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import torch
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
import gradio as gr
from PIL import Image
# Use a publicly available high-capacity model.
# For instance, we use "google/pix2struct-docvqa-large".
# (If you need a different model or a private one, adjust accordingly and add authentication if necessary.)
model_name = "google/pix2struct-docvqa-large"
model = Pix2StructForConditionalGeneration.from_pretrained(model_name)
processor = Pix2StructProcessor.from_pretrained(model_name)
def solve_problem(image):
try:
# Ensure the image is in RGB.
image = image.convert("RGB")
# Preprocess image and text prompt.
inputs = processor(
images=[image],
text="Solve the following problem:",
return_tensors="pt",
max_patches=2048
)
# Generate prediction.
predictions = model.generate(
**inputs,
max_new_tokens=200,
early_stopping=True,
num_beams=4,
temperature=0.2
)
# Decode the prompt (input IDs) and the generated output.
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.
iface = gr.Interface(
fn=solve_problem,
inputs=gr.Image(type="pil", label="Upload Your Problem Image", image_mode="RGB"),
outputs=gr.Textbox(label="Solution", show_copy_button=True),
title="Problem Solver with Pix2Struct",
description=(
"Upload an image (for example, a handwritten math or logic problem) "
"and get a solution generated by a high-capacity Pix2Struct model.\n\n"
"Note: For best results on domain-specific tasks, consider fine-tuning on your own dataset."
),
examples=[
["example_problem1.png"],
["example_problem2.jpg"]
],
theme="soft",
allow_flagging="never"
)
if __name__ == "__main__":
iface.launch()
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