Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the model and tokenizer
+classifier = pipeline("text-classification",model="AbraMuhara/Fine-TunedBERTURKOfansifTespit")
+
+# Define the prediction function
+def classify_text(text):
+    # Use the pipeline to classify the text
+    result = classifier(text)
+    # Extract the label and score
+    label = result[0]['label']
+    score = result[0]['score']
+    return f"Label: {label}\nScore: {score:.4f}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_text,          # The function to call for predictions
+    inputs=gr.Textbox(),       # Text input box
+    outputs=gr.Text(),         # Text output box
+    title="Text Classification",  # Title of the app
+    description="Enter text to classify it and get the prediction label and score."  # Description
+)
+
+# Launch the interface
+iface.launch()