Update app.py

app.py

@@ -1,45 +1,7 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-from transformers import AutoProcessor, BlipForConditionalGeneration
+from transformers import pipeline
 
-# HuggingFace
-# Load model directly
-#from transformers import AutoProcessor, AutoModelForSeq2SeqLM
-
-processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
-#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
-    # You do not need a question for image captioning
-    text = "the image of"
-    inputs = processor(images=image, text=text, return_tensors="pt")
-    
-
-    # Generate a caption for the image
-    # Generate a caption for the image
-    outputs = model.generate(**inputs, max_length=50)
-
-    # Decode the generated tokens to text and store it into `caption`
-    # Decode the generated tokens to text
-    caption = processor.decode(outputs[0], skip_special_tokens=True)
-    # Print the caption
-    #print(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."
+#pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+pipe = pipeline(model="Salesforce/blip-image-captioning-base")
+preds = pipe(
+    images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
 )
-