Update app.py

app.py

@@ -1,7 +1,8 @@
 import os
 import random
-import re
-import ast
+import uuid
+import json
+import time
 import asyncio
 from threading import Thread
 
@@ -11,69 +12,82 @@ import torch
 import numpy as np
 from PIL import Image, ImageOps
 import cv2
+
 from transformers import (
+    Qwen2VLForConditionalGeneration,
     Qwen2_5_VLForConditionalGeneration,
+    AutoModelForCausalLM,
     AutoModelForVision2Seq,
     AutoProcessor,
     TextIteratorStreamer,
 )
+from transformers.image_utils import load_image
 
 from docling_core.types.doc import DoclingDocument, DocTagsDocument
 
-# --- Constants ---
+import re
+import ast
+import html
+
+# Constants for text generation
 MAX_MAX_NEW_TOKENS = 5120
 DEFAULT_MAX_NEW_TOKENS = 3072
 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
-DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# --- Model Loading ---
-def load_model(model_id, model_class, subfolder=None):
-    """Generic function to load a model and its processor."""
-    processor_kwargs = {"trust_remote_code": True}
-    model_kwargs = {"trust_remote_code": True, "torch_dtype": torch.float16}
 
-    if subfolder:
-        processor_kwargs["subfolder"] = subfolder
-        model_kwargs["subfolder"] = subfolder
-
-    processor = AutoProcessor.from_pretrained(model_id, **processor_kwargs)
-    model = model_class.from_pretrained(model_id, **model_kwargs).to(DEVICE).eval()
-    return processor, model
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 # Load Nanonets-OCR-s
-processor_m, model_m = load_model(
-    "nanonets/Nanonets-OCR-s", Qwen2_5_VLForConditionalGeneration
-)
+MODEL_ID_M = "nanonets/Nanonets-OCR-s"
+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 MonkeyOCR
-processor_g, model_g = load_model(
-    "echo840/MonkeyOCR", Qwen2_5_VLForConditionalGeneration, subfolder="Recognition"
+MODEL_ID_G = "echo840/MonkeyOCR"
+SUBFOLDER = "Recognition"
+processor_g = AutoProcessor.from_pretrained(
+    MODEL_ID_G,
+    trust_remote_code=True,
+    subfolder=SUBFOLDER
 )
+model_g = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_G,
+    trust_remote_code=True,
+    subfolder=SUBFOLDER,
+    torch_dtype=torch.float16
+).to(device).eval()
 
 # Load Typhoon-OCR-7B
-processor_l, model_l = load_model(
-    "scb10x/typhoon-ocr-7b", Qwen2_5_VLForConditionalGeneration
-)
+MODEL_ID_L = "scb10x/typhoon-ocr-7b"
+processor_l = AutoProcessor.from_pretrained(MODEL_ID_L, trust_remote_code=True)
+model_l = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_L,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
 
 # Load SmolDocling-256M-preview
-processor_x, model_x = load_model(
-    "ds4sd/SmolDocling-256M-preview", AutoModelForVision2Seq
-)
+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()
 
 # Thyme-RL
-processor_n, model_n = load_model(
-    "Kwai-Keye/Thyme-RL", Qwen2_5_VLForConditionalGeneration
-)
-
-MODEL_MAPPING = {
-    "Nanonets-OCR-s": (processor_m, model_m),
-    "MonkeyOCR-Recognition": (processor_g, model_g),
-    "Typhoon-OCR-7B": (processor_l, model_l),
-    "SmolDocling-256M-preview": (processor_x, model_x),
-    "Thyme-RL": (processor_n, model_n),
-}
-
-# --- Preprocessing Functions ---
+MODEL_ID_N = "Kwai-Keye/Thyme-RL"
+processor_n = AutoProcessor.from_pretrained(MODEL_ID_N, trust_remote_code=True)
+model_n = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_N,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+# 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")
@@ -82,81 +96,94 @@ def add_random_padding(image, min_percent=0.1, max_percent=0.10):
     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
-    )
+    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 normalize_values(text, target_max=500):
-    """Normalize numerical values in text to a target maximum for SmolDocling."""
+    """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):
-        try:
-            num_list = ast.literal_eval(match.group(0))
-            normalized = normalize_list(num_list)
-            return "".join([f"<loc_{num}>" for num in normalized])
-        except (ValueError, SyntaxError):
-            return match.group(0)
+        num_list = ast.literal_eval(match.group(0))
+        normalized = normalize_list(num_list)
+        return "".join([f"<loc_{num}>" for num in normalized])
 
     pattern = r"\[([\d\.\s,]+)\]"
-    return re.sub(pattern, process_match, text)
+    normalized_text = re.sub(pattern, process_match, text)
+    return normalized_text
 
-def downsample_video(video_path, num_frames=10):
-    """Downsample a video to evenly spaced frames, returning PIL images."""
-    if not video_path:
-        return []
+def downsample_video(video_path):
+    """Downsample a video to evenly spaced frames, returning PIL images with timestamps."""
     vidcap = cv2.VideoCapture(video_path)
     total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = vidcap.get(cv2.CAP_PROP_FPS)
     frames = []
-    frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
-
+    frame_indices = np.linspace(0, total_frames - 1, 10, dtype=int)
     for i in frame_indices:
         vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
         success, image = vidcap.read()
         if success:
-            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            frames.append(Image.fromarray(image_rgb))
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            pil_image = Image.fromarray(image)
+            timestamp = round(i / fps, 2)
+            frames.append((pil_image, timestamp))
     vidcap.release()
     return frames
 
-# --- Core Generation Logic ---
-def _generate_response(model_name, text, images, max_new_tokens, temperature, top_p, top_k, repetition_penalty):
-    """Helper function to handle model inference."""
-    if not images:
-        yield "Please upload an image or video.", ""
+@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 == "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
+    elif model_name == "Typhoon-OCR-7B":
+        processor = processor_l
+        model = model_l
+    elif model_name == "Thyme-RL":
+        processor = processor_n
+        model = model_n
+    else:
+        yield "Invalid model selected.", "Invalid model selected."
         return
 
-    try:
-        processor, model = MODEL_MAPPING[model_name]
-    except KeyError:
-        yield "Invalid model selected.", ""
+    if image is None:
+        yield "Please upload an image.", "Please upload an image."
         return
 
-    # Model-specific preprocessing
+    images = [image]
+
     if model_name == "SmolDocling-256M-preview":
-        if any(keyword in text for keyword in ["OTSL", "code"]):
+        if "OTSL" in text or "code" in text:
             images = [add_random_padding(img) for img in images]
-        if any(keyword in text for keyword in ["OCR at text at", "Identify element", "formula"]):
+        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"}] * len(images) + [{"type": "text", "text": text}],
+            "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
-    )
+    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,
@@ -166,7 +193,6 @@ def _generate_response(model_name, text, images, max_new_tokens, temperature, to
         "top_k": top_k,
         "repetition_penalty": repetition_penalty,
     }
-
     thread = Thread(target=model.generate, kwargs=generation_kwargs)
     thread.start()
 
@@ -175,70 +201,102 @@ def _generate_response(model_name, text, images, max_new_tokens, temperature, to
         buffer += new_text.replace("<|im_end|>", "")
         yield buffer, buffer
 
-    # Model-specific post-processing
     if model_name == "SmolDocling-256M-preview":
         cleaned_output = buffer.replace("<end_of_utterance>", "").strip()
-        is_doc_tag = any(
-            tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]
-        )
-        if is_doc_tag:
+        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)
-            
-            try:
-                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 buffer, markdown_output
-            except Exception as e:
-                yield buffer, f"Error processing Docling output: {e}"
+            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 buffer, markdown_output
         else:
             yield buffer, cleaned_output
 
 @spaces.GPU
-def generate_for_image(model_name, text, image, *args):
-    """Generate responses for a single image input."""
-    if image is None:
-        yield "Please upload an image.", ""
+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 == "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
+    elif model_name == "Typhoon-OCR-7B":
+        processor = processor_l
+        model = model_l
+    elif model_name == "Thyme-RL":
+        processor = processor_n
+        model = model_n
+    else:
+        yield "Invalid model selected.", "Invalid model selected."
         return
-    yield from _generate_response(model_name, text, [image], *args)
 
-
-@spaces.GPU
-def generate_for_video(model_name, text, video_path, *args):
-    """Generate responses for video input by downsampling frames."""
     if video_path is None:
-        yield "Please upload a video.", ""
+        yield "Please upload a video.", "Please upload a video."
         return
+
     frames = downsample_video(video_path)
-    if not frames:
-        yield "Could not process video. Please check the file.", ""
-        return
-    yield from _generate_response(model_name, text, frames, *args)
+    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)
 
-# --- Gradio Interface ---
-css = """
-.submit-btn {
-    background-color: #2980b9 !important;
-    color: white !important;
-    font-weight: bold !important;
-    border: none !important;
-    transition: background-color 0.3s ease;
-}
-.submit-btn:hover {
-    background-color: #3498db !important;
-}
-.output-container {
-    border: 2px solid #4682B4;
-    border-radius: 10px;
-    padding: 20px;
-    height: 100%;
-}
-"""
+    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 = ""
+    for new_text in streamer:
+        buffer += new_text.replace("<|im_end|>", "")
+        yield buffer, buffer
+
+    if model_name == "SmolDocling-256M-preview":
+        cleaned_output = buffer.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 buffer, markdown_output
+        else:
+            yield buffer, cleaned_output
 
-# Define examples
+# Define examples for image and video inference
 image_examples = [
     ["Reconstruct the doc [table] as it is.", "images/0.png"],
     ["Describe the image!", "images/8.png"],
@@ -248,101 +306,92 @@ image_examples = [
     ["Convert chart to OTSL.", "images/4.png"],
     ["Convert code to text", "images/5.jpg"],
     ["Convert this table to OTSL.", "images/6.jpg"],
-    ["Convert formula to latex.", "images/7.jpg"],
+    ["Convert formula to late.", "images/7.jpg"],
 ]
 
 video_examples = [
     ["Explain the video in detail.", "videos/1.mp4"],
-    ["Explain the video in detail.", "videos/2.mp4"],
+    ["Explain the video in detail.", "videos/2.mp4"]
 ]
 
+#css
+css = """
+.submit-btn {
+    background-color: #2980b9 !important;
+    color: white !important;
+}
+.submit-btn:hover {
+    background-color: #3498db !important;
+}
+.canvas-output {
+    border: 2px solid #4682B4;
+    border-radius: 10px;
+    padding: 20px;
+}
+"""
+
+# Create the Gradio Interface
 with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
     gr.Markdown("# **[Multimodal OCR2](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
-    gr.Markdown("A unified interface for state-of-the-art multimodal and document AI models. Select a model, upload an image or video, and enter a query to begin.")
-
     with gr.Row():
-        # --- LEFT COLUMN (INPUTS) ---
-        with gr.Column(scale=1):
-            model_choice = gr.Dropdown(
-                choices=[
-                    "Nanonets-OCR-s",
-                    "MonkeyOCR-Recognition",
-                    "Thyme-RL",
-                    "Typhoon-OCR-7B",
-                    "SmolDocling-256M-preview",
-                ],
-                label="Select Model⚡",
-                value="Nanonets-OCR-s",
-            )
-
+        with gr.Column():
             with gr.Tabs():
-                with gr.TabItem("🖼️ Image Inference"):
-                    image_query = gr.Textbox(label="Query", placeholder="e.g., 'OCR the document'")
-                    image_upload = gr.Image(type="pil", label="Upload Image", height="299")
-                    image_submit = gr.Button("Generate", elem_classes="submit-btn")
-                    gr.Examples(examples=image_examples, inputs=[image_query, image_upload])
-
-                with gr.TabItem("🎬 Video Inference"):
-                    video_query = gr.Textbox(label="Query", placeholder="e.g., 'What is happening in this video?'")
-                    video_upload = gr.Video(label="Upload Video", height="299")
-                    video_submit = gr.Button("Generate", elem_classes="submit-btn")
-                    gr.Examples(examples=video_examples, inputs=[video_query, video_upload])
-            
-            with gr.Accordion("⚙️ Advanced Options", open=False):
-                # FIX: Changed 'min' to 'minimum' and 'max' to 'maximum'
-                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=2.0, step=0.1, value=0.6
-                )
-                top_p = gr.Slider(
-                    label="Top-P", minimum=0.05, maximum=1.0, step=0.05, value=0.9
-                )
-                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
-                )
-            
-            advanced_params = [max_new_tokens, temperature, top_p, top_k, repetition_penalty]
-
-        # --- RIGHT COLUMN (OUTPUTS & INFO) ---
-        with gr.Column(scale=2):
-            with gr.Column(elem_classes="output-container"):
+                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", height=290)
+                    image_submit = gr.Button("Submit", elem_classes="submit-btn")
+                    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", height=290)
+                    video_submit = gr.Button("Submit", elem_classes="submit-btn")
+                    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)
+                top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9)
+                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():
+            with gr.Column(elem_classes="canvas-output"):
                 gr.Markdown("## Output")
-                raw_output = gr.Textbox(
-                    label="Raw Output Stream", interactive=False, lines=8
-                )
+                raw_output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=5)
+                
                 with gr.Accordion("(Result.md)", open=False):
-                    formatted_output = gr.Markdown(label="Formatted Result (Markdown)")
+                    formatted_output = gr.Markdown(label="(Result.md)")
+            
+            model_choice = gr.Radio(
+                choices=["Nanonets-OCR-s", "MonkeyOCR-Recognition", "Thyme-RL", "Typhoon-OCR-7B", "SmolDocling-256M-preview"],
+                label="Select Model",
+                value="Nanonets-OCR-s"
+            )
+            
+            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR2/discussions)")
+            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("> [SmolDocling-256M](https://huggingface.co/ds4sd/SmolDocling-256M-preview): SmolDocling is a multimodal Image-Text-to-Text model designed for efficient document conversion. It retains Docling's most popular features while ensuring full compatibility with Docling through seamless support for DoclingDocuments.")
+            gr.Markdown("> [MonkeyOCR-Recognition](https://huggingface.co/echo840/MonkeyOCR): MonkeyOCR adopts a Structure-Recognition-Relation (SRR) triplet paradigm, which simplifies the multi-tool pipeline of modular approaches while avoiding the inefficiency of using large multimodal models for full-page document processing.")
+            gr.Markdown("> [Typhoon-OCR-7B](https://huggingface.co/scb10x/typhoon-ocr-7b): A bilingual document parsing model built specifically for real-world documents in Thai and English inspired by models like olmOCR based on Qwen2.5-VL-Instruction. Extracts and interprets embedded text (e.g., chart labels, captions) in Thai or English.")
+            gr.Markdown("> [Thyme-RL](https://huggingface.co/Kwai-Keye/Thyme-RL): Thyme: Think Beyond Images. Thyme transcends traditional ``thinking with images'' paradigms by autonomously generating and executing diverse image processing and computational operations through executable code, significantly enhancing performance on high-resolution perception and complex reasoning tasks.")
+            gr.Markdown(">⚠️note: all the models in space are not guaranteed to perform well in video inference use cases.")  
             
-            with gr.Accordion("💻 Model Information", open=True):
-                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.
-                    - **[SmolDocling-256M](https://huggingface.co/ds4sd/SmolDocling-256M-preview)**:  SmolDocling is a multimodal Image-Text-to-Text model designed for efficient document conversion. It retains Docling's most popular features while ensuring full compatibility with Docling through seamless support for DoclingDocuments.
-                    - **[MonkeyOCR-Recognition](https://huggingface.co/echo840/MonkeyOCR)**: MonkeyOCR adopts a Structure-Recognition-Relation (SRR) triplet paradigm, which simplifies the multi-tool pipeline of modular approaches while avoiding the inefficiency of using large multimodal models for full-page document processing.
-                    - **[Typhoon-OCR-7B](https://huggingface.co/scb10x/typhoon-ocr-7b)**: A bilingual document parsing model built specifically for real-world documents in Thai and English inspired by models like olmOCR based on Qwen2.5-VL-Instruction. Extracts and interprets embedded text (e.g., chart labels, captions) in Thai or English.
-                    - **[Thyme-RL](https://huggingface.co/Kwai-Keye/Thyme-RL)**: Think Beyond Images. Thyme transcends traditional ``thinking with images'' paradigms by autonomously generating and executing diverse image processing and computational operations through executable code, significantly enhancing performance on high-resolution perception and complex reasoning tasks.
-
-                    - **⚠️Note**: Performance on video inference tasks is experimental and may vary between models.
-                    
-                    > [Report a Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR2/discussions)
-                    """
-                )
-
-    # --- Event Handlers ---
     image_submit.click(
-        fn=generate_for_image,
-        inputs=[model_choice, image_query, image_upload] + advanced_params,
-        outputs=[raw_output, formatted_output],
+        fn=generate_image,
+        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        outputs=[raw_output, formatted_output]
     )
-
     video_submit.click(
-        fn=generate_for_video,
-        inputs=[model_choice, video_query, video_upload] + advanced_params,
-        outputs=[raw_output, formatted_output],
+        fn=generate_video,
+        inputs=[model_choice, video_query, video_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        outputs=[raw_output, 
+                 formatted_output]
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=50).launch(share=True, show_error=True)
+    demo.queue(max_size=50).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)