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a2ca4b5 ee4081d a2ca4b5 ee4081d a2ca4b5 ee4081d 79d16a5 ee4081d a2ca4b5 79d16a5 a2ca4b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from PIL import Image
import requests
import gradio as gr
# app title
title = "ScriptSense"
description = "<p style='text-align: center; font-size: 22px; font-weight: bold;'>design and crafted by aryan verma</p>"
article = "<p style='text-align: center; font-size: 14px;'>aryan verma | 241306064</p>"
css = "footer {display: none !important;}"
# sample images
examples = [
["", "images/1.jpg", "images/1.jpg"],
["", "images/sample-handwritten-2.PNG", "images/sample-handwritten-2.PNG"]
]
#you can load any model from huggingface
processor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-handwritten')
model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-large-handwritten')
# prediction function for handwritting
def predict(ImageUrl,imgDraw,imgUplod):
image = None
#fetch the image from url or handwritten canvas or the uplaoded image
if ImageUrl:
try:
image = Image.open(requests.get(ImageUrl, stream=True).raw).convert("RGB")
except:
return "Error: Invalid Image URL"
# Prioritize uploaded image if it exists
elif imgUplod is not None:
image = imgUplod.convert("RGB")
# Fallback to the drawing canvas
elif imgDraw is not None:
# Handle Gradio 4+ sketchpad returning a dictionary
if isinstance(imgDraw, dict) and "composite" in imgDraw:
if imgDraw["composite"] is not None:
image = imgDraw["composite"].convert("RGB")
elif not isinstance(imgDraw, dict):
image = imgDraw.convert("RGB")
if image is None:
return "Please provide an image via URL, Sketchpad, or Upload."
#predict the image using microsoft/trocr-large-handwritten model loaded earlier
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
#gradio interface
interface = gr.Interface(
fn=predict,
inputs=["text", gr.Sketchpad(type="pil"), gr.Image(type="pil")],
outputs="text",
title=title,
description=description,
article=article,
examples=examples,
css=css
)
interface.launch()
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