app.py

app.py

@@ -0,0 +1,28 @@
+import gradio as gr
+from transformers import pipeline
+
+# Initialize the image classification pipeline
+pipe = pipeline("image-classification", model="eligapris/v-mdd-2000-150")
+
+def classify_image(image):
+    # Perform image classification
+    results = pipe(image)
+
+    # Format the results
+    output = ""
+    for result in results:
+        output += f"Label: {result['label']}, Score: {result['score']:.4f}\n"
+
+    return output
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Image Classification",
+    description="Upload an image to classify it using the eligapris/v-mdd-2000-150 model."
+)
+
+# Launch the app
+iface.launch(share=True)