Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from transformers import AutoProcessor, BlipForConditionalGeneration
+
+# Load the pretrained processor and model
+processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+# Define the caption_image function that takes an input image and returns a caption
+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
+    inputs = processor(raw_image, return_tensors="pt")
+    # Generate a caption for the image
+    out = model.generate(**inputs, max_length=50)
+    # Decode the generated tokens to text
+    caption = processor.decode(out[0], skip_special_tokens=True)
+    return caption
+
+# Gradio interface
+iface = gr.Interface(
+    fn=caption_image, 
+    inputs=gr.Image(), 
+    outputs="text",
+    title="Image Captioning",
+    description="This is a web app for generating captions for images using a trained AI model."
+)
+
+# Launch the app with a public link
+iface.launch(share=True)
+