Create app.py

app.py

@@ -0,0 +1,41 @@
+import requests
+
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForVision2Seq
+
+
+model = AutoModelForVision2Seq.from_pretrained("microsoft/kosmos-2-patch14-224")
+processor = AutoProcessor.from_pretrained("microsoft/kosmos-2-patch14-224")
+
+# The original Kosmos-2 demo saves the image first then reload it. For some images, this will give slightly different image input and change the generation outputs.
+
+prompt = "An image of"
+
+def describe_image(image_path):
+  inputs = processor(text=prompt, images=image_path, return_tensors="pt")
+
+  generated_ids = model.generate(
+      pixel_values=inputs["pixel_values"],
+      input_ids=inputs["input_ids"],
+      attention_mask=inputs["attention_mask"],
+      image_embeds=None,
+      image_embeds_position_mask=inputs["image_embeds_position_mask"],
+      use_cache=True,
+      max_new_tokens=128,
+  )
+  generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+  # Specify `cleanup_and_extract=False` in order to see the raw model generation.
+  processed_text = processor.post_process_generation(generated_text, cleanup_and_extract=False)
+
+  processed_text, entities = processor.post_process_generation(generated_text)
+
+  return processed_text
+
+import gradio as gr
+
+gr_app = gr.Interface(fn=describe_image, inputs=[gr.Image(label="Upload an image for description", type='pil')],
+                      outputs=[gr.Textbox(label="Image description")], title="App for image description")
+
+if __name__ == "__main__":
+  gr_app.launch(show_error = True)