Update app.py

app.py

@@ -9,22 +9,31 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Load the image-to-text pipeline
 caption_image = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large", device=device)
+translate = pipeline("translation_en_to_ar", model="Helsinki-NLP/opus-mt-en-ar")
 
 # List of local image paths
 example_images = ["flower.jpg"]
 
-# Function to process the image
-def process_image(image):
-    caption = caption_image(image)[0]['generated_text']
-    return caption
+def Arabic_Image_Captioning(image):
+    caption = caption_image(image)
+    caption = caption[0]['generated_text']
 
-# Create Gradio interface with example images
-iface = gr.Interface(
-    fn=process_image,
+    arabic_caption = translate(caption)
+    arabic_caption = arabic_caption[0]['translation_text']
+
+    html_result = f'<div dir="rtl">{arabic_caption}</div>'
+    return html_result
+
+demo = gr.Interface(
+    fn=Arabic_Image_Captioning,
     inputs=gr.Image(type="pil"),
-    outputs=gr.Textbox(label="Generated Caption"),
-    examples=example_images  # Use local images as examples
+    outputs=gr.HTML(label="Caption in Arabic"),
+    title="Arabic Image Captioning",
+    description="Upload an image to generate an arabic caption.",
+    examples=example_images
+
 )
 
+
 # Launch the interface
 iface.launch()