Update app.py

app.py

@@ -1,7 +1,13 @@
 import gradio as gr
 
-# Load the model
-model = gr.load("models/AhmedBou/JoBert")
+def classify_job_description(text):
+    import transformers
+    from transformers import pipeline
+
+    nlp = pipeline("text-classification", model="your_model_name")
+    result = nlp(text)
+    # Process the result to extract labels and confidences
+    return result
 
 # Define the title and text labels
 title = "Classify Job Descriptions at the Span Level"
@@ -15,13 +21,10 @@ label_texts = {
 }
 
 # Create the Gradio interface
-def classify_job_description(text):
-    return model(text)
-
 interface = gr.Interface(
     fn=classify_job_description,
-    inputs=gr.inputs.Textbox(lines=10, placeholder="Enter job description here..."),
-    outputs=gr.outputs.Textbox(label="Classification Results"),
+    inputs=gr.components.Textbox(lines=10, placeholder="Enter job description here..."),
+    outputs=gr.components.Tabular(headers=["Label", "Confidence"]),
     title=title,
     description="\n".join([f"{key}: {value}" for key, value in label_texts.items()])
 )