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import pandas as pd
from PIL import Image
import streamlit as st
from streamlit_drawable_canvas import st_canvas
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
processor = TrOCRProcessor.from_pretrained("UBC-NLP/ArOCR-Fonts-v2")
model = VisionEncoderDecoderModel.from_pretrained("UBC-NLP/ArOCR-Fonts-v2")
def predict(img):
# if img is None:
# _,generated_text=main(image)
# return generated_text
# else:
images = Image.fromarray(img).convert("RGB")
pixel_values = processor(images, return_tensors="pt").pixel_values
# PIL_image = Image.fromarray(np.uint8(pixel_values.squeeze()[0,:,:])).convert('RGB')
generated_ids = model.generate(pixel_values,max_length=512)
generated_text = processor.batch_decode(
generated_ids, skip_special_tokens=True)[0]
return generated_text
# Specify canvas parameters in application
stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 3)
stroke_color = st.sidebar.color_picker("Stroke color hex: ")
bg_color = st.sidebar.color_picker("Background color hex: ", "#eee")
bg_image = st.sidebar.file_uploader("Background image:", type=["png", "jpg"])
realtime_update = st.sidebar.checkbox("Update in realtime", True)
# Create a canvas component
canvas_result = st_canvas(
fill_color="rgba(255, 165, 0, 0.3)", # Fixed fill color with some opacity
stroke_width=stroke_width,
stroke_color=stroke_color,
background_color=bg_color,
background_image=Image.open(bg_image) if bg_image else None,
update_streamlit=realtime_update,
height=200,
drawing_mode="freedraw",
display_toolbar=st.sidebar.checkbox("Display toolbar", True),
key="full_app",
)
# Do something interesting with the image data and paths
if canvas_result.image_data is not None:
st.image(canvas_result.image_data)
st.text(predict(canvas_result.image_data)) |