Create app.py

app.py

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+from transformers import pipeline
+import gradio as gr
+
+def image_classifier(image):
+    classifier = pipeline("image-classification", model="dupthotshering/bhutanese-textile-model")
+    results = classifier(image)
+    
+    # Convert results into a dictionary with class names as keys
+    formatted_results = {result['label']: result['score'] for result in results}
+    return formatted_results
+
+# Define Gradio interface
+demo = gr.Interface(
+    fn=image_classifier,
+    inputs=gr.Image(type="pil"),  # Ensure input is an image
+    outputs=gr.Label(num_top_classes=7),  # Display all class scores
+    title="Bhutanese Textile Classifier",
+    description="Upload an image to classify it into one of the Bhutanese textile categories."
+)
+
+demo.launch()