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import gradio as gr
import numpy as np
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
#from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering
from PIL import Image
#client = InferenceClient("models/microsoft/trocr-base-handwritten")
#processor = AutoProcessor.from_pretrained("microsoft/trocr-base-handwritten")
processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')
#model = AutoModelForDocumentQuestionAnswering.from_pretrained("microsoft/trocr-base-handwritten")
model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')
def sepia(input_img):
sepia_filter = np.array([
[0.393, 0.769, 0.189],
[0.349, 0.686, 0.168],
[0.272, 0.534, 0.131]
])
sepia_img = input_img.dot(sepia_filter.T)
sepia_img /= sepia_img.max()
sepia_values = repr(sepia_img)
return sepia_img, sepia_values
## https://www.gradio.app/docs/gradio/blocks
## required positional arguments: 'inputs' and 'outputs'
def process_image(image):
try:
pixel_values = processor(images=image, return_tensors="pt").pixel_values
generated_ids = model.generate(pixel_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_text
except Exception as e:
return f"Error: {str(e)}"
def additional_input(text):
return f"Additional input received: {text}"
sepia_interface = gr.Interface(sepia, gr.Image(), "image")
with gr.Blocks() as generated_output:
with gr.Column():
sepia_values_text=gr.Textbox(label="Sepia Values")
output_img = gr.Image(label="Output Image")
gr.Interface(fn=sepia,
inputs=gr.Image(
#this makes the camera stream live
sources=["webcam"],
streaming=True
),
outputs=[output_img, sepia_values_text],
live=True,
show_progress="full")
with gr.Row():
output_img.change(
fn=process_image,
inputs=output_img,
outputs=gr.Textbox(label="Recognized Text"),
show_progress="full")
#with gr.Blocks() as generated_output:
# inp = gr.Interface(sepia, gr.Image(), "image")
# out = gr.Textbox()
#demo = gr.TabbedInterface([sepia_interface, generated_output], ["RGB Sepia Filter", "Handwritten to Text"])
#with gr.Blocks() as demo:
# with gr.Row():
# input_img = gr.Image(label="Input Image")
# submit_button = gr.Button("Submit")
# output_img = gr.Image(label="Output Image")
# sepia_values_text = gr.Textbox(label="Sepia Values")
# submit_button.click(sepia, inputs=input_img, outputs=[output_img, sepia_values_text])
if __name__ == "__main__":
generated_output.launch() |