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Create app.py
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app.py
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!pip install transformers
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!pip install gradio
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from transformers import pipeline
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image_captioner = pipeline("image-to-text",model="Salesforce/blip-image-captioning-large")
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#Set up Prerequisites for Image Captioning App User Interface
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import os
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import io
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import IPython.display
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from PIL import Image
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import base64
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import gradio as gr
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def image_to_base64_str(pil_image):
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byte_arr = io.BytesIO()
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pil_image.save(byte_arr, format='PNG')
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byte_arr = byte_arr.getvalue()
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return str(base64.b64encode(byte_arr).decode('utf-8'))
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def captioner(image):
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base64_image = image_to_base64_str(image)
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result = image_captioner(base64_image)
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return result[0]['generated_text']
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gr.close_all()
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ImageCaptionApp = gr.Interface(fn=captioner,
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inputs=[gr.Image(label="Upload image", type="pil")],
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outputs=[gr.Textbox(label="Caption")],
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title="Image Captioning with BLIP",
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description="Caption any image using the BLIP model",
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allow_flagging="never")
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ImageCaptionApp.launch()
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