Update app.py

app.py

@@ -1,3 +1,37 @@
 import gradio as gr
+from transformers import pipeline
 
-gr.load("models/Karzan/user_profile_skills_model").launch()
+# Load the classification pipeline
+classifier = pipeline(
+    "sentiment-analysis", 
+    model="Karzan/user_profile_skills_model", 
+    return_all_scores=True, 
+    top_k=10
+)
+
+# Define the prediction function
+def classify_text(text):
+    # Perform classification
+    results = classifier(text)
+    # Format the output
+    formatted_results = [
+        {"label": item["label"], "score": round(item["score"], 4)}
+        for result in results for item in result
+    ]
+    return formatted_results
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Text Classification with Hugging Face Transformers")
+    gr.Markdown("Enter text to classify using the model: **Karzan/user_profile_skills_model**.")
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Input Text", lines=3, placeholder="Type something...")
+            classify_button = gr.Button("Classify")
+        with gr.Column():
+            output_text = gr.JSON(label="Classification Results")
+
+    classify_button.click(classify_text, inputs=input_text, outputs=output_text)
+
+# Launch the app
+demo.launch()