Update app.py

app.py

@@ -10,26 +10,17 @@ import gradio as gr
 import spaces
 import torch
 import numpy as np
-from PIL import Image, ImageOps
+from PIL import Image
 import cv2
-import pymupdf
-import io
 
 from transformers import (
     Qwen2VLForConditionalGeneration,
-    VisionEncoderDecoderModel,
-    AutoModelForVision2Seq,
+    Qwen2_5_VLForConditionalGeneration,
     AutoProcessor,
     TextIteratorStreamer,
 )
 from transformers.image_utils import load_image
 
-from docling_core.types.doc import DoclingDocument, DocTagsDocument
-
-import re
-import ast
-import html
-
 # Constants for text generation
 MAX_MAX_NEW_TOKENS = 2048
 DEFAULT_MAX_NEW_TOKENS = 1024
@@ -37,71 +28,29 @@ MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-# Global variables for Dolphin model
-model_k = None
-processor_k = None
-tokenizer_k = None
-
-# Load models
-def initialize_models():
-    global model_k, processor_k, tokenizer_k
-    # Load olmOCR-7B-0225-preview
-    MODEL_ID_M = "allenai/olmOCR-7B-0225-preview"
-    processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
-    model_m = Qwen2VLForConditionalGeneration.from_pretrained(
-        MODEL_ID_M, trust_remote_code=True, 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)
-    if model_k is None:
-        model_k = VisionEncoderDecoderModel.from_pretrained(
-            MODEL_ID_K, trust_remote_code=True, torch_dtype=torch.float16
-        ).to(device).eval()
-        tokenizer_k = processor_k.tokenizer
-
-    # Load SmolDocling-256M-preview
-    MODEL_ID_X = "ds4sd/SmolDocling-256M-preview"
-    processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
-    model_x = AutoModelForVision2Seq.from_pretrained(
-        MODEL_ID_X, trust_remote_code=True, torch_dtype=torch.float16
-    ).to(device).eval()
-
-    return processor_m, model_m, processor_x, model_x
-
-processor_m, model_m, processor_x, model_x = initialize_models()
-
-# Preprocessing functions for SmolDocling-256M
-def add_random_padding(image, min_percent=0.1, max_percent=0.10):
-    """Add random padding to an image based on its size."""
-    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))
-    padded_image = ImageOps.expand(image, border=(pad_w, pad_h, pad_w, pad_h), fill=corner_pixel)
-    return padded_image
-
-def normalize_values(text, target_max=500):
-    """Normalize numerical values in text to a target maximum."""
-    def normalize_list(values):
-        max_value = max(values) if values else 1
-        return [round((v / max_value) * target_max) for v in values]
-
-    def process_match(match):
-        num_list = ast.literal_eval(match.group(0))
-        normalized = normalize_list(num_list)
-        return "".join([f"<loc_{num}>" for num in normalized])
-
-    pattern = r"\[([\d\.\s,]+)\]"
-    normalized_text = re.sub(pattern, process_match, text)
-    return normalized_text
+# Load VIREX-062225-exp
+MODEL_ID_M = "prithivMLmods/VIREX-062225-exp"
+processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True)
+model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_M,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+# Load DREX-062225-exp
+MODEL_ID_X = "prithivMLmods/DREX-062225-exp"
+processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True)
+model_x = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_X,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
 
 def downsample_video(video_path):
-    """Downsample a video to evenly spaced frames, returning PIL images with timestamps."""
+    """
+    Downsamples the video to evenly spaced frames.
+    Each frame is returned as a PIL image along with its timestamp.
+    """
     vidcap = cv2.VideoCapture(video_path)
     total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
     fps = vidcap.get(cv2.CAP_PROP_FPS)
@@ -118,343 +67,128 @@ def downsample_video(video_path):
     vidcap.release()
     return frames
 
-# Dolphin-specific functions
-@spaces.GPU
-def model_chat(prompt, image, is_batch=False):
-    """Use Dolphin model for inference, supporting both single and batch processing."""
-    global model_k, processor_k, tokenizer_k
-    if model_k is None:
-        initialize_models()
-
-    if not is_batch:
-        images = [image]
-        prompts = [prompt]
-    else:
-        images = image
-        prompts = prompt if isinstance(prompt, list) else [prompt] * len(images)
-
-    inputs = processor_k(images, return_tensors="pt", padding=True).to(device)
-    pixel_values = inputs.pixel_values.half()
-
-    prompts = [f"<s>{p} <Answer/>" for p in prompts]
-    prompt_inputs = tokenizer_k(
-        prompts, add_special_tokens=False, return_tensors="pt", padding=True
-    ).to(device)
-
-    outputs = model_k.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=tokenizer_k.pad_token_id,
-        eos_token_id=tokenizer_k.eos_token_id,
-        use_cache=True,
-        bad_words_ids=[[tokenizer_k.unk_token_id]],
-        return_dict_in_generate=True,
-        do_sample=False,
-        num_beams=1,
-        repetition_penalty=1.1
-    )
-    sequences = tokenizer_k.batch_decode(outputs.sequences, skip_special_tokens=False)
-    
-    results = []
-    for i, sequence in enumerate(sequences):
-        cleaned = sequence.replace(prompts[i], "").replace("<pad>", "").replace("</s>", "").strip()
-        results.append(cleaned)
-
-    return results[0] if not is_batch else results
-
-@spaces.GPU
-def process_element_batch(elements, prompt, max_batch_size=16):
-    """Process a batch of elements with the same prompt."""
-    results = []
-    batch_size = min(len(elements), max_batch_size)
-
-    for i in range(0, len(elements), batch_size):
-        batch_elements = elements[i:i + batch_size]
-        crops_list = [elem["crop"] for elem in batch_elements]
-        prompts_list = [prompt] * len(crops_list)
-        
-        batch_results = model_chat(prompts_list, crops_list, is_batch=True)
-        
-        for j, result in enumerate(batch_results):
-            elem = batch_elements[j]
-            results.append({
-                "label": elem["label"],
-                "bbox": elem["bbox"],
-                "text": result.strip(),
-                "reading_order": elem["reading_order"],
-            })
-    
-    return results
-
-def process_elements(layout_results, image):
-    """Parse layout results and extract elements from the image."""
-    try:
-        elements = ast.literal_eval(layout_results)
-    except:
-        elements = []
-    
-    text_elements = []
-    table_elements = []
-    figure_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:
-                element_info = {
-                    "crop": cropped,
-                    "label": label,
-                    "bbox": [x1, y1, x2, y2],
-                    "reading_order": reading_order,
-                }
-                if label == "text":
-                    text_elements.append(element_info)
-                elif label == "table":
-                    table_elements.append(element_info)
-                elif label == "figure":
-                    figure_results.append({
-                        "label": label,
-                        "bbox": [x1, y1, x2, y2],
-                        "text": "[Figure]",
-                        "reading_order": reading_order
-                    })
-            reading_order += 1
-        except Exception as e:
-            print(f"Error processing element: {e}")
-            continue
-    
-    recognition_results = figure_results.copy()
-    
-    if text_elements:
-        text_results = process_element_batch(text_elements, "Read text in the image.")
-        recognition_results.extend(text_results)
-    
-    if table_elements:
-        table_results = process_element_batch(table_elements, "Parse the table in the image.")
-        recognition_results.extend(table_results)
-    
-    recognition_results.sort(key=lambda x: x["reading_order"])
-    return recognition_results
-
-def generate_markdown(recognition_results):
-    """Generate markdown from extracted elements."""
-    markdown = ""
-    for element in recognition_results:
-        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 convert_to_image(image):
-    """Convert uploaded file to PIL Image, handling PDFs by extracting the first page."""
-    if isinstance(image, str):  # File path from Gradio
-        if image.lower().endswith('.pdf'):
-            doc = pymupdf.open(image)
-            page = doc[0]
-            pix = page.get_pixmap()
-            img_data = pix.tobytes("png")
-            pil_image = Image.open(io.BytesIO(img_data)).convert("RGB")
-            doc.close()
-            return pil_image
-        else:
-            return Image.open(image).convert("RGB")
-    elif isinstance(image, Image.Image):  # Already a PIL Image
-        return image.convert("RGB")
-    return None
-
-def process_image_with_dolphin(image):
-    """Process a single image with Dolphin model."""
-    pil_image = convert_to_image(image)
-    if pil_image is None:
-        return "Error: Unable to process the uploaded file."
-    layout_output = model_chat("Parse the reading order of this document.", pil_image)
-    elements = process_elements(layout_output, pil_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, temperature: float = 0.6,
-                   top_p: float = 0.9, 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 or PDF (first page will be processed)."
-            return
-        markdown_content = process_image_with_dolphin(image)
-        yield markdown_content
+                   max_new_tokens: int = 1024,
+                   temperature: float = 0.6,
+                   top_p: float = 0.9,
+                   top_k: int = 50,
+                   repetition_penalty: float = 1.2):
+    """
+    Generates responses using the selected model for image input.
+    """
+    if model_name == "VIREX-062225-exp":
+        processor = processor_m
+        model = model_m
+    elif model_name == "DREX-062225-exp":
+        processor = processor_x
+        model = model_x
     else:
-        if model_name == "olmOCR-7B-0225-preview":
-            processor = processor_m
-            model = model_m
-        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 = [convert_to_image(image)]
-        if images[0] is None:
-            yield "Error: Unable to process the uploaded file."
-            return
-
-        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}
-                ]
-            }
+        yield "Invalid model selected."
+        return
+
+    if image is None:
+        yield "Please upload an image."
+        return
+
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"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,
-        }
-        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_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = processor(
+        text=[prompt_full],
+        images=[image],
+        return_tensors="pt",
+        padding=True,
+        truncation=False,
+        max_length=MAX_INPUT_TOKEN_LENGTH
+    ).to(device)
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        buffer = buffer.replace("<|im_end|>", "")
+        time.sleep(0.01)
+        yield buffer
 
 @spaces.GPU
 def generate_video(model_name: str, text: str, video_path: str,
-                   max_new_tokens: int = 1024, temperature: float = 0.6,
-                   top_p: float = 0.9, 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 idx, (frame, _) in enumerate(frames):
-            markdown_content = process_image_with_dolphin(frame)
-            markdown_contents.append(f"**Frame {idx + 1}:**\n{markdown_content}")
-        combined_markdown = "\n\n---\n\n".join(markdown_contents)
-        yield combined_markdown
+                   max_new_tokens: int = 1024,
+                   temperature: float = 0.6,
+                   top_p: float = 0.9,
+                   top_k: int = 50,
+                   repetition_penalty: float = 1.2):
+    """
+    Generates responses using the selected model for video input.
+    """
+    if model_name == "VIREX-062225-exp":
+        processor = processor_m
+        model = model_m
+    elif model_name == "DREX-062225-exp":
+        processor = processor_x
+        model = model_x
     else:
-        if model_name == "olmOCR-7B-0225-preview":
-            processor = processor_m
-            model = model_m
-        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,
-        }
-        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
-
-# Define examples
+        yield "Invalid model selected."
+        return
+
+    if video_path is None:
+        yield "Please upload a video."
+        return
+
+    frames = downsample_video(video_path)
+    messages = [
+        {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
+        {"role": "user", "content": [{"type": "text", "text": text}]}
+    ]
+    for frame in frames:
+        image, timestamp = frame
+        messages[1]["content"].append({"type": "text", "text": f"Frame {timestamp}:"})
+        messages[1]["content"].append({"type": "image", "image": image})
+    inputs = processor.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_dict=True,
+        return_tensors="pt",
+        truncation=False,
+        max_length=MAX_INPUT_TOKEN_LENGTH
+    ).to(device)
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = {
+        **inputs,
+        "streamer": streamer,
+        "max_new_tokens": max_new_tokens,
+        "do_sample": True,
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "repetition_penalty": repetition_penalty,
+    }
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    buffer = ""
+    for new_text in streamer:
+        buffer += new_text
+        buffer = buffer.replace("<|im_end|>", "")
+        time.sleep(0.01)
+        yield buffer
+
+# 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"],
+    ["Perform OCR on the Image.", "images/1.jpg"],
+    ["Extract the table content", "images/2.png"]
 ]
 
 video_examples = [
-    ["Explain the ad in detail", "example/1.mp4"],
-    ["Identify the main actions in the coca cola ad...", "example/2.mp4"]
+    ["Explain the Ad in Detail", "videos/1.mp4"],
+    ["Identify the main actions in the cartoon video", "videos/2.mp4"]
 ]
 
 css = """
@@ -467,23 +201,28 @@ css = """
 }
 """
 
-# Create Gradio Interface
+# Create the Gradio Interface
 with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
-    gr.Markdown("# **[Docling-VLMs](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
-    gr.Markdown("**Note:** For Dolphin model, the text query is ignored, and PDFs are processed by parsing the first page.")
+    gr.Markdown("# **Multimodal OCR**")
     with gr.Row():
         with gr.Column():
             with gr.Tabs():
                 with gr.TabItem("Image Inference"):
                     image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
-                    image_upload = gr.Image(type="pil", label="Image or PDF")
+                    image_upload = gr.Image(type="pil", label="Image")
                     image_submit = gr.Button("Submit", elem_classes="submit-btn")
-                    gr.Examples(examples=image_examples, inputs=[image_query, image_upload])
+                    gr.Examples(
+                        examples=image_examples,
+                        inputs=[image_query, image_upload]
+                    )
                 with gr.TabItem("Video Inference"):
                     video_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
                     video_upload = gr.Video(label="Video")
                     video_submit = gr.Button("Submit", elem_classes="submit-btn")
-                    gr.Examples(examples=video_examples, inputs=[video_query, video_upload])
+                    gr.Examples(
+                        examples=video_examples,
+                        inputs=[video_query, video_upload]
+                    )
             with gr.Accordion("Advanced options", open=False):
                 max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
                 temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6)
@@ -491,13 +230,19 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
                 top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50)
                 repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2)
         with gr.Column():
-            output = gr.Textbox(label="Output", interactive=False, lines=3, scale=2)
+            output = gr.Textbox(label="Output", interactive=False, lines=2, scale=2)
             model_choice = gr.Radio(
-                choices=["olmOCR-7B-0225-preview", "SmolDocling-256M-preview", "ByteDance-s-Dolphin"],
+                choices=["DREX-062225-exp", "VIREX-062225-exp"],
                 label="Select Model",
-                value="olmOCR-7B-0225-preview"
+                value="VIREX-062225-exp"
             )
             
+            gr.Markdown("**Model Info 💻 | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Doc-VLMs/discussions)**")
+            gr.Markdown("> [Qwen2-VL-OCR-2B-Instruct](https://huggingface.co/prithivMLmods/Qwen2-VL-OCR-2B-Instruct): qwen2-vl-ocr-2b-instruct model is a fine-tuned version of qwen2-vl-2b-instruct, tailored for tasks that involve [messy] optical character recognition (ocr), image-to-text conversion, and math problem solving with latex formatting.")
+            gr.Markdown("> [Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s): nanonets-ocr-s is a powerful, state-of-the-art image-to-markdown ocr model that goes far beyond traditional text extraction. it transforms documents into structured markdown with intelligent content recognition and semantic tagging.")
+            gr.Markdown("> [RolmOCR](https://huggingface.co/reducto/RolmOCR): rolmocr, high-quality, openly available approach to parsing pdfs and other complex documents oprical character recognition. it is designed to handle a wide range of document types, including scanned documents, handwritten text, and complex layouts.")
+            gr.Markdown("> [Aya-Vision](https://huggingface.co/CohereLabs/aya-vision-8b): cohere labs aya vision 8b is an open weights research release of an 8-billion parameter model with advanced capabilities optimized for a variety of vision-language use cases, including ocr, captioning, visual reasoning, summarization, question answering, code, and more.")
+
     image_submit.click(
         fn=generate_image,
         inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],