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Upload app.py
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app.py
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import gradio as gr
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from gradio_client import Client
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import os
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from PIL import Image
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import io
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import base64
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hf_token = os.environ.get("HF_TKN")
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def convert_base64_to_img(image_string):
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# Split the input string to separate the metadata header and the base64-encoded data
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header, encoded_data = image_string.split(",", 1)
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# Now, encoded_data contains the base64-encoded image data
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image_data = base64.b64decode(encoded_data)
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# Create a BytesIO object to store the image data
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image_file = io.BytesIO(image_data)
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# Open the image using the BytesIO object
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img = Image.open(image_file)
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# Save the image as a JPEG file
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img.save('output.png', 'PNG')
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return "output.png"
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def infer(image_string, question):
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image_in = convert_base64_to_img(image_string)
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client = Client("https://fffiloni-moondream1.hf.space/", hf_token=hf_token)
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result = client.predict(
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image_in, # filepath in 'image' Image component
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question, # str in 'Question' Textbox component
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api_name="/predict"
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)
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print(result)
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return result
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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image_string = gr.Textbox(interactive=False)
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question = gr.Textbox(interactive=False)
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submit_btn = gr.Button("Submit", interactive=False)
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with gr.Column():
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answer = gr.Textbox(interactive=False)
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submit_btn.click(
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fn=infer,
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inputs=[image_string, question],
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outputs=[answer]
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)
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demo.launch()
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