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app.py

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+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()

requirements.txt

@@ -0,0 +1,10 @@
+decord
+transformers<4.50.0
+einops 
+timm
+accelerate>=0.26.0
+sentencepiece
+pandas
+tqdm
+flash-attn
+streamlit