Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+
+# Load the tokenizer and model
+model_name = "alpcansoydas/product-model-18.10.24-bert-total27label_ifhavemorethan100sampleperfamily"
+tokenizer_name = "bert-base-uncased"
+
+# Initialize tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Create a pipeline for text classification
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Function to classify input text
+def classify_product_family(text):
+    results = classifier(text)
+    predicted_label = results[0]['label']
+    return f"Predicted Family Label: {predicted_label}"
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Product Family Classifier")
+    gr.Markdown("Classify product descriptions into one of 27 family labels.")
+    
+    input_text = gr.Textbox(label="Enter Product Description", placeholder="Type product description here...")
+    output_label = gr.Textbox(label="Predicted Family Label")
+    
+    classify_button = gr.Button("Classify")
+    classify_button.click(fn=classify_product_family, inputs=input_text, outputs=output_label)
+
+# Launch the Gradio interface
+demo.launch()