Create app.py

app.py

@@ -0,0 +1,33 @@
+import gradio as gr
+from transformers import OFATokenizer, OFAModel
+from PIL import Image
+import torch
+
+# Load the OFA tokenizer and model
+tokenizer = OFATokenizer.from_pretrained("OFA-Sys/ofa-base")
+model = OFAModel.from_pretrained("OFA-Sys/ofa-base", use_cache=True)
+
+def image_captioning(image):
+    # Preprocess the image
+    img = Image.open(image).convert("RGB")
+
+    # Generate the caption
+    inputs = tokenizer([img], return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(**inputs)
+
+    # Decode the output
+    caption = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return caption
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=image_captioning,
+    inputs=gr.Image(label="Upload an Image", type="filepath"),
+    outputs=gr.Textbox(label="Generated Caption"),
+    title="OFA Image Captioning",
+    description="Upload an image to generate a caption using the OFA model.",
+)
+
+# Launch the interface
+interface.launch()