app.py

import streamlit as st
import tempfile
import os
import time
import re
import numpy as np
import torch
from PIL import Image
from decord import VideoReader, cpu
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer, TextIteratorStreamer
from threading import Thread

# Set page configuration
st.set_page_config(page_title="Omni DeepSeek Video Analysis", layout="wide")

# Constants
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

# Add CSS for text wrapping and vertical scrollbar for the expander
st.markdown("""
<style>
.output-text {
    white-space: pre-wrap !important;
    word-wrap: break-word !important;
}
.streamlit-expanderContent {
    white-space: pre-wrap !important;
    word-wrap: break-word !important;
    max-height: 100px;  /* 根据需要调整高度 */
    overflow-y: auto;   /* 添加垂直滚动条 */
}
</style>
""", unsafe_allow_html=True)

# Model loading utilities
@st.cache_resource
def load_model_and_tokenizer():
    """Load and cache the model and tokenizer"""
    path = 'AlphaTok/omni-deepseek-v0'
    
    with st.spinner("Loading model (this may take a minute)..."):
        model = AutoModel.from_pretrained(
            path,
            torch_dtype=torch.bfloat16,
            low_cpu_mem_usage=True,
            use_flash_attn=True,
            trust_remote_code=True
        ).eval()
        
        # Move to GPU if available
        if torch.cuda.is_available():
            model = model.cuda()
            st.success("Model loaded on GPU")
        else:
            st.warning("GPU not available, running on CPU (inference will be slow)")
            
        tokenizer = AutoTokenizer.from_pretrained(
            path, 
            trust_remote_code=True, 
            use_fast=False
        )
        
    return model, tokenizer

# Video processing functions
def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform

def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # Calculate the target aspect ratio
    def find_closest_aspect_ratio(aspect_ratio, target_ratios):
        best_ratio_diff = float('inf')
        best_ratio = (1, 1)
        for ratio in target_ratios:
            target_aspect_ratio = ratio[0] / ratio[1]
            ratio_diff = abs(aspect_ratio - target_aspect_ratio)
            if ratio_diff < best_ratio_diff:
                best_ratio_diff = ratio_diff
                best_ratio = ratio
        return best_ratio

    target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio, target_ratios)
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images

def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
    if bound:
        start, end = bound[0], bound[1]
    else:
        start, end = -100000, 100000
    start_idx = max(first_idx, round(start * fps))
    end_idx = min(round(end * fps), max_frame)
    seg_size = float(end_idx - start_idx) / num_segments
    frame_indices = np.array([
        int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
        for idx in range(num_segments)
    ])
    return frame_indices

def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
    max_frame = len(vr) - 1
    fps = float(vr.get_avg_fps())

    pixel_values_list, num_patches_list = [], []
    transform = build_transform(input_size=input_size)
    frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
    for frame_index in frame_indices:
        img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
        img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
        pixel_values = [transform(tile) for tile in img]
        pixel_values = torch.stack(pixel_values)
        num_patches_list.append(pixel_values.shape[0])
        pixel_values_list.append(pixel_values)
    pixel_values = torch.cat(pixel_values_list)
    return pixel_values, num_patches_list

# Save uploaded file to a temporary location
def save_uploaded_file(uploaded_file):
    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp:
        tmp.write(uploaded_file.getvalue())
        return tmp.name

def process_video_and_run_inference(video_path, prompt, model, tokenizer):
    # 加载并预处理视频
    with st.spinner("Processing video..."):
        pixel_values, num_patches_list = load_video(
            video_path, 
            num_segments=16, 
            max_num=1
        )
        if torch.cuda.is_available():
            pixel_values = pixel_values.to(torch.bfloat16).cuda()
        else:
            pixel_values = pixel_values.to(torch.bfloat16)
    
    # 初始化用于文本生成的 streamer
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=10)
    generation_config = dict(max_new_tokens=1024, do_sample=False, streamer=streamer)
    
    # 启动模型对话线程
    thread = Thread(
        target=model.chat, 
        kwargs=dict(
            tokenizer=tokenizer,
            pixel_values=pixel_values,
            question=prompt,
            history=None,
            return_history=False,
            generation_config=generation_config,
        )
    )
    thread.start()
    
    # 用于累积模型原始输出的变量
    raw_output = ""
    
    # 初始化状态变量，用于拆分 think 和 regular 部分
    think_mode = False
    think_content = ""
    regular_content = ""
    
    # 针对每个从 streamer 中获取的文本块进行处理
    for new_text in streamer:
        # 将原始新文本累加到 raw_output 中
        raw_output += new_text
        
        pos = 0
        while pos < len(new_text):
            idx_think = new_text.find("<think>", pos)
            idx_think_close = new_text.find("</think>", pos)
            # 如果本段中没有任何标签，则将剩余内容加入当前模式，并退出循环
            if idx_think == -1 and idx_think_close == -1:
                if think_mode:
                    think_content += new_text[pos:]
                    yield {"type": "think", "content": think_content}
                else:
                    regular_content += new_text[pos:]
                    yield {"type": "regular", "content": regular_content}
                break
            # 如果 <think> 出现得更早或 </think> 不存在
            if idx_think != -1 and (idx_think_close == -1 or idx_think < idx_think_close):
                # 先处理标签前的内容
                if think_mode:
                    think_content += new_text[pos:idx_think]
                    yield {"type": "think", "content": think_content}
                else:
                    regular_content += new_text[pos:idx_think]
                    yield {"type": "regular", "content": regular_content}
                pos = idx_think + len("<think>")
                think_mode = True
            else:
                # 处理 </think> 出现的情况
                if think_mode:
                    think_content += new_text[pos:idx_think_close]
                    yield {"type": "think", "content": think_content}
                    think_content = ""  # 清空 think 内容缓存
                else:
                    regular_content += new_text[pos:idx_think_close]
                    yield {"type": "regular", "content": regular_content}
                pos = idx_think_close + len("</think>")
                think_mode = False

    thread.join()  # 确保线程结束

    # 在终端打印完整的模型原始输出
    print("Complete raw model output:")
    print(raw_output)

# Main app function
def main():
    st.title("Video Analysis with Omni DeepSeek")
    st.markdown("Upload a video and provide a prompt to analyze it.")
    
    # Load model and tokenizer
    model, tokenizer = load_model_and_tokenizer()
    
    # Sidebar for inputs
    with st.sidebar:
        st.header("Upload and Settings")
        video_file = st.file_uploader("Upload Video", type=["mp4", "avi", "mov", "mkv"])
        # 添加提示词模板选择，下拉框中包含默认模板和omni-matrix模板
        template_option = st.selectbox("Select Prompt Template", options=["Default", "Omni-Matrix Template"])
        if template_option == "Default":
            prompt = st.text_area("Enter your prompt", value="Please describe this video", height=100)
        else:
            prompt = st.text_area("Enter your prompt", value=f"""
Analyze the video and determine whether the user requires assistance based on the video activity type and behavior. Generate the output in the following structured JSON format:

1. **help_needed**: A boolean value (true or false) indicating whether the user needs help based on the video content.
2. **video_description**: A brief description of the video content.
3. **video_type**: The type of activity in the video. Options include working, meeting, coding, gaming, watching, or other.
4. **function_call_name**: If help_needed is true, specify the name of the function to provide assistance. Options include draft_copy (drafting a copy), assist_coding (coding assistance), web_search (web search). If no help is needed, return an empty string.
5. **function_call_parameters**: If help is needed, provide the required parameters for the function call; otherwise, return an empty array. The parameters are defined as follows:
    - **draft_copy**: Two strings - the first one is the copy subject and the second one is the copy content.
        -- copy_subject(str): The subject of the copy
        -- copy_content(str): The content of the copy
    - **web_search**: 
        -- web_search_content(str): A single string containing the search query.
    - **assist_coding**:
        -- coding_subject(str): The subject of the code
        -- coding_content(str): The content of the code

**Input Requirements:**
The input is a description of the video, and the model needs to analyze it to determine user behavior and generate a JSON response in the following format:

json
{{
    "help_needed": true/false,
    "video_description": "Brief description of the video content",
    "video_type": "working"/"meeting"/"coding"/"gaming"/"watching"/"other",
    "function_call_name": "draft_email/assist_coding/web_search",
    "function_call_parameters": {{
        "parameter1":"parameter1 content", 
        "parameter2":"parameter2 content"
    }}
}}

**Examples:**
1. If the video shows the user debugging code and repeatedly checking documentation:
json
{{
    "help_needed": true,
    "video_description": "The user is debugging code and may need assistance.",
    "video_type": "coding",
    "function_call_name": "assist_coding",
    "function_call_parameters": {{
        "coding_subject": "Help the user implement quicksort.",
        "coding_content": "
        def quicksort(arr):
            if len(arr) <= 1:
                return arr

            pivot = arr[len(arr) // 2]
            left = [x for x in arr if x < pivot]
            middle = [x for x in arr if x == pivot]
            right = [x for x in arr if x > pivot]
            return quicksort(left) + middle + quicksort(right)
            "
    }}
}}

2. If the video shows the user watching a movie and no assistance is required:
json
{{
    "help_needed": false,
    "video_description": "The user is watching a movie.",
    "video_type": "watching",
    "function_call_name": "",
    "function_call_parameters": []
}}

3. If the video shows the user writing an email and might need assistance drafting it:
json
{{
    "help_needed": true,
    "video_description": "The user is writing an email and may need assistance.",
    "video_type": "working",
    "function_call_name": "draft_copy",
    "function_call_parameters": {{
        "copy_subject": "Follow-up Meeting", 
        "copy_content": "Please confirm your availability for the next meeting."
    }}
}}

4. If the video shows the user searching for a specific topic online:
json
{{
    "help_needed": true,
    "video_description": "The user is searching for information online.",
    "video_type": "working",
    "function_call_name": "web_search",
    "function_call_parameters": {{
        "web_search_content": "latest AI research papers"
    }}
}}
""", height=400)
        run_button = st.button("Analyze Video", type="primary")
        
        st.markdown("---")
        st.markdown("### Model Information")
        st.info("Using AlphaTok/omni-deepseek-v0 model")
    
    # Main content area with two columns
    col1, col2 = st.columns([1, 1])
    
    with col1:
        st.header("Input")
        if video_file:
            st.video(video_file)
            st.text(f"Prompt: {prompt}")
    
    with col2:
        st.header("Output")
        # 将 thinking 折叠框默认展开
        thinking_container = st.expander("Thinking Process", expanded=True)
        output_container = st.container()
    
    if run_button and video_file and prompt:
        # Save the uploaded video
        video_path = save_uploaded_file(video_file)
        
        # Create a progress bar
        progress_bar = st.progress(0.0)
        
        # Placeholders for streaming output
        thinking_placeholder = thinking_container.empty()
        output_placeholder = output_container.empty()
        
        try:
            progress_step = 0
            # 在流式输出过程中将进度条固定显示在 90%
            for result in process_video_and_run_inference(video_path, prompt, model, tokenizer):
                progress_step += 1
                progress_bar.progress(min(0.9, progress_step / 1024))
                if result["type"] == "think":
                    thinking_placeholder.markdown(f"""<div class="output-text">{result['content']}</div>""", unsafe_allow_html=True)
                elif result["type"] == "regular":
                    content = result["content"]
                    if re.search(r'```\s*json\s*\{', content):
                        json_content = re.search(r'```\s*json\s*(\{.*?\})\s*```', content, re.DOTALL)
                        if json_content:
                            output_placeholder.json(json_content.group(1))
                        else:
                            output_placeholder.markdown(f"""<div class="output-text">{content}</div>""", unsafe_allow_html=True)
                    else:
                        output_placeholder.markdown(f"""<div class="output-text">{content}</div>""", unsafe_allow_html=True)
            
            # 模型生成结束后完成进度条更新
            progress_bar.progress(1.0)
            time.sleep(0.5)
            progress_bar.empty()
            os.unlink(video_path)
            
        except Exception as e:
            st.error(f"An error occurred: {str(e)}")
            if os.path.exists(video_path):
                os.unlink(video_path)

if __name__ == "__main__":
    main()