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import gradio as gr
import numpy as np
from PIL import Image
from transformers import AutoProcessor, BlipForConditionalGeneration
# HuggingFace
# Load model directly
from transformers import AutoProcessor, AutoModelForSeq2SeqLM
processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = AutoModelForSeq2SeqLM.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."
)
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