movie was really bad; I didn't like it."]
+model_path = "waelChafei/bertuned"
 
+
+model = BertForSequenceClassification.from_pretrained(model_path)
+tokenizer= BertTokenizerFast.from_pretrained(model_path)
+nlp= pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+def text_classification(text):
+    result= classifier(text)
+    return result
 io = gr.Interface(fn=text_classification, 
                          inputs= gr.Textbox(lines=2, label="Text", placeholder="Enter title here..."), 
                          outputs=gr.Textbox(lines=2, label="Text Classification Result"),