Update app.py

app.py

@@ -1,30 +1,29 @@
-import gradio as gr
-from transformers import AutoProcessor, BlipForConditionalGeneration
-from PIL import Image
-import numpy as np
-
-# Load BLIP model
-processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
-
-def caption_image(input_image: np.ndarray):
-    # Convert numpy array to PIL Image
-    raw_image = Image.fromarray(input_image).convert('RGB')
-    
-    # Generate caption
-    inputs = processor(images=raw_image, text="a photo of", return_tensors="pt")
-    outputs = model.generate(**inputs, max_new_tokens=50)
-    caption = processor.decode(outputs[0], skip_special_tokens=True)
-    
-    return caption
-
-# Gradio interface
-iface = gr.Interface(
-    fn=caption_image,
-    inputs=gr.Image(),
-    outputs="text",
-    title="Image Captioning",
-    examples=["example1.jpg", "example2.jpg"]  # Optional: Add sample images
-)
-
+import gradio as gr
+from transformers import AutoProcessor, BlipForConditionalGeneration
+from PIL import Image
+import numpy as np
+
+# Load BLIP model
+processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+def caption_image(input_image: np.ndarray):
+    # Convert numpy array to PIL Image
+    raw_image = Image.fromarray(input_image).convert('RGB')
+    
+    # Generate caption
+    inputs = processor(images=raw_image, text="a photo of", return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=50)
+    caption = processor.decode(outputs[0], skip_special_tokens=True)
+    
+    return caption
+
+# Gradio interface
+iface = gr.Interface(
+    fn=caption_image,
+    inputs=gr.Image(),
+    outputs="text",
+    title="Image Captioning",
+)
+
 iface.launch()