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