Create app.py

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+from transformers import AutoProcessor, BlipForConditionalGeneration
+
+processor = # write your code here
+model = # write your code here
+
+def caption_image(input_image: np.ndarray):
+    # Convert numpy array to PIL Image and convert to RGB
+    raw_image = Image.fromarray(input_image).convert('RGB')
+    
+    # Process the image
+    
+
+    # Generate a caption for the image
+
+
+    # Decode the generated tokens to text and store it into `caption`
+    
+
+    return caption
+
+iface = gr.Interface(
+    fn=caption_image, 
+    inputs=gr.Image(), 
+    outputs="text",
+    title="Image Captioning",
+    description="This is a simple web app for generating captions for images using a trained model."
+)
+
+iface = gr.Interface(
+    fn=caption_image, 
+    inputs=gr.Image(), 
+    outputs="text",
+    title="Image Captioning",
+    description="This is a simple web app for generating captions for images using a trained model."
+)