Update app.py

app.py

@@ -1,8 +1,7 @@
 import os
 import random
-import uuid
-import json
-import time
+import re
+import ast
 import asyncio
 from threading import Thread
 
@@ -12,82 +11,69 @@ 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
 
-import re
-import ast
-import html
-
-# Constants for text generation
+# --- Constants ---
 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}
 
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    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
 
 # Load Nanonets-OCR-s
-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()
+processor_m, model_m = load_model(
+    "nanonets/Nanonets-OCR-s", Qwen2_5_VLForConditionalGeneration
+)
 
 # Load MonkeyOCR
-MODEL_ID_G = "echo840/MonkeyOCR"
-SUBFOLDER = "Recognition"
-processor_g = AutoProcessor.from_pretrained(
-    MODEL_ID_G,
-    trust_remote_code=True,
-    subfolder=SUBFOLDER
+processor_g, model_g = load_model(
+    "echo840/MonkeyOCR", Qwen2_5_VLForConditionalGeneration, subfolder="Recognition"
 )
-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
-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()
+processor_l, model_l = load_model(
+    "scb10x/typhoon-ocr-7b", Qwen2_5_VLForConditionalGeneration
+)
 
 # 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()
+processor_x, model_x = load_model(
+    "ds4sd/SmolDocling-256M-preview", AutoModelForVision2Seq
+)
 
 # Thyme-RL
-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
+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 ---
 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")
@@ -96,94 +82,81 @@ 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))  # Top-left corner
-    padded_image = ImageOps.expand(image, border=(pad_w, pad_h, pad_w, pad_h), fill=corner_pixel)
+    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."""
+    """Normalize numerical values in text to a target maximum for SmolDocling."""
     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])
+        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)
 
     pattern = r"\[([\d\.\s,]+)\]"
-    normalized_text = re.sub(pattern, process_match, text)
-    return normalized_text
+    return re.sub(pattern, process_match, text)
 
-def downsample_video(video_path):
-    """Downsample a video to evenly spaced frames, returning PIL images with timestamps."""
+def downsample_video(video_path, num_frames=10):
+    """Downsample a video to evenly spaced frames, returning PIL images."""
+    if not video_path:
+        return []
     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, 10, dtype=int)
+    frame_indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
+
     for i in frame_indices:
         vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)
         success, image = vidcap.read()
         if success:
-            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            pil_image = Image.fromarray(image)
-            timestamp = round(i / fps, 2)
-            frames.append((pil_image, timestamp))
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            frames.append(Image.fromarray(image_rgb))
     vidcap.release()
     return frames
 
-@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."
+# --- 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.", ""
         return
 
-    if image is None:
-        yield "Please upload an image.", "Please upload an image."
+    try:
+        processor, model = MODEL_MAPPING[model_name]
+    except KeyError:
+        yield "Invalid model selected.", ""
         return
 
-    images = [image]
-
+    # Model-specific preprocessing
     if model_name == "SmolDocling-256M-preview":
-        if "OTSL" in text or "code" in text:
+        if any(keyword in text for keyword in ["OTSL", "code"]):
             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:
+        if any(keyword in text for keyword in ["OCR at text at", "Identify element", "formula"]):
             text = normalize_values(text, target_max=500)
 
     messages = [
         {
             "role": "user",
-            "content": [{"type": "image"} for _ in images] + [
-                {"type": "text", "text": text}
-            ]
+            "content": [{"type": "image"}] * len(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)
+    inputs = processor(text=prompt, images=images, return_tensors="pt").to(DEVICE)
+
+    streamer = TextIteratorStreamer(
+        processor, skip_prompt=True, skip_special_tokens=True
+    )
 
-    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
     generation_kwargs = {
         **inputs,
         "streamer": streamer,
@@ -193,6 +166,7 @@ def generate_image(model_name: str, text: str, image: Image.Image,
         "top_k": top_k,
         "repetition_penalty": repetition_penalty,
     }
+
     thread = Thread(target=model.generate, kwargs=generation_kwargs)
     thread.start()
 
@@ -201,102 +175,70 @@ def generate_image(model_name: str, text: str, image: Image.Image,
         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()
-        if any(tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]):
+        is_doc_tag = any(
+            tag in cleaned_output for tag in ["<doctag>", "<otsl>", "<code>", "<chart>", "<formula>"]
+        )
+        if is_doc_tag:
             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
+            
+            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}"
         else:
             yield buffer, cleaned_output
 
 @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 == "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."
+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.", ""
         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.", "Please upload a video."
+        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()
+    if not frames:
+        yield "Could not process video. Please check the file.", ""
+        return
+    yield from _generate_response(model_name, text, frames, *args)
 
-    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
+# --- 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%;
+}
+"""
 
-# Define examples for image and video inference
+# Define examples
 image_examples = [
     ["Reconstruct the doc [table] as it is.", "images/0.png"],
     ["Describe the image!", "images/8.png"],
@@ -306,92 +248,99 @@ 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 late.", "images/7.jpg"],
+    ["Convert formula to latex.", "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("# **[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():
-        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", 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=5)
-                
-                with gr.Accordion("(Result.md)", open=False):
-                    formatted_output = gr.Markdown(label="(Result.md)")
-            
+        # --- LEFT COLUMN (INPUTS) ---
+        with gr.Column(scale=1):
             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"
+                choices=[
+                    "Nanonets-OCR-s",
+                    "MonkeyOCR-Recognition",
+                    "Thyme-RL",
+                    "Typhoon-OCR-7B",
+                    "SmolDocling-256M-preview",
+                ],
+                label="🤖 Select Model",
+                value="Nanonets-OCR-s",
             )
+
+            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")
+                    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")
+                    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):
+                max_new_tokens = gr.Slider(
+                    label="Max New Tokens", min=1, max=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS
+                )
+                temperature = gr.Slider(
+                    label="Temperature", min=0.1, max=2.0, step=0.1, value=0.6
+                )
+                top_p = gr.Slider(
+                    label="Top-P", min=0.05, max=1.0, step=0.05, value=0.9
+                )
+                top_k = gr.Slider(label="Top-K", min=1, max=1000, step=1, value=50)
+                repetition_penalty = gr.Slider(
+                    label="Repetition Penalty", min=1.0, max=2.0, step=0.05, value=1.2
+                )
             
-            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.")  
+            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"):
+                gr.Markdown("## Output")
+                raw_output = gr.Textbox(
+                    label="Raw Output Stream", interactive=False, lines=8
+                )
+                formatted_output = gr.Markdown(label="Formatted Result (Markdown)")
             
+            with gr.Accordion("💻 Model Information", open=True):
+                gr.Markdown(
+                    """
+                    - **[Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s)**: Transforms documents into structured markdown with intelligent content recognition.
+                    - **[SmolDocling-256M](https://huggingface.co/ds4sd/SmolDocling-256M-preview)**: An efficient multimodal model for converting documents to structured formats.
+                    - **[MonkeyOCR-Recognition](https://huggingface.co/echo840/MonkeyOCR)**: Adopts a Structure-Recognition-Relation paradigm for efficient document processing.
+                    - **[Typhoon-OCR-7B](https://huggingface.co/scb10x/typhoon-ocr-7b)**: A bilingual (Thai/English) document parsing model for real-world documents.
+                    - **[Thyme-RL](https://huggingface.co/Kwai-Keye/Thyme-RL)**: Generates and executes code for image processing 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_image,
-        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
-        outputs=[raw_output, formatted_output]
+        fn=generate_for_image,
+        inputs=[model_choice, image_query, image_upload] + advanced_params,
+        outputs=[raw_output, formatted_output],
     )
+
     video_submit.click(
-        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]
+        fn=generate_for_video,
+        inputs=[model_choice, video_query, video_upload] + advanced_params,
+        outputs=[raw_output, formatted_output],
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=50).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)
+    demo.queue(max_size=50).launch(share=True, show_error=True)