Spaces:
Runtime error
Runtime error
File size: 2,550 Bytes
3dc1b9e 5093ea9 3dc1b9e 5093ea9 3dc1b9e 5093ea9 3dc1b9e 5093ea9 3dc1b9e 5093ea9 3dc1b9e 5093ea9 3dc1b9e 5093ea9 3dc1b9e 5093ea9 3dc1b9e 5093ea9 3dc1b9e 5093ea9 3dc1b9e 5093ea9 3dc1b9e 5093ea9 |
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 67 68 69 70 71 72 73 |
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
import gradio as gr
from PIL import Image
# Load the pre-trained Pix2Struct model and processor.
model_name = "google/pix2struct-mathqa-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: We omit the header_text parameter because this is not a VQA task.
inputs = processor(
images=[image], # Provide a list of images.
text="Solve the following math problem:", # Prompt text.
return_tensors="pt",
max_patches=2048 # Increase the maximum patches for better math handling.
)
# Generate the solution with specified generation parameters.
predictions = model.generate(
**inputs,
max_new_tokens=200,
early_stopping=True,
num_beams=4,
temperature=0.2
)
# Decode the input text and the model prediction.
# Here, we access "input_ids" via the dictionary key.
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", # Force RGB conversion.
source="upload"
),
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"], # Ensure these example files exist in your working directory.
["example_algebra.jpg"]
],
theme="soft",
allow_flagging="never"
)
if __name__ == "__main__":
demo.launch()
|