Update app.py

app.py

@@ -1,420 +1,420 @@
-import os
-import random
-import uuid
-import json
-import time
-import asyncio
-from threading import Thread
-import base64
-from io import BytesIO
-import re
-
-import gradio as gr
-import spaces
-import torch
-import numpy as np
-from PIL import Image, ImageDraw
-import cv2
-
-from transformers import (
-    Qwen2VLForConditionalGeneration,
-    Qwen2_5_VLForConditionalGeneration,
-    AutoProcessor,
-    TextIteratorStreamer,
-)
-from qwen_vl_utils import process_vision_info
-
-# Constants for text generation
-MAX_MAX_NEW_TOKENS = 2048
-DEFAULT_MAX_NEW_TOKENS = 1024
-MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-# Load Camel-Doc-OCR-062825
-MODEL_ID_M = "prithivMLmods/Camel-Doc-OCR-062825"
-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 ViLaSR-7B
-MODEL_ID_X = "AntResearchNLP/ViLaSR"
-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()
-
-# Load OCRFlux-3B
-MODEL_ID_T = "ChatDOC/OCRFlux-3B"
-processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
-model_t = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_T,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-# Load ShotVL-7B
-MODEL_ID_S = "Vchitect/ShotVL-7B"
-processor_s = AutoProcessor.from_pretrained(MODEL_ID_S, trust_remote_code=True)
-model_s = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-    MODEL_ID_S,
-    trust_remote_code=True,
-    torch_dtype=torch.float16
-).to(device).eval()
-
-# Helper functions for object detection
-def image_to_base64(image):
-    """Convert a PIL image to a base64-encoded string."""
-    buffered = BytesIO()
-    image.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-    return img_str
-
-def draw_bounding_boxes(image, bounding_boxes, outline_color="red", line_width=2):
-    """Draw bounding boxes on an image."""
-    draw = ImageDraw.Draw(image)
-    for box in bounding_boxes:
-        xmin, ymin, xmax, ymax = box
-        draw.rectangle([xmin, ymin, xmax, ymax], outline=outline_color, width=line_width)
-    return image
-
-def rescale_bounding_boxes(bounding_boxes, original_width, original_height, scaled_width=1000, scaled_height=1000):
-    """Rescale bounding boxes from normalized (1000x1000) to original image dimensions."""
-    x_scale = original_width / scaled_width
-    y_scale = original_height / scaled_height
-    rescaled_boxes = []
-    for box in bounding_boxes:
-        xmin, ymin, xmax, ymax = box
-        rescaled_box = [
-            xmin * x_scale,
-            ymin * y_scale,
-            xmax * x_scale,
-            ymax * y_scale
-        ]
-        rescaled_boxes.append(rescaled_box)
-    return rescaled_boxes
-
-# Default system prompt for object detection
-default_system_prompt = (
-    "You are a helpful assistant to detect objects in images. When asked to detect elements based on a description, "
-    "you return bounding boxes for all elements in the form of [xmin, ymin, xmax, ymax] with the values being scaled "
-    "to 512 by 512 pixels. When there are more than one result, answer with a list of bounding boxes in the form "
-    "of [[xmin, ymin, xmax, ymax], [xmin, ymin, xmax, ymax], ...]."
-    "Parse only the boxes; don't write unnecessary content."
-)
-
-# Function for object detection
-@spaces.GPU
-def run_example(image, text_input, system_prompt):
-    """Detect objects in an image and return bounding box annotations."""
-    model = model_x  
-    processor = processor_x
-
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": f"data:image;base64,{image_to_base64(image)}"},
-                {"type": "text", "text": system_prompt},
-                {"type": "text", "text": text_input},
-            ],
-        }
-    ]
-
-    text = processor.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
-    )
-    image_inputs, video_inputs = process_vision_info(messages)
-    inputs = processor(
-        text=[text],
-        images=image_inputs,
-        videos=video_inputs,
-        padding=True,
-        return_tensors="pt",
-    )
-    inputs = inputs.to("cuda")
-
-    generated_ids = model.generate(**inputs, max_new_tokens=256)
-    generated_ids_trimmed = [
-        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-    ]
-    output_text = processor.batch_decode(
-        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-    )
-    pattern = r'\[\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\]'
-    matches = re.findall(pattern, str(output_text))
-    parsed_boxes = [[int(num) for num in match] for match in matches]
-    scaled_boxes = rescale_bounding_boxes(parsed_boxes, image.width, image.height)
-    annotated_image = draw_bounding_boxes(image.copy(), scaled_boxes)
-    return output_text[0], str(parsed_boxes), annotated_image
-
-def downsample_video(video_path):
-    """
-    Downsample a video to evenly spaced frames, returning each as a PIL image 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)
-    frames = []
-    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 = 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
-
-@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 using the selected model for image input.
-    """
-    if model_name == "Camel-Doc-OCR-062825":
-        processor = processor_m
-        model = model_m
-    elif model_name == "ViLaSR-7B":
-        processor = processor_x
-        model = model_x
-    elif model_name == "OCRFlux-3B":
-        processor = processor_t
-        model = model_t
-    elif model_name == "ShotVL-7B":
-        processor = processor_s
-        model = model_s
-    else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
-
-    if image is None:
-        yield "Please upload an image.", "Please upload an image."
-        return
-
-    messages = [{
-        "role": "user",
-        "content": [
-            {"type": "image", "image": image},
-            {"type": "text", "text": text},
-        ]
-    }]
-    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
-        time.sleep(0.01)
-        yield buffer, 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 using the selected model for video input.
-    """
-    if model_name == "Camel-Doc-OCR-062825":
-        processor = processor_m
-        model = model_m
-    elif model_name == "ViLaSR-7B":
-        processor = processor_x
-        model = model_x
-    elif model_name == "OCRFlux-3B":
-        processor = processor_t
-        model = model_t
-    elif model_name == "ShotVL-7B":
-        processor = processor_s
-        model = model_s
-    else:
-        yield "Invalid model selected.", "Invalid model selected."
-        return
-
-    if video_path is None:
-        yield "Please upload a video.", "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, buffer
-
-# Define examples for image, video, and object detection inference
-image_examples = [
-    ["convert this page to doc [text] precisely for markdown.", "images/1.png"],
-    ["convert this page to doc [table] precisely for markdown.", "images/2.png"],
-    ["explain the movie shot in detail.", "images/3.png"],   
-    ["fill the correct numbers.", "images/4.png"]
-]
-
-video_examples = [
-    ["explain the ad video in detail.", "videos/1.mp4"],
-    ["explain the video in detail.", "videos/2.mp4"]
-]
-
-object_detection_examples = [
-    ["object/1.png", "detect red and yellow cars."],
-    ["object/2.png", "detect the white cat."]
-]
-
-# Added CSS to style the output area as a "Canvas"
-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("# **[Doc VLMs v2 [Localization]](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
-    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")
-                    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")
-                    video_submit = gr.Button("Submit", elem_classes="submit-btn")
-                    gr.Examples(
-                        examples=video_examples,
-                        inputs=[video_query, video_upload]
-                    )
-                with gr.TabItem("Object Detection / Localization"):
-                    with gr.Row():
-                        with gr.Column():
-                            input_img = gr.Image(label="Input Image", type="pil")
-                            system_prompt = gr.Textbox(label="System Prompt", value=default_system_prompt, visible=False)
-                            text_input = gr.Textbox(label="Query Input")
-                            submit_btn = gr.Button(value="Submit", elem_classes="submit-btn")
-                        with gr.Column():
-                            model_output_text = gr.Textbox(label="Model Output Text")
-                            parsed_boxes = gr.Textbox(label="Parsed Boxes")
-                            annotated_image = gr.Image(label="Annotated Image")
-
-                    gr.Examples(
-                        examples=object_detection_examples,
-                        inputs=[input_img, text_input],
-                        outputs=[model_output_text, parsed_boxes, annotated_image],
-                        fn=run_example,
-                        cache_examples=True,
-                    )
-
-                    submit_btn.click(
-                        fn=run_example,
-                        inputs=[input_img, text_input, system_prompt],
-                        outputs=[model_output_text, parsed_boxes, annotated_image]
-                    )
-
-            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("## Result.Md")
-                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2)
-                markdown_output = gr.Markdown(label="Formatted Result (Result.Md)")
-
-            model_choice = gr.Radio(
-                choices=["Camel-Doc-OCR-062825", "OCRFlux-3B", "ShotVL-7B", "ViLaSR-7B"],
-                label="Select Model",
-                value="Camel-Doc-OCR-062825"
-            )
-
-            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Doc-VLMs-v2-Localization/discussions)")
-            gr.Markdown("> [Camel-Doc-OCR-062825](https://huggingface.co/prithivMLmods/Camel-Doc-OCR-062825) : camel-doc-ocr-062825 model is a fine-tuned version of qwen2.5-vl-7b-instruct, optimized for document retrieval, content extraction, and analysis recognition. built on top of the qwen2.5-vl architecture, this model enhances document comprehension capabilities.")
-            gr.Markdown("> [OCRFlux-3B](https://huggingface.co/ChatDOC/OCRFlux-3B) : ocrflux-3b model that's fine-tuned from qwen2.5-vl-3b-instruct using our private document datasets and some data from olmocr-mix-0225 dataset. optimized for document retrieval, content extraction, and analysis recognition. the best way to use this model is via the ocrflux toolkit.")
-            gr.Markdown("> [ViLaSR](https://huggingface.co/AntResearchNLP/ViLaSR) : vilasr-7b model as presented in reinforcing spatial reasoning in vision-language models with interwoven thinking and visual drawing. efficient reasoning capabilities.")
-            gr.Markdown("> [ShotVL-7B](https://huggingface.co/Vchitect/ShotVL-7B) : shotvl-7b is a fine-tuned version of qwen2.5-vl-7b-instruct, trained by supervised fine-tuning on the largest and high-quality dataset for cinematic language understanding to date. it currently achieves state-of-the-art performance on shotbench.")
-            gr.Markdown(">⚠️note: all the models in space are not guaranteed to perform well in video inference use cases.")  
-            
-    image_submit.click(
-        fn=generate_image,
-        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
-        outputs=[output, markdown_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=[output, markdown_output]
-    )
-
-if __name__ == "__main__":
+import os
+import random
+import uuid
+import json
+import time
+import asyncio
+from threading import Thread
+import base64
+from io import BytesIO
+import re
+
+import gradio as gr
+import spaces
+import torch
+import numpy as np
+from PIL import Image, ImageDraw
+import cv2
+
+from transformers import (
+    Qwen2VLForConditionalGeneration,
+    Qwen2_5_VLForConditionalGeneration,
+    AutoProcessor,
+    TextIteratorStreamer,
+)
+from qwen_vl_utils import process_vision_info
+
+# Constants for text generation
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 1024
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# Load Camel-Doc-OCR-062825
+MODEL_ID_M = "prithivMLmods/Camel-Doc-OCR-062825"
+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 ViLaSR-7B
+MODEL_ID_X = "AntResearchNLP/ViLaSR"
+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()
+
+# Load OCRFlux-3B
+MODEL_ID_T = "ChatDOC/OCRFlux-3B"
+processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True)
+model_t = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_T,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+# Load ShotVL-7B
+MODEL_ID_S = "Vchitect/ShotVL-7B"
+processor_s = AutoProcessor.from_pretrained(MODEL_ID_S, trust_remote_code=True)
+model_s = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    MODEL_ID_S,
+    trust_remote_code=True,
+    torch_dtype=torch.float16
+).to(device).eval()
+
+# Helper functions for object detection
+def image_to_base64(image):
+    """Convert a PIL image to a base64-encoded string."""
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+    return img_str
+
+def draw_bounding_boxes(image, bounding_boxes, outline_color="red", line_width=2):
+    """Draw bounding boxes on an image."""
+    draw = ImageDraw.Draw(image)
+    for box in bounding_boxes:
+        xmin, ymin, xmax, ymax = box
+        draw.rectangle([xmin, ymin, xmax, ymax], outline=outline_color, width=line_width)
+    return image
+
+def rescale_bounding_boxes(bounding_boxes, original_width, original_height, scaled_width=1000, scaled_height=1000):
+    """Rescale bounding boxes from normalized (1000x1000) to original image dimensions."""
+    x_scale = original_width / scaled_width
+    y_scale = original_height / scaled_height
+    rescaled_boxes = []
+    for box in bounding_boxes:
+        xmin, ymin, xmax, ymax = box
+        rescaled_box = [
+            xmin * x_scale,
+            ymin * y_scale,
+            xmax * x_scale,
+            ymax * y_scale
+        ]
+        rescaled_boxes.append(rescaled_box)
+    return rescaled_boxes
+
+# Default system prompt for object detection
+default_system_prompt = (
+    "You are a helpful assistant to detect objects in images. When asked to detect elements based on a description, "
+    "you return bounding boxes for all elements in the form of [xmin, ymin, xmax, ymax] with the values being scaled "
+    "to 512 by 512 pixels. When there are more than one result, answer with a list of bounding boxes in the form "
+    "of [[xmin, ymin, xmax, ymax], [xmin, ymin, xmax, ymax], ...]."
+    "Parse only the boxes; don't write unnecessary content. Follow this command strictly at all times."
+)
+
+# Function for object detection
+@spaces.GPU
+def run_example(image, text_input, system_prompt):
+    """Detect objects in an image and return bounding box annotations."""
+    model = model_x  
+    processor = processor_x
+
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": f"data:image;base64,{image_to_base64(image)}"},
+                {"type": "text", "text": system_prompt},
+                {"type": "text", "text": text_input},
+            ],
+        }
+    ]
+
+    text = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    image_inputs, video_inputs = process_vision_info(messages)
+    inputs = processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+    )
+    inputs = inputs.to("cuda")
+
+    generated_ids = model.generate(**inputs, max_new_tokens=256)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    pattern = r'\[\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\]'
+    matches = re.findall(pattern, str(output_text))
+    parsed_boxes = [[int(num) for num in match] for match in matches]
+    scaled_boxes = rescale_bounding_boxes(parsed_boxes, image.width, image.height)
+    annotated_image = draw_bounding_boxes(image.copy(), scaled_boxes)
+    return output_text[0], str(parsed_boxes), annotated_image
+
+def downsample_video(video_path):
+    """
+    Downsample a video to evenly spaced frames, returning each as a PIL image 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)
+    frames = []
+    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 = 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
+
+@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 using the selected model for image input.
+    """
+    if model_name == "Camel-Doc-OCR-062825":
+        processor = processor_m
+        model = model_m
+    elif model_name == "ViLaSR-7B":
+        processor = processor_x
+        model = model_x
+    elif model_name == "OCRFlux-3B":
+        processor = processor_t
+        model = model_t
+    elif model_name == "ShotVL-7B":
+        processor = processor_s
+        model = model_s
+    else:
+        yield "Invalid model selected.", "Invalid model selected."
+        return
+
+    if image is None:
+        yield "Please upload an image.", "Please upload an image."
+        return
+
+    messages = [{
+        "role": "user",
+        "content": [
+            {"type": "image", "image": image},
+            {"type": "text", "text": text},
+        ]
+    }]
+    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
+        time.sleep(0.01)
+        yield buffer, 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 using the selected model for video input.
+    """
+    if model_name == "Camel-Doc-OCR-062825":
+        processor = processor_m
+        model = model_m
+    elif model_name == "ViLaSR-7B":
+        processor = processor_x
+        model = model_x
+    elif model_name == "OCRFlux-3B":
+        processor = processor_t
+        model = model_t
+    elif model_name == "ShotVL-7B":
+        processor = processor_s
+        model = model_s
+    else:
+        yield "Invalid model selected.", "Invalid model selected."
+        return
+
+    if video_path is None:
+        yield "Please upload a video.", "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, buffer
+
+# Define examples for image, video, and object detection inference
+image_examples = [
+    ["convert this page to doc [text] precisely for markdown.", "images/1.png"],
+    ["convert this page to doc [table] precisely for markdown.", "images/2.png"],
+    ["explain the movie shot in detail.", "images/3.png"],   
+    ["fill the correct numbers.", "images/4.png"]
+]
+
+video_examples = [
+    ["explain the ad video in detail.", "videos/1.mp4"],
+    ["explain the video in detail.", "videos/2.mp4"]
+]
+
+object_detection_examples = [
+    ["object/1.png", "detect red and yellow cars."],
+    ["object/2.png", "detect the white cat."]
+]
+
+# Added CSS to style the output area as a "Canvas"
+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("# **[Doc VLMs v2 [Localization]](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**")
+    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")
+                    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")
+                    video_submit = gr.Button("Submit", elem_classes="submit-btn")
+                    gr.Examples(
+                        examples=video_examples,
+                        inputs=[video_query, video_upload]
+                    )
+                with gr.TabItem("Object Detection / Localization"):
+                    with gr.Row():
+                        with gr.Column():
+                            input_img = gr.Image(label="Input Image", type="pil")
+                            system_prompt = gr.Textbox(label="System Prompt", value=default_system_prompt, visible=False)
+                            text_input = gr.Textbox(label="Query Input")
+                            submit_btn = gr.Button(value="Submit", elem_classes="submit-btn")
+                        with gr.Column():
+                            model_output_text = gr.Textbox(label="Model Output Text")
+                            parsed_boxes = gr.Textbox(label="Parsed Boxes")
+                            annotated_image = gr.Image(label="Annotated Image")
+
+                    gr.Examples(
+                        examples=object_detection_examples,
+                        inputs=[input_img, text_input],
+                        outputs=[model_output_text, parsed_boxes, annotated_image],
+                        fn=run_example,
+                        cache_examples=True,
+                    )
+
+                    submit_btn.click(
+                        fn=run_example,
+                        inputs=[input_img, text_input, system_prompt],
+                        outputs=[model_output_text, parsed_boxes, annotated_image]
+                    )
+
+            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("## Result.Md")
+                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2)
+                markdown_output = gr.Markdown(label="Formatted Result (Result.Md)")
+
+            model_choice = gr.Radio(
+                choices=["Camel-Doc-OCR-062825", "OCRFlux-3B", "ShotVL-7B", "ViLaSR-7B"],
+                label="Select Model",
+                value="Camel-Doc-OCR-062825"
+            )
+
+            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Doc-VLMs-v2-Localization/discussions)")
+            gr.Markdown("> [Camel-Doc-OCR-062825](https://huggingface.co/prithivMLmods/Camel-Doc-OCR-062825) : camel-doc-ocr-062825 model is a fine-tuned version of qwen2.5-vl-7b-instruct, optimized for document retrieval, content extraction, and analysis recognition. built on top of the qwen2.5-vl architecture, this model enhances document comprehension capabilities.")
+            gr.Markdown("> [OCRFlux-3B](https://huggingface.co/ChatDOC/OCRFlux-3B) : ocrflux-3b model that's fine-tuned from qwen2.5-vl-3b-instruct using our private document datasets and some data from olmocr-mix-0225 dataset. optimized for document retrieval, content extraction, and analysis recognition. the best way to use this model is via the ocrflux toolkit.")
+            gr.Markdown("> [ViLaSR](https://huggingface.co/AntResearchNLP/ViLaSR) : vilasr-7b model as presented in reinforcing spatial reasoning in vision-language models with interwoven thinking and visual drawing. efficient reasoning capabilities.")
+            gr.Markdown("> [ShotVL-7B](https://huggingface.co/Vchitect/ShotVL-7B) : shotvl-7b is a fine-tuned version of qwen2.5-vl-7b-instruct, trained by supervised fine-tuning on the largest and high-quality dataset for cinematic language understanding to date. it currently achieves state-of-the-art performance on shotbench.")
+            gr.Markdown(">⚠️note: all the models in space are not guaranteed to perform well in video inference use cases.")  
+            
+    image_submit.click(
+        fn=generate_image,
+        inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
+        outputs=[output, markdown_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=[output, markdown_output]
+    )
+
+if __name__ == "__main__":
     demo.queue(max_size=30).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)