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# import gradio as gr
# from transformers import AutoProcessor, AutoModelForImageTextToText
# from PIL import Image
# import re
# # Load SmolDocling model & processor once
# processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview")
# model = AutoModelForImageTextToText.from_pretrained("ds4sd/SmolDocling-256M-preview")
# def extract_fcel_values_from_image(image, prompt_text):
# """Run SmolDocling on an image and return numeric values inside <fcel> tags."""
# # Prepare prompt for the model
# messages = [
# {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt_text}]}
# ]
# prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
# inputs = processor(text=prompt, images=[image], return_tensors="pt")
# # Generate output
# outputs = model.generate(**inputs, max_new_tokens=2048)
# prompt_length = inputs.input_ids.shape[1]
# generated = outputs[:, prompt_length:]
# result = processor.batch_decode(generated, skip_special_tokens=False)[0]
# clean_text = result.replace("<end_of_utterance>", "").strip()
# # Extract only <fcel> values
# values = re.findall(r"<fcel>([\d.]+)", clean_text)
# values = [float(v) for v in values] # convert to floats
# return values, clean_text
# def compare_images(image1, image2, prompt_text):
# # Extract fcel values from both images
# values1, raw1 = extract_fcel_values_from_image(image1, prompt_text)
# values2, raw2 = extract_fcel_values_from_image(image2, prompt_text)
# # Calculate accuracy
# if len(values1) == len(values2) and values1 == values2:
# accuracy = 100.0
# else:
# matches = sum(1 for a, b in zip(values1, values2) if a == b)
# total = max(len(values1), len(values2))
# accuracy = (matches / total) * 100 if total > 0 else 0
# return {
# # "Extracted Values 1": values1,
# # "Extracted Values 2": values2,
# "Accuracy (%)": accuracy
# }
# # Gradio UI
# demo = gr.Interface(
# fn=compare_images,
# inputs=[
# gr.Image(type="pil", label="Upload First Table Image"),
# gr.Image(type="pil", label="Upload Second Table Image"),
# gr.Textbox(lines=1, placeholder="Enter prompt (e.g. Extract table as OTSL)", label="Prompt")
# ],
# outputs="json",
# title="Table Data Accuracy Checker (SmolDocling)",
# description="Uploads two table images, extracts only <fcel> values from OTSL output, and compares them for accuracy."
# )
# demo.launch()
import gradio as gr
from transformers import AutoProcessor, AutoModelForVision2Seq, TextIteratorStreamer
from transformers.image_utils import load_image
from threading import Thread
import torch
import html
import re
from PIL import Image, ImageOps
# Load model & processor once at startup
processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview")
model = AutoModelForVision2Seq.from_pretrained("ds4sd/SmolDocling-256M-preview", torch_dtype=torch.bfloat16).to("cuda")
def add_random_padding(image, min_percent=0.1, max_percent=0.10):
image = image.convert("RGB")
width, height = image.size
pad_w_percent = random.uniform(min_percent, max_percent)
pad_h_percent = random.uniform(min_percent, max_percent)
pad_w = int(width * pad_w_percent)
pad_h = int(height * pad_h_percent)
corner_pixel = image.getpixel((0, 0)) # Top-left corner
padded_image = ImageOps.expand(image, border=(pad_w, pad_h, pad_w, pad_h), fill=corner_pixel)
return padded_image
def extract_table(image_file):
# Load image
image = load_image(image_file)
# Optionally add padding if needed for model robustness (optional)
image = add_random_padding(image)
# Fixed prompt to extract table only (modify if needed)
text = "Convert this table to OTSL."
# Build the message structure for processor
resulting_messages = [{
"role": "user",
"content": [{"type": "image"}] + [{"type": "text", "text": text}]
}]
prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True)
inputs = processor(text=prompt, images=[image], return_tensors="pt").to('cuda')
generation_args = {
"input_ids": inputs.input_ids,
"pixel_values": inputs.pixel_values,
"attention_mask": inputs.attention_mask,
"max_new_tokens": 8192,
"num_return_sequences": 1,
}
streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
generation_args["streamer"] = streamer
thread = Thread(target=model.generate, kwargs=generation_args)
thread.start()
output_text = ""
for new_text in streamer:
output_text += new_text
# Clean and return output
cleaned_output = output_text.replace("<end_of_utterance>", "").strip()
# Optionally convert <chart> tags to <otsl> if present
if "<chart>" in cleaned_output:
cleaned_output = cleaned_output.replace("<chart>", "<otsl>").replace("</chart>", "</otsl>")
cleaned_output = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', cleaned_output)
return cleaned_output or "No table found or unable to extract."
# Gradio UI
demo = gr.Interface(
fn=extract_table,
inputs=gr.Image(type="filepath", label="Upload Table Image"),
outputs=gr.Textbox(label="Extracted Table (OTSL Format)"),
title="Table Extraction from Image using SmolDocling-256M",
description="Upload an image containing a table. The model will extract the table and output it in OTSL format."
)
demo.launch(debug=True)