added code

app.py

@@ -0,0 +1,27 @@
+import gradio as gr
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from PIL import Image
+import torch
+
+# Example with BLIP (replace with your fine-tuned model)
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+
+def caption_image(image):
+    if image is None:
+        return "No image provided"
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        out = model.generate(**inputs)
+    caption = processor.decode(out[0], skip_special_tokens=True)
+    return caption
+
+demo = gr.Interface(
+    fn=caption_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Custom UI Action Description"
+)
+
+if __name__ == "__main__":
+    demo.launch()