Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -16,7 +16,6 @@ import cv2
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from transformers import (
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Qwen2VLForConditionalGeneration,
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Qwen2_5_VLForConditionalGeneration,
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VisionEncoderDecoderModel,
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AutoModelForVision2Seq,
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AutoProcessor,
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TextIteratorStreamer,
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@@ -45,15 +44,6 @@ model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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torch_dtype=torch.float16
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).to(device).eval()
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# Load ByteDance's Dolphin
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MODEL_ID_K = "ByteDance/Dolphin"
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processor_k = AutoProcessor.from_pretrained(MODEL_ID_K, trust_remote_code=True)
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model_k = VisionEncoderDecoderModel.from_pretrained(
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MODEL_ID_K,
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trust_remote_code=True,
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torch_dtype=torch.float16
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).to(device).eval()
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# Load SmolDocling-256M-preview
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MODEL_ID_X = "ds4sd/SmolDocling-256M-preview"
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processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
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vidcap.release()
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return frames
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# Dolphin-specific functions
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def model_chat(prompt, image):
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"""Use Dolphin model for inference."""
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processor = processor_k
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model = model_k
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device = "cuda" if torch.cuda.is_available() else "cpu"
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inputs = processor(image, return_tensors="pt").to(device)
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pixel_values = inputs.pixel_values.half()
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prompt_inputs = processor.tokenizer(
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f"<s>{prompt} <Answer/>",
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add_special_tokens=False,
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return_tensors="pt"
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).to(device)
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outputs = model.generate(
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pixel_values=pixel_values,
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decoder_input_ids=prompt_inputs.input_ids,
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decoder_attention_mask=prompt_inputs.attention_mask,
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min_length=1,
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max_length=4096,
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pad_token_id=processor.tokenizer.pad_token_id,
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eos_token_id=processor.tokenizer.eos_token_id,
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use_cache=False, # Changed to False to avoid deprecation warning
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bad_words_ids=[[processor.tokenizer.unk_token_id]],
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return_dict_in_generate=True,
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do_sample=False,
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num_beams=1,
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repetition_penalty=1.1
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)
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sequence = processor.tokenizer.batch_decode(outputs.sequences, skip_special_tokens=False)[0]
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cleaned = sequence.replace(f"<s>{prompt} <Answer/>", "").replace("<pad>", "").replace("</s>", "").strip()
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return cleaned
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def process_elements(layout_results, image):
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"""Parse layout results and extract elements from the image."""
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# Placeholder parsing logic based on expected Dolphin output
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# Assuming layout_results is a string like "[(x1,y1,x2,y2,label), ...]"
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try:
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elements = ast.literal_eval(layout_results)
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except:
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elements = [] # Fallback if parsing fails
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recognition_results = []
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reading_order = 0
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for bbox, label in elements:
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try:
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x1, y1, x2, y2 = map(int, bbox)
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cropped = image.crop((x1, y1, x2, y2))
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if cropped.size[0] > 0 and cropped.size[1] > 0:
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if label == "text":
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text = model_chat("Read text in the image.", cropped)
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recognition_results.append({
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"label": label,
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"bbox": [x1, y1, x2, y2],
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"text": text.strip(),
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"reading_order": reading_order
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})
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elif label == "table":
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table_text = model_chat("Parse the table in the image.", cropped)
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recognition_results.append({
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"label": label,
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"bbox": [x1, y1, x2, y2],
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"text": table_text.strip(),
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"reading_order": reading_order
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})
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elif label == "figure":
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recognition_results.append({
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"label": label,
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"bbox": [x1, y1, x2, y2],
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"text": "[Figure]", # Placeholder for figure content
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"reading_order": reading_order
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})
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reading_order += 1
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except Exception as e:
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print(f"Error processing element: {e}")
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continue
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return recognition_results
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def generate_markdown(recognition_results):
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"""Generate markdown from extracted elements."""
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markdown = ""
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for element in sorted(recognition_results, key=lambda x: x["reading_order"]):
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if element["label"] == "text":
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markdown += f"{element['text']}\n\n"
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elif element["label"] == "table":
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markdown += f"**Table:**\n{element['text']}\n\n"
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elif element["label"] == "figure":
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markdown += f"{element['text']}\n\n"
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return markdown.strip()
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def process_image_with_dolphin(image):
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"""Process a single image with Dolphin model."""
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layout_output = model_chat("Parse the reading order of this document.", image)
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elements = process_elements(layout_output, image)
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markdown_content = generate_markdown(elements)
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return markdown_content
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@spaces.GPU
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def generate_image(model_name: str, text: str, image: Image.Image,
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max_new_tokens: int = 1024,
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top_k: int = 50,
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repetition_penalty: float = 1.2):
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"""Generate responses for image input using the selected model."""
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else:
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if
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text = normalize_values(text, target_max=500)
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messages = [
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{
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"role": "user",
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"content": [{"type": "image"} for _ in images] + [
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{"type": "text", "text": text}
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]
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}
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]
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prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
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streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
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generation_kwargs = {
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**inputs,
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"streamer": streamer,
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"max_new_tokens": max_new_tokens,
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"temperature": temperature,
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"top_p": top_p,
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"top_k": top_k,
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"repetition_penalty": repetition_penalty,
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}
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@spaces.GPU
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def generate_video(model_name: str, text: str, video_path: str,
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top_k: int = 50,
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repetition_penalty: float = 1.2):
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"""Generate responses for video input using the selected model."""
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yield combined_markdown
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else:
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text = normalize_values(text, target_max=500)
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messages = [
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{
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"role": "user",
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"content": [{"type": "image"} for _ in images] + [
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{"type": "text", "text": text}
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prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
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streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
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generation_kwargs = {
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**inputs,
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"streamer": streamer,
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"max_new_tokens": max_new_tokens,
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"temperature": temperature,
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"top_p": top_p,
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"top_k": top_k,
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"repetition_penalty": repetition_penalty,
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}
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# Define examples for image and video inference
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image_examples = [
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["
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video_examples = [
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# Create the Gradio Interface
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with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
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gr.Markdown("# **[
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with gr.Row():
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with gr.Column():
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with gr.Tabs():
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with gr.Column():
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output = gr.Textbox(label="Output", interactive=False, lines=3, scale=2)
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model_choice = gr.Radio(
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choices=["Nanonets-OCR-s", "
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label="Select Model",
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value="Nanonets-OCR-s"
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)
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from transformers import (
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Qwen2VLForConditionalGeneration,
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Qwen2_5_VLForConditionalGeneration,
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AutoModelForVision2Seq,
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AutoProcessor,
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TextIteratorStreamer,
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torch_dtype=torch.float16
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).to(device).eval()
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# Load SmolDocling-256M-preview
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MODEL_ID_X = "ds4sd/SmolDocling-256M-preview"
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processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
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vidcap.release()
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return frames
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@spaces.GPU
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def generate_image(model_name: str, text: str, image: Image.Image,
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max_new_tokens: int = 1024,
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top_k: int = 50,
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repetition_penalty: float = 1.2):
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"""Generate responses for image input using the selected model."""
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# Model selection
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if model_name == "Nanonets-OCR-s":
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processor = processor_m
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model = model_m
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elif model_name == "MonkeyOCR-Recognition":
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processor = processor_g
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model = model_g
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elif model_name == "SmolDocling-256M-preview":
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processor = processor_x
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model = model_x
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else:
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yield "Invalid model selected."
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return
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if image is None:
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yield "Please upload an image."
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return
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# Prepare images as a list (single image for image inference)
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images = [image]
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# SmolDocling-256M specific preprocessing
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if model_name == "SmolDocling-256M-preview":
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if "OTSL" in text or "code" in text:
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images = [add_random_padding(img) for img in images]
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if "OCR at text at" in text or "Identify element" in text or "formula" in text:
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text = normalize_values(text, target_max=500)
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# Unified message structure for all models
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messages = [
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"role": "user",
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"content": [{"type": "image"} for _ in images] + [
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{"type": "text", "text": text}
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]
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}
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]
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prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
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inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
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# Generation with streaming
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streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
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generation_kwargs = {
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**inputs,
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"streamer": streamer,
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"max_new_tokens": max_new_tokens,
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"temperature": temperature,
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"top_p": top_p,
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"top_k": top_k,
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"repetition_penalty": repetition_penalty,
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}
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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# Stream output and collect full response
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buffer = ""
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full_output = ""
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for new_text in streamer:
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full_output += new_text
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buffer += new_text.replace("<|im_end|>", "")
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yield buffer
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# SmolDocling-256M specific postprocessing
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if model_name == "SmolDocling-256M-preview":
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cleaned_output = full_output.replace("<end_of_utterance>", "").strip()
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if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
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191 |
+
if "<chart>" in cleaned_output:
|
192 |
+
cleaned_output = cleaned_output.replace("<chart>", "<otsl>").replace("</chart>", "</otsl>")
|
193 |
+
cleaned_output = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', cleaned_output)
|
194 |
+
doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
|
195 |
+
doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
|
196 |
+
markdown_output = doc.export_to_markdown()
|
197 |
+
yield f"**MD Output:**\n\n{markdown_output}"
|
198 |
+
else:
|
199 |
+
yield cleaned_output
|
200 |
|
201 |
@spaces.GPU
|
202 |
def generate_video(model_name: str, text: str, video_path: str,
|
|
|
206 |
top_k: int = 50,
|
207 |
repetition_penalty: float = 1.2):
|
208 |
"""Generate responses for video input using the selected model."""
|
209 |
+
# Model selection
|
210 |
+
if model_name == "Nanonets-OCR-s":
|
211 |
+
processor = processor_m
|
212 |
+
model = model_m
|
213 |
+
elif model_name == "MonkeyOCR-Recognition":
|
214 |
+
processor = processor_g
|
215 |
+
model = model_g
|
216 |
+
elif model_name == "SmolDocling-256M-preview":
|
217 |
+
processor = processor_x
|
218 |
+
model = model_x
|
|
|
219 |
else:
|
220 |
+
yield "Invalid model selected."
|
221 |
+
return
|
222 |
+
|
223 |
+
if video_path is None:
|
224 |
+
yield "Please upload a video."
|
225 |
+
return
|
226 |
+
|
227 |
+
# Extract frames from video
|
228 |
+
frames = downsample_video(video_path)
|
229 |
+
images = [frame for frame, _ in frames]
|
230 |
+
|
231 |
+
# SmolDocling-256M specific preprocessing
|
232 |
+
if model_name == "SmolDocling-256M-preview":
|
233 |
+
if "OTSL" in text or "code" in text:
|
234 |
+
images = [add_random_padding(img) for img in images]
|
235 |
+
if "OCR at text at" in text or "Identify element" in text or "formula" in text:
|
236 |
+
text = normalize_values(text, target_max=500)
|
237 |
+
|
238 |
+
# Unified message structure for all models
|
239 |
+
messages = [
|
240 |
+
{
|
241 |
+
"role": "user",
|
242 |
+
"content": [{"type": "image"} for _ in images] + [
|
243 |
+
{"type": "text", "text": text}
|
244 |
+
]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
245 |
}
|
246 |
+
]
|
247 |
+
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
|
248 |
+
inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
|
249 |
+
|
250 |
+
# Generation with streaming
|
251 |
+
streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
|
252 |
+
generation_kwargs = {
|
253 |
+
**inputs,
|
254 |
+
"streamer": streamer,
|
255 |
+
"max_new_tokens": max_new_tokens,
|
256 |
+
"temperature": temperature,
|
257 |
+
"top_p": top_p,
|
258 |
+
"top_k": top_k,
|
259 |
+
"repetition_penalty": repetition_penalty,
|
260 |
+
}
|
261 |
+
thread = Thread(target=model.generate, kwargs=generation_kwargs)
|
262 |
+
thread.start()
|
263 |
+
|
264 |
+
# Stream output and collect full response
|
265 |
+
buffer = ""
|
266 |
+
full_output = ""
|
267 |
+
for new_text in streamer:
|
268 |
+
full_output += new_text
|
269 |
+
buffer += new_text.replace("<|im_end|>", "")
|
270 |
+
yield buffer
|
271 |
+
|
272 |
+
# SmolDocling-256M specific postprocessing
|
273 |
+
if model_name == "SmolDocling-256M-preview":
|
274 |
+
cleaned_output = full_output.replace("<end_of_utterance>", "").strip()
|
275 |
+
if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
|
276 |
+
if "<chart>" in cleaned_output:
|
277 |
+
cleaned_output = cleaned_output.replace("<chart>", "<otsl>").replace("</chart>", "</otsl>")
|
278 |
+
cleaned_output = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', cleaned_output)
|
279 |
+
doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
|
280 |
+
doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
|
281 |
+
markdown_output = doc.export_to_markdown()
|
282 |
+
yield f"**MD Output:**\n\n{markdown_output}"
|
283 |
+
else:
|
284 |
+
yield cleaned_output
|
285 |
|
286 |
# Define examples for image and video inference
|
287 |
image_examples = [
|
288 |
+
["fill the correct numbers", "example/image3.png"],
|
289 |
+
["ocr the image", "example/image1.png"],
|
290 |
+
["explain the scene", "example/image2.jpg"],
|
291 |
]
|
292 |
|
293 |
video_examples = [
|
|
|
307 |
|
308 |
# Create the Gradio Interface
|
309 |
with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
|
310 |
+
gr.Markdown("# **[core OCR](https://huggingface.co/collections/prithivMLmods/core-and-docscope-ocr-models-6816d7f1bde3f911c6c852bc)**")
|
311 |
with gr.Row():
|
312 |
with gr.Column():
|
313 |
with gr.Tabs():
|
|
|
336 |
with gr.Column():
|
337 |
output = gr.Textbox(label="Output", interactive=False, lines=3, scale=2)
|
338 |
model_choice = gr.Radio(
|
339 |
+
choices=["Nanonets-OCR-s", "MonkeyOCR-Recognition", "SmolDocling-256M-preview"],
|
340 |
label="Select Model",
|
341 |
value="Nanonets-OCR-s"
|
342 |
)
|