app.py

import os
import random
import uuid
import json
import time
import asyncio
from threading import Thread
from pathlib import Path
from io import BytesIO
from typing import Optional, Tuple, Dict, Any, Iterable

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

from transformers import (
    Qwen3VLMoeForConditionalGeneration,
    AutoProcessor,
    TextIteratorStreamer,
)
from transformers.image_utils import load_image

# --- Theme Definition ---
from gradio.themes import Soft
from gradio.themes.utils import colors, fonts, sizes

colors.teal_gray = colors.Color(
    name="teal_gray",
    c50="#e8f1f4", c100="#cddde3", c200="#a8c3cf", c300="#7da6b8",
    c400="#588aa2", c500="#3d6e87", c600="#335b70", c700="#2b495a",
    c800="#2c5364", c900="#233f4b", c950="#1b323c",
)

colors.red_gray = colors.Color(
    name="red_gray",
    c50="#f7eded", c100="#f5dcdc", c200="#efb4b4", c300="#e78f8f",
    c400="#d96a6a", c500="#c65353", c600="#b24444", c700="#8f3434",
    c800="#732d2d", c900="#5f2626", c950="#4d2020",
)

class Teals(Soft):
    def __init__(
        self,
        *,
        primary_hue: colors.Color | str = colors.gray,
        secondary_hue: colors.Color | str = colors.teal_gray,
        neutral_hue: colors.Color | str = colors.slate,
        text_size: sizes.Size | str = sizes.text_md,
        font: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("Inconsolata"), "Arial", "sans-serif",
        ),
        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
        ),
    ):
        super().__init__(
            primary_hue=primary_hue,
            secondary_hue=secondary_hue,
            neutral_hue=neutral_hue,
            text_size=text_size,
            font=font,
            font_mono=font_mono,
        )
        super().set(
            background_fill_primary="*primary_50",
            background_fill_primary_dark="*primary_900",
            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
            button_primary_text_color="white",
            button_primary_text_color_hover="black",
            button_primary_background_fill="linear-gradient(90deg, *secondary_400, *secondary_400)",
            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_300, *secondary_300)",
            button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_800)",
            button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_500)",
            button_secondary_text_color="black",
            button_secondary_text_color_hover="white",
            button_secondary_background_fill="linear-gradient(90deg, *primary_300, *primary_300)",
            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
            button_cancel_background_fill=f"linear-gradient(90deg, {colors.red_gray.c400}, {colors.red_gray.c500})",
            button_cancel_background_fill_dark=f"linear-gradient(90deg, {colors.red_gray.c700}, {colors.red_gray.c800})",
            button_cancel_background_fill_hover=f"linear-gradient(90deg, {colors.red_gray.c500}, {colors.red_gray.c600})",
            button_cancel_background_fill_hover_dark=f"linear-gradient(90deg, {colors.red_gray.c800}, {colors.red_gray.c900})",
            button_cancel_text_color="white",
            button_cancel_text_color_dark="white",
            button_cancel_text_color_hover="white",
            button_cancel_text_color_hover_dark="white",
            slider_color="*secondary_300",
            slider_color_dark="*secondary_600",
            block_title_text_weight="600",
            block_border_width="3px",
            block_shadow="*shadow_drop_lg",
            button_primary_shadow="*shadow_drop_lg",
            button_large_padding="11px",
            color_accent_soft="*primary_100",
            block_label_background_fill="*primary_200",
        )

teals = Teals()

# --- Custom CSS ---
css = """
:root {
    --color-grey-50: #f9fafb;
    --banner-background: var(--secondary-400);
    --banner-text-color: var(--primary-100);
    --banner-background-dark: var(--secondary-800);
    --banner-text-color-dark: var(--primary-100);
    --banner-chrome-height: calc(16px + 43px);
    --chat-chrome-height-wide-no-banner: 320px;
    --chat-chrome-height-narrow-no-banner: 450px;
    --chat-chrome-height-wide: calc(var(--chat-chrome-height-wide-no-banner) + var(--banner-chrome-height));
    --chat-chrome-height-narrow: calc(var(--chat-chrome-height-narrow-no-banner) + var(--banner-chrome-height));
}
.banner-message { background-color: var(--banner-background); padding: 5px; margin: 0; border-radius: 5px; border: none; }
.banner-message-text { font-size: 13px; font-weight: bolder; color: var(--banner-text-color) !important; }
body.dark .banner-message { background-color: var(--banner-background-dark) !important; }
body.dark .gradio-container .contain .banner-message .banner-message-text { color: var(--banner-text-color-dark) !important; }
.toast-body { background-color: var(--color-grey-50); }
.html-container:has(.css-styles) { padding: 0; margin: 0; }
.css-styles { height: 0; }
.model-message { text-align: end; }
.model-dropdown-container { display: flex; align-items: center; gap: 10px; padding: 0; }
.user-input-container .multimodal-textbox{ border: none !important; }
.control-button { height: 51px; }
button.cancel { border: var(--button-border-width) solid var(--button-cancel-border-color); background: var(--button-cancel-background-fill); color: var(--button-cancel-text-color); box-shadow: var(--button-cancel-shadow); }
button.cancel:hover, .cancel[disabled] { background: var(--button-cancel-background-fill-hover); color: var(--button-cancel-text-color-hover); }
.opt-out-message { top: 8px; }
.opt-out-message .html-container, .opt-out-checkbox label { font-size: 14px !important; padding: 0 !important; margin: 0 !important; color: var(--neutral-400) !important; }
div.block.chatbot { height: calc(100svh - var(--chat-chrome-height-wide)) !important; max-height: 900px !important; }
div.no-padding { padding: 0 !important; }
@media (max-width: 1280px) { div.block.chatbot { height: calc(100svh - var(--chat-chrome-height-wide)) !important; } }
@media (max-width: 1024px) {
    .responsive-row { flex-direction: column; }
    .model-message { text-align: start; font-size: 10px !important; }
    .model-dropdown-container { flex-direction: column; align-items: flex-start; }
    div.block.chatbot { height: calc(100svh - var(--chat-chrome-height-narrow)) !important; }
}
@media (max-width: 400px) {
    .responsive-row { flex-direction: column; }
    .model-message { text-align: start; font-size: 10px !important; }
    .model-dropdown-container { flex-direction: column; align-items: flex-start; }
    div.block.chatbot { max-height: 360px !important; }
}
@media (max-height: 932px) { .chatbot { max-height: 500px !important; } }
@media (max-height: 1280px) { div.block.chatbot { max-height: 800px !important; } }
"""

# --- App Constants & Setup ---
MAX_MAX_NEW_TOKENS = 4096
DEFAULT_MAX_NEW_TOKENS = 2048
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

print("CUDA_VISIBLE_DEVICES=", os.environ.get("CUDA_VISIBLE_DEVICES"))
print("torch.__version__ =", torch.__version__)
print("torch.version.cuda =", torch.version.cuda)
print("cuda available:", torch.cuda.is_available())
print("cuda device count:", torch.cuda.device_count())
if torch.cuda.is_available():
    print("current device:", torch.cuda.current_device())
    print("device name:", torch.cuda.get_device_name(torch.cuda.current_device()))
print("Using device:", device)

# --- Model Loading ---
MODEL_ID_Q3VL = "Qwen/Qwen3-VL-30B-A3B-Instruct"
processor_q3vl = AutoProcessor.from_pretrained(MODEL_ID_Q3VL, trust_remote_code=True, use_fast=False)
model_q3vl = Qwen3VLMoeForConditionalGeneration.from_pretrained(
    MODEL_ID_Q3VL,
    trust_remote_code=True,
    dtype=torch.float16
).to(device).eval()

# --- Backend Functions ---
def downsample_video(video_path):
    vidcap = cv2.VideoCapture(video_path)
    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
    frames = []
    frame_indices = np.linspace(0, total_frames - 1, min(total_frames, 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)
            frames.append(pil_image)
    vidcap.release()
    return frames

def convert_pdf_to_images(file_path: str, dpi: int = 200):
    if not file_path:
        return []
    images = []
    pdf_document = fitz.open(file_path)
    zoom = dpi / 72.0
    mat = fitz.Matrix(zoom, zoom)
    for page_num in range(len(pdf_document)):
        page = pdf_document.load_page(page_num)
        pix = page.get_pixmap(matrix=mat)
        img_data = pix.tobytes("png")
        images.append(Image.open(BytesIO(img_data)))
    pdf_document.close()
    return images

def get_initial_pdf_state() -> Dict[str, Any]:
    return {"pages": [], "total_pages": 0, "current_page_index": 0}

def load_and_preview_pdf(file_path: Optional[str]) -> Tuple[Optional[Image.Image], Dict[str, Any], str]:
    state = get_initial_pdf_state()
    if not file_path:
        return None, state, '<div style="text-align:center;">No file loaded</div>'
    try:
        pages = convert_pdf_to_images(file_path)
        if not pages:
            return None, state, '<div style="text-align:center;">Could not load file</div>'
        state["pages"] = pages
        state["total_pages"] = len(pages)
        page_info_html = f'<div style="text-align:center;">Page 1 / {state["total_pages"]}</div>'
        return pages[0], state, page_info_html
    except Exception as e:
        return None, state, f'<div style="text-align:center;">Failed to load preview: {e}</div>'

def navigate_pdf_page(direction: str, state: Dict[str, Any]):
    if not state or not state["pages"]:
        return None, state, '<div style="text-align:center;">No file loaded</div>'
    current_index = state["current_page_index"]
    total_pages = state["total_pages"]
    if direction == "prev":
        new_index = max(0, current_index - 1)
    elif direction == "next":
        new_index = min(total_pages - 1, current_index + 1)
    else:
        new_index = current_index
    state["current_page_index"] = new_index
    image_preview = state["pages"][new_index]
    page_info_html = f'<div style="text-align:center;">Page {new_index + 1} / {total_pages}</div>'
    return image_preview, state, page_info_html

@spaces.GPU
def generate_image(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):
    if image is None:
        yield "Please upload an image.", "Please upload an image."
        return
    messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": text}]}]
    prompt_full = processor_q3vl.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = processor_q3vl(text=[prompt_full], images=[image], return_tensors="pt", padding=True).to(device)
    streamer = TextIteratorStreamer(processor_q3vl, skip_prompt=True, skip_special_tokens=True)
    generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
    thread = Thread(target=model_q3vl.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(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):
    if video_path is None:
        yield "Please upload a video.", "Please upload a video."
        return
    frames = downsample_video(video_path)
    if not frames:
        yield "Could not process video.", "Could not process video."
        return
    messages = [{"role": "user", "content": [{"type": "text", "text": text}]}]
    for frame in frames:
        messages[0]["content"].insert(0, {"type": "image"})
    prompt_full = processor_q3vl.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = processor_q3vl(text=[prompt_full], images=frames, return_tensors="pt", padding=True).to(device)
    streamer = TextIteratorStreamer(processor_q3vl, 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_q3vl.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

@spaces.GPU
def generate_pdf(text: str, state: Dict[str, Any], max_new_tokens: int = 2048, temperature: float = 0.6, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.2):
    if not state or not state["pages"]:
        yield "Please upload a PDF file first.", "Please upload a PDF file first."
        return
    page_images = state["pages"]
    full_response = ""
    for i, image in enumerate(page_images):
        page_header = f"--- Page {i+1}/{len(page_images)} ---\n"
        yield full_response + page_header, full_response + page_header
        messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": text}]}]
        prompt_full = processor_q3vl.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        inputs = processor_q3vl(text=[prompt_full], images=[image], return_tensors="pt", padding=True).to(device)
        streamer = TextIteratorStreamer(processor_q3vl, skip_prompt=True, skip_special_tokens=True)
        generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens}
        thread = Thread(target=model_q3vl.generate, kwargs=generation_kwargs)
        thread.start()
        page_buffer = ""
        for new_text in streamer:
            page_buffer += new_text
            yield full_response + page_header + page_buffer, full_response + page_header + page_buffer
            time.sleep(0.01)
        full_response += page_header + page_buffer + "\n\n"

# --- Gradio Interface ---
image_examples = [["Describe the safety measures in the image. Conclude (Safe / Unsafe)..", "images/5.jpg"], ["Convert this page to doc [markdown] precisely.", "images/3.png"]]
video_examples = [["Explain the video in detail.", "videos/2.mp4"]]
pdf_examples = [["examples/sample-doc.pdf"]]

with gr.Blocks(theme=teals, css=css) as demo:
    pdf_state = gr.State(value=get_initial_pdf_state())
    gr.Markdown("# **Qwen3-VL-Hf-Demo**")
    with gr.Row():
        with gr.Column(scale=2):
            with gr.Tabs():
                with gr.TabItem("Image Inference"):
                    image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...")
                    image_upload = gr.Gallery(type="pil", label="Image", height=290)
                    image_submit = gr.Button("Submit", variant="primary")
                    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", variant="primary")
                    gr.Examples(examples=video_examples, inputs=[video_query, video_upload])

                with gr.TabItem("PDF Inference"):
                    with gr.Row():
                        with gr.Column(scale=1):
                            pdf_query = gr.Textbox(label="Query Input", placeholder="e.g., 'Summarize this document'")
                            pdf_upload = gr.File(label="Upload PDF", file_types=[".pdf"])
                            pdf_submit = gr.Button("Submit", variant="primary")
                        with gr.Column(scale=1):
                            pdf_preview_img = gr.Image(label="PDF Preview", height=290)
                            with gr.Row():
                                prev_page_btn = gr.Button("◀ Previous")
                                page_info = gr.HTML('<div style="text-align:center;">No file loaded</div>')
                                next_page_btn = gr.Button("Next ▶")

            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(scale=3):
            gr.Markdown("## Output")
            output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=14, show_copy_button=True)
            with gr.Accordion("(Result.md)", open=False):
                markdown_output = gr.Markdown(label="(Result.Md)")
                
    # Event handlers
    image_submit.click(
        fn=generate_image,
        inputs=[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=[video_query, video_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
        outputs=[output, markdown_output]
    )
    pdf_submit.click(
        fn=generate_pdf,
        inputs=[pdf_query, pdf_state, max_new_tokens, temperature, top_p, top_k, repetition_penalty],
        outputs=[output, markdown_output]
    )
    pdf_upload.change(
        fn=load_and_preview_pdf,
        inputs=[pdf_upload],
        outputs=[pdf_preview_img, pdf_state, page_info]
    )
    prev_page_btn.click(
        fn=lambda s: navigate_pdf_page("prev", s),
        inputs=[pdf_state],
        outputs=[pdf_preview_img, pdf_state, page_info]
    )
    next_page_btn.click(
        fn=lambda s: navigate_pdf_page("next", s),
        inputs=[pdf_state],
        outputs=[pdf_preview_img, pdf_state, page_info]
    )
if __name__ == "__main__":
    demo.queue(max_size=50).launch(mcp_server=True, ssr_mode=False, show_error=True)