app.py

import os
import random
import uuid
import json
import time
import asyncio
from threading import Thread

import gradio as gr
import spaces
import torch
import numpy as np
from PIL import Image
import cv2
import requests

from transformers import (
    Qwen2VLForConditionalGeneration,
    Qwen2_5_VLForConditionalGeneration,
    AutoModelForImageTextToText,
    AutoProcessor,
    TextIteratorStreamer,
)
from transformers.image_utils import load_image

# 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 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()

# Load typhoon-ocr-3b
MODEL_ID_T = "scb10x/typhoon-ocr-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 olmOCR-7B-0225-preview
MODEL_ID_O = "allenai/olmOCR-7B-0225-preview"
processor_o = AutoProcessor.from_pretrained(MODEL_ID_O, trust_remote_code=True)
model_o = Qwen2VLForConditionalGeneration.from_pretrained(
    MODEL_ID_O,
    trust_remote_code=True,
    torch_dtype=torch.float16
).to(device).eval()

# Load Lumian-VLR-7B-Thinking
MODEL_ID_J = "prithivMLmods/Lumian-VLR-7B-Thinking"
SUBFOLDER = "think-preview"
processor_j = AutoProcessor.from_pretrained(MODEL_ID_J, trust_remote_code=True, subfolder=SUBFOLDER)
model_j = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_J,
    trust_remote_code=True,
    subfolder=SUBFOLDER,
    torch_dtype=torch.float16
).to(device).eval()

# Load LMM-R1-MGT-PerceReason
MODEL_ID_F = "VLM-Reasoner/LMM-R1-MGT-PerceReason"
processor_f = AutoProcessor.from_pretrained(MODEL_ID_F, trust_remote_code=True)
model_f = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    MODEL_ID_F,
    trust_remote_code=True,
    torch_dtype=torch.float16
).to(device).eval()

def downsample_video(video_path):
    """
    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)
    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):
    """
    Generates responses using the selected model for image input.
    """
    if model_name == "DREX-062225-7B-exp":
        processor = processor_x
        model = model_x
    elif model_name == "olmOCR-7B-0225-preview":
        processor = processor_o
        model = model_o
    elif model_name == "Typhoon-OCR-3B":
        processor = processor_t
        model = model_t
    elif model_name == "Lumian-VLR-7B-Thinking":
        processor = processor_j
        model = model_j
    elif model_name == "LMM-R1-MGT-PerceReason":
        processor = processor_f
        model = model_f
    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):
    """
    Generates responses using the selected model for video input.
    """
    if model_name == "DREX-062225-7B-exp":
        processor = processor_x
        model = model_x
    elif model_name == "olmOCR-7B-0225-preview":
        processor = processor_o
        model = model_o
    elif model_name == "Typhoon-OCR-3B":
        processor = processor_t
        model = model_t
    elif model_name == "Lumian-VLR-7B-Thinking":
        processor = processor_j
        model = model_j
    elif model_name == "LMM-R1-MGT-PerceReason":
        processor = processor_f
        model = model_f
    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

def save_to_md(output_text):
    """
    Saves the output text to a Markdown file and returns the file path for download.
    """
    file_path = f"result_{uuid.uuid4()}.md"
    with open(file_path, "w") as f:
        f.write(output_text)
    return file_path

# Define examples for image and video inference
image_examples = [
    ["Describe the safety measures in the image. Conclude (Safe / Unsafe)..", "images/5.jpg"],
    ["Convert this page to doc [text] precisely.", "images/3.png"],
    ["Convert this page to doc [text] precisely.", "images/4.png"],
    ["Explain the creativity in the image.", "images/6.jpg"],
    ["Convert this page to doc [text] precisely.", "images/1.png"],
    ["Convert chart to OTSL.", "images/2.png"]
]

video_examples = [
    ["Explain the video in detail.", "videos/2.mp4"],
    ["Explain the ad in detail.", "videos/1.mp4"]
]

# 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("# **[Multimodal VLM Thinking](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.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")
                output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2, show_copy_button=True)
                with gr.Accordion("(Result.md)", open=False):                
                    markdown_output = gr.Markdown(label="(Result.Md)")
            model_choice = gr.Radio(
                choices=["Lumian-VLR-7B-Thinking", "DREX-062225-7B-exp", "olmOCR-7B-0225-preview", "LMM-R1-MGT-PerceReason", "Typhoon-OCR-3B"],
                label="Select Model",
                value="Lumian-VLR-7B-Thinking"
            )
            gr.Markdown("**Model Info 💻** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-VLM-Thinking/discussions)")          
            gr.Markdown("> Lumian-VLR-7B-Thinking is a high-fidelity vision-language reasoning model built on Qwen2.5-VL-7B-Instruct, designed for fine-grained multimodal understanding, enhancing image captioning, video reasoning, and document comprehension through explicit grounded reasoning. It is trained first via supervised fine-tuning (SFT) on visually-grounded reasoning traces and then further refined using GRPO reinforcement learning to boost reasoning accuracy.")
            gr.Markdown("> LMM-R1-MGT-PerceReason is a vision-language model focused on advanced reasoning using a multimodal tree search approach enabling progressive visual-textual slow thinking, improving complex spatial and logical reasoning without fine-tuning. OLMOCR-7B-0225-preview is a 7B parameter open large model designed for OCR tasks with robust text extraction, especially in complex document layouts. ")
            gr.Markdown("> Typhoon-ocr-3b is a 3B parameter OCR model optimized for efficient and accurate optical character recognition in challenging conditions. DREX-062225-exp is an experimental multimodal model emphasizing strong document reading and extraction capabilities combined with vision-language understanding to support detailed document parsing and reasoning tasks.")
            gr.Markdown("> ⚠️ Note: Models in this space may not perform well on video inference tasks.")
            
    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)