Update app.py

app.py

@@ -16,7 +16,6 @@ import cv2
 from transformers import (
     Qwen2VLForConditionalGeneration,
     Qwen2_5_VLForConditionalGeneration,
-    VisionEncoderDecoderModel,
     AutoModelForVision2Seq,
     AutoProcessor,
     TextIteratorStreamer,
@@ -45,15 +44,6 @@ model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
     torch_dtype=torch.float16
 ).to(device).eval()
 
-# Load ByteDance's Dolphin
-MODEL_ID_K = "ByteDance/Dolphin"
-processor_k = AutoProcessor.from_pretrained(MODEL_ID_K, trust_remote_code=True)
-model_k = VisionEncoderDecoderModel.from_pretrained(
-    MODEL_ID_K,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
 # Load SmolDocling-256M-preview
 MODEL_ID_X = "ds4sd/SmolDocling-256M-preview"
 processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
@@ -124,104 +114,6 @@ def downsample_video(video_path):
     vidcap.release()
     return frames
 
-# Dolphin-specific functions
-def model_chat(prompt, image):
-    """Use Dolphin model for inference."""
-    processor = processor_k
-    model = model_k
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    inputs = processor(image, return_tensors="pt").to(device)
-    pixel_values = inputs.pixel_values.half()
-    prompt_inputs = processor.tokenizer(
-        f"<s>{prompt} <Answer/>",
-        add_special_tokens=False,
-        return_tensors="pt"
-    ).to(device)
-    outputs = model.generate(
-        pixel_values=pixel_values,
-        decoder_input_ids=prompt_inputs.input_ids,
-        decoder_attention_mask=prompt_inputs.attention_mask,
-        min_length=1,
-        max_length=4096,
-        pad_token_id=processor.tokenizer.pad_token_id,
-        eos_token_id=processor.tokenizer.eos_token_id,
-        use_cache=False,  # Changed to False to avoid deprecation warning
-        bad_words_ids=[[processor.tokenizer.unk_token_id]],
-        return_dict_in_generate=True,
-        do_sample=False,
-        num_beams=1,
-        repetition_penalty=1.1
-    )
-    sequence = processor.tokenizer.batch_decode(outputs.sequences, skip_special_tokens=False)[0]
-    cleaned = sequence.replace(f"<s>{prompt} <Answer/>", "").replace("<pad>", "").replace("</s>", "").strip()
-    return cleaned
-
-def process_elements(layout_results, image):
-    """Parse layout results and extract elements from the image."""
-    # Placeholder parsing logic based on expected Dolphin output
-    # Assuming layout_results is a string like "[(x1,y1,x2,y2,label), ...]"
-    try:
-        elements = ast.literal_eval(layout_results)
-    except:
-        elements = []  # Fallback if parsing fails
-    
-    recognition_results = []
-    reading_order = 0
-    
-    for bbox, label in elements:
-        try:
-            x1, y1, x2, y2 = map(int, bbox)
-            cropped = image.crop((x1, y1, x2, y2))
-            if cropped.size[0] > 0 and cropped.size[1] > 0:
-                if label == "text":
-                    text = model_chat("Read text in the image.", cropped)
-                    recognition_results.append({
-                        "label": label,
-                        "bbox": [x1, y1, x2, y2],
-                        "text": text.strip(),
-                        "reading_order": reading_order
-                    })
-                elif label == "table":
-                    table_text = model_chat("Parse the table in the image.", cropped)
-                    recognition_results.append({
-                        "label": label,
-                        "bbox": [x1, y1, x2, y2],
-                        "text": table_text.strip(),
-                        "reading_order": reading_order
-                    })
-                elif label == "figure":
-                    recognition_results.append({
-                        "label": label,
-                        "bbox": [x1, y1, x2, y2],
-                        "text": "[Figure]",  # Placeholder for figure content
-                        "reading_order": reading_order
-                    })
-            reading_order += 1
-        except Exception as e:
-            print(f"Error processing element: {e}")
-            continue
-    
-    return recognition_results
-
-def generate_markdown(recognition_results):
-    """Generate markdown from extracted elements."""
-    markdown = ""
-    for element in sorted(recognition_results, key=lambda x: x["reading_order"]):
-        if element["label"] == "text":
-            markdown += f"{element['text']}\n\n"
-        elif element["label"] == "table":
-            markdown += f"**Table:**\n{element['text']}\n\n"
-        elif element["label"] == "figure":
-            markdown += f"{element['text']}\n\n"
-    return markdown.strip()
-
-def process_image_with_dolphin(image):
-    """Process a single image with Dolphin model."""
-    layout_output = model_chat("Parse the reading order of this document.", image)
-    elements = process_elements(layout_output, image)
-    markdown_content = generate_markdown(elements)
-    return markdown_content
-
 @spaces.GPU
 def generate_image(model_name: str, text: str, image: Image.Image,
                    max_new_tokens: int = 1024,
@@ -230,82 +122,81 @@ def generate_image(model_name: str, text: str, image: Image.Image,
                    top_k: int = 50,
                    repetition_penalty: float = 1.2):
     """Generate responses for image input using the selected model."""
-    if model_name == "ByteDance-s-Dolphin":
-        if image is None:
-            yield "Please upload an image."
-            return
-        markdown_content = process_image_with_dolphin(image)
-        yield markdown_content
+    # Model selection
+    if model_name == "Nanonets-OCR-s":
+        processor = processor_m
+        model = model_m
+    elif model_name == "MonkeyOCR-Recognition":
+        processor = processor_g
+        model = model_g
+    elif model_name == "SmolDocling-256M-preview":
+        processor = processor_x
+        model = model_x
     else:
-        # Existing logic for other models
-        if model_name == "Nanonets-OCR-s":
-            processor = processor_m
-            model = model_m
-        elif model_name == "MonkeyOCR-Recognition":
-            processor = processor_g
-            model = model_g
-        elif model_name == "SmolDocling-256M-preview":
-            processor = processor_x
-            model = model_x
-        else:
-            yield "Invalid model selected."
-            return
-
-        if image is None:
-            yield "Please upload an image."
-            return
-
-        images = [image]
-
-        if model_name == "SmolDocling-256M-preview":
-            if "OTSL" in text or "code" in text:
-                images = [add_random_padding(img) for img in images]
-            if "OCR at text at" in text or "Identify element" in text or "formula" in text:
-                text = normalize_values(text, target_max=500)
-
-        messages = [
-            {
-                "role": "user",
-                "content": [{"type": "image"} for _ in images] + [
-                    {"type": "text", "text": text}
-                ]
-            }
-        ]
-        prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
-        inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
-
-        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-        generation_kwargs = {
-            **inputs,
-            "streamer": streamer,
-            "max_new_tokens": max_new_tokens,
-            "temperature": temperature,
-            "top_p": top_p,
-            "top_k": top_k,
-            "repetition_penalty": repetition_penalty,
+        yield "Invalid model selected."
+        return
+
+    if image is None:
+        yield "Please upload an image."
+        return
+
+    # Prepare images as a list (single image for image inference)
+    images = [image]
+
+    # SmolDocling-256M specific preprocessing
+    if model_name == "SmolDocling-256M-preview":
+        if "OTSL" in text or "code" in text:
+            images = [add_random_padding(img) for img in images]
+        if "OCR at text at" in text or "Identify element" in text or "formula" in text:
+            text = normalize_values(text, target_max=500)
+
+    # Unified message structure for all models
+    messages = [
+        {
+            "role": "user",
+            "content": [{"type": "image"} for _ in images] + [
+                {"type": "text", "text": text}
+            ]
         }
-        thread = Thread(target=model.generate, kwargs=generation_kwargs)
-        thread.start()
-
-        buffer = ""
-        full_output = ""
-        for new_text in streamer:
-            full_output += new_text
-            buffer += new_text.replace("<|im_end|>", "")
-            yield buffer
-
-        if model_name == "SmolDocling-256M-preview":
-            cleaned_output = full_output.replace("<end_of_utterance>", "").strip()
-            if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
-                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)
-                doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
-                doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
-                markdown_output = doc.export_to_markdown()
-                yield f"**MD Output:**\n\n{markdown_output}"
-            else:
-                yield cleaned_output
+    ]
+    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+    inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
+
+    # Generation with streaming
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {
+        **inputs,
+        "streamer": streamer,
+        "max_new_tokens": max_new_tokens,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "repetition_penalty": repetition_penalty,
+    }
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    # Stream output and collect full response
+    buffer = ""
+    full_output = ""
+    for new_text in streamer:
+        full_output += new_text
+        buffer += new_text.replace("<|im_end|>", "")
+        yield buffer
+
+    # SmolDocling-256M specific postprocessing
+    if model_name == "SmolDocling-256M-preview":
+        cleaned_output = full_output.replace("<end_of_utterance>", "").strip()
+        if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
+            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)
+            doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
+            doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
+            markdown_output = doc.export_to_markdown()
+            yield f"**MD Output:**\n\n{markdown_output}"
+        else:
+            yield cleaned_output
 
 @spaces.GPU
 def generate_video(model_name: str, text: str, video_path: str,
@@ -315,94 +206,88 @@ def generate_video(model_name: str, text: str, video_path: str,
                    top_k: int = 50,
                    repetition_penalty: float = 1.2):
     """Generate responses for video input using the selected model."""
-    if model_name == "ByteDance-s-Dolphin":
-        if video_path is None:
-            yield "Please upload a video."
-            return
-        frames = downsample_video(video_path)
-        markdown_contents = []
-        for frame, _ in frames:
-            markdown_content = process_image_with_dolphin(frame)
-            markdown_contents.append(markdown_content)
-        combined_markdown = "\n\n".join(markdown_contents)
-        yield combined_markdown
+    # Model selection
+    if model_name == "Nanonets-OCR-s":
+        processor = processor_m
+        model = model_m
+    elif model_name == "MonkeyOCR-Recognition":
+        processor = processor_g
+        model = model_g
+    elif model_name == "SmolDocling-256M-preview":
+        processor = processor_x
+        model = model_x
     else:
-        # Existing logic for other models
-        if model_name == "Nanonets-OCR-s":
-            processor = processor_m
-            model = model_m
-        elif model_name == "MonkeyOCR-Recognition":
-            processor = processor_g
-            model = model_g
-        elif model_name == "SmolDocling-256M-preview":
-            processor = processor_x
-            model = model_x
-        else:
-            yield "Invalid model selected."
-            return
-
-        if video_path is None:
-            yield "Please upload a video."
-            return
-
-        frames = downsample_video(video_path)
-        images = [frame for frame, _ in frames]
-
-        if model_name == "SmolDocling-256M-preview":
-            if "OTSL" in text or "code" in text:
-                images = [add_random_padding(img) for img in images]
-            if "OCR at text at" in text or "Identify element" in text or "formula" in text:
-                text = normalize_values(text, target_max=500)
-
-        messages = [
-            {
-                "role": "user",
-                "content": [{"type": "image"} for _ in images] + [
-                    {"type": "text", "text": text}
-                ]
-            }
-        ]
-        prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
-        inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
-
-        streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
-        generation_kwargs = {
-            **inputs,
-            "streamer": streamer,
-            "max_new_tokens": max_new_tokens,
-            "temperature": temperature,
-            "top_p": top_p,
-            "top_k": top_k,
-            "repetition_penalty": repetition_penalty,
+        yield "Invalid model selected."
+        return
+
+    if video_path is None:
+        yield "Please upload a video."
+        return
+
+    # Extract frames from video
+    frames = downsample_video(video_path)
+    images = [frame for frame, _ in frames]
+
+    # SmolDocling-256M specific preprocessing
+    if model_name == "SmolDocling-256M-preview":
+        if "OTSL" in text or "code" in text:
+            images = [add_random_padding(img) for img in images]
+        if "OCR at text at" in text or "Identify element" in text or "formula" in text:
+            text = normalize_values(text, target_max=500)
+
+    # Unified message structure for all models
+    messages = [
+        {
+            "role": "user",
+            "content": [{"type": "image"} for _ in images] + [
+                {"type": "text", "text": text}
+            ]
         }
-        thread = Thread(target=model.generate, kwargs=generation_kwargs)
-        thread.start()
-
-        buffer = ""
-        full_output = ""
-        for new_text in streamer:
-            full_output += new_text
-            buffer += new_text.replace("<|im_end|>", "")
-            yield buffer
-
-        if model_name == "SmolDocling-256M-preview":
-            cleaned_output = full_output.replace("<end_of_utterance>", "").strip()
-            if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
-                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)
-                doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
-                doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
-                markdown_output = doc.export_to_markdown()
-                yield f"**MD Output:**\n\n{markdown_output}"
-            else:
-                yield cleaned_output
+    ]
+    prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+    inputs = processor(text=prompt, images=images, return_tensors="pt").to(device)
+
+    # Generation with streaming
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {
+        **inputs,
+        "streamer": streamer,
+        "max_new_tokens": max_new_tokens,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "repetition_penalty": repetition_penalty,
+    }
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    # Stream output and collect full response
+    buffer = ""
+    full_output = ""
+    for new_text in streamer:
+        full_output += new_text
+        buffer += new_text.replace("<|im_end|>", "")
+        yield buffer
+
+    # SmolDocling-256M specific postprocessing
+    if model_name == "SmolDocling-256M-preview":
+        cleaned_output = full_output.replace("<end_of_utterance>", "").strip()
+        if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
+            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)
+            doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([cleaned_output], images)
+            doc = DoclingDocument.load_from_doctags(doctags_doc, document_name="Document")
+            markdown_output = doc.export_to_markdown()
+            yield f"**MD Output:**\n\n{markdown_output}"
+        else:
+            yield cleaned_output
 
 # Define examples for image and video inference
 image_examples = [
-    ["Convert this page to docling", "images/1.png"],
-    ["OCR the image", "images/2.jpg"],
-    ["Convert this page to docling", "images/3.png"],
+    ["fill the correct numbers", "example/image3.png"],
+    ["ocr the image", "example/image1.png"],
+    ["explain the scene", "example/image2.jpg"],
 ]
 
 video_examples = [
@@ -422,7 +307,7 @@ css = """
 
 # Create the Gradio Interface
 with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
-    gr.Markdown("# **[Core OCR](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
+    gr.Markdown("# **[core OCR](https://huggingface.co/collections/prithivMLmods/core-and-docscope-ocr-models-6816d7f1bde3f911c6c852bc)**")
     with gr.Row():
         with gr.Column():
             with gr.Tabs():
@@ -451,7 +336,7 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
         with gr.Column():
             output = gr.Textbox(label="Output", interactive=False, lines=3, scale=2)
             model_choice = gr.Radio(
-                choices=["Nanonets-OCR-s", "SmolDocling-256M-preview", "MonkeyOCR-Recognition", "ByteDance-s-Dolphin"],
+                choices=["Nanonets-OCR-s", "MonkeyOCR-Recognition", "SmolDocling-256M-preview"],
                 label="Select Model",
                 value="Nanonets-OCR-s"
             )