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from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
import gradio as gr
from PIL import Image
# Use a public model identifier. If you need a private model, remember to authenticate.
model_name = "google/pix2struct-textcaps-base"
model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
processor = Pix2StructProcessor.from_pretrained(model_name)
def solve_math_problem(image):
try:
# Ensure the image is in RGB format.
image = image.convert("RGB")
# Preprocess the image and text. Note that header_text is omitted as it's not used for non-VQA tasks.
inputs = processor(
images=[image],
text="Solve the following math problem:",
return_tensors="pt",
max_patches=2048
)
# Generate the solution with generation parameters.
predictions = model.generate(
**inputs,
max_new_tokens=200,
early_stopping=True,
num_beams=4,
temperature=0.2
)
# Decode the problem text and generated solution.
problem_text = processor.decode(
inputs["input_ids"][0],
skip_special_tokens=True,
clean_up_tokenization_spaces=True
)
solution = processor.decode(
predictions[0],
skip_special_tokens=True,
clean_up_tokenization_spaces=True
)
return f"Problem: {problem_text}\nSolution: {solution}"
except Exception as e:
return f"Error processing image: {str(e)}"
# Set up the Gradio interface.
demo = gr.Interface(
fn=solve_math_problem,
inputs=gr.Image(
type="pil",
label="Upload Handwritten Math Problem",
image_mode="RGB" # This forces the input to be RGB.
),
outputs=gr.Textbox(label="Solution", show_copy_button=True),
title="Handwritten Math Problem Solver",
description="Upload an image of a handwritten math problem (algebra, arithmetic, etc.) and get the solution",
examples=[
["example_addition.png"],
["example_algebra.jpg"]
],
theme="soft",
allow_flagging="never"
)
if __name__ == "__main__":
demo.launch()
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