Remove obsolete webui.py file

- Deleted webui.py as it was replaced by vidai/web.py in the multi-process architecture
- Functionality moved to separate web interface process
- Cleaned up legacy monolithic code that is no longer used

webui.py

-# Video AI Web Interface
-# Copyright (C) 2024 Stefy Lanza <stefy@sexhack.me>
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-from flask import Flask, request, render_template, render_template_string, send_from_directory
-import os
-import sys
-import torch
-from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
-import tempfile
-import subprocess
-import shutil
-import json
-import time
-import cv2
-import threading
-try:
-    import psutil
-    psutil_available = True
-except ImportError:
-    psutil_available = False
-    print("psutil not available, install with: pip install psutil")
-
-# Set PyTorch CUDA memory management
-os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
-
-try:
-    import pynvml
-    pynvml.nvmlInit()
-    nvml_available = True
-except ImportError:
-    try:
-        import nvidia_ml_py as pynvml
-        pynvml.nvmlInit()
-        nvml_available = True
-    except ImportError:
-        nvml_available = False
-        print("NVIDIA ML library not available, install with: pip install nvidia-ml-py")
-
-try:
-    import flash_attn
-    flash_attn_available = True
-except ImportError:
-    flash_attn_available = False
-    print("flash_attn not available, install with: pip install flash-attn")
-
-force_ffmpeg = '--ffmpeg' in sys.argv
-optimize = '--optimize' in sys.argv
-flash_attn = '--flash' in sys.argv
-model_default = "Qwen/Qwen2.5-VL-7B-Instruct"
-if '--model' in sys.argv:
-    idx = sys.argv.index('--model')
-    if idx + 1 < len(sys.argv):
-        model_default = sys.argv[idx + 1]
-allowed_dir = None
-if '--dir' in sys.argv:
-    idx = sys.argv.index('--dir')
-    if idx + 1 < len(sys.argv):
-        allowed_dir = os.path.abspath(sys.argv[idx + 1])
-
-app = Flask(__name__)
-os.makedirs('static', exist_ok=True)
-status = "Idle"
-start_time = 0
-cancel = False
-analysis_result = None
-analysis_thread = None
-model_cache = {}
-processor_cache = {}
-current_frame = 0
-total_frames = 0
-
-# System prompt
-config_dir = os.path.expanduser("~/.config/AIVideo")
-os.makedirs(config_dir, exist_ok=True)
-system_prompt_file = os.path.join(config_dir, "system_prompt.txt")
-if os.path.exists(system_prompt_file):
-    with open(system_prompt_file, "r") as f:
-        system_prompt = f.read()
-else:
-    system_prompt = "when the action done by the person or persons in the frame changes, or where the scenario change, or where there an active action after a long time of no actions happening"
-
-# GPU delegation
-gpu_mem = []
-if torch.cuda.is_available():
-    for i in range(torch.cuda.device_count()):
-        gpu_mem.append(torch.cuda.get_device_properties(i).total_memory)
-    max_gpu = gpu_mem.index(max(gpu_mem)) if gpu_mem else 0
-    min_gpu = gpu_mem.index(min(gpu_mem)) if gpu_mem else 0
-else:
-    max_gpu = min_gpu = 0
-
-# Set OpenCV to smaller GPU if available
-try:
-    if cv2 and hasattr(cv2, 'cuda'):
-        cv2.cuda.setDevice(min_gpu)
-except:
-    pass
-
-def extract_frames(video_path, interval=10):
-    global optimize
-    if cv2 and not force_ffmpeg:
-        cap = cv2.VideoCapture(video_path)
-        fps = cap.get(cv2.CAP_PROP_FPS)
-        frame_interval = int(fps * interval)
-        frames = []
-        count = 0
-        while cap.isOpened():
-            ret, frame = cap.read()
-            if not ret:
-                break
-            if count % frame_interval == 0:
-                if optimize:
-                    height, width = frame.shape[:2]
-                    new_width = 640
-                    new_height = int(height * new_width / width)
-                    frame = cv2.resize(frame, (new_width, new_height))
-                temp_img = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
-                cv2.imwrite(temp_img.name, frame)
-                frames.append((temp_img.name, count / fps))
-            count += 1
-        cap.release()
-        return frames, None
-    else:
-        output_dir = tempfile.mkdtemp()
-        vf = f"fps=1/{interval}"
-        if optimize:
-            vf += ",scale=640:-1"
-        cmd = ["ffmpeg", "-i", video_path, "-vf", vf, os.path.join(output_dir, "frame_%04d.jpg")]
-        subprocess.run(cmd, check=True, capture_output=True)
-        frames = []
-        for file in sorted(os.listdir(output_dir)):
-            if file.endswith('.jpg'):
-                path = os.path.join(output_dir, file)
-                frame_num = int(file.split('_')[1].split('.')[0])
-                ts = (frame_num - 1) * interval
-                frames.append((path, ts))
-        return frames, output_dir
-def analyze_media_thread(media_path, prompt, model_path, interval=10):
-    global analysis_result, cancel, status
-    analysis_result = analyze_media(media_path, prompt, model_path, interval)
-    if cancel:
-        status = "Cancelled"
-    else:
-        status = "Completed"
-
-def is_video(file_path):
-    return file_path.lower().endswith(('.mp4', '.avi', '.mov', '.mkv'))
-
-def analyze_single_image(image_path, prompt, model, processor):
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {"type": "image", "image": image_path},
-                {"type": "text", "text": prompt},
-            ],
-        }
-    ]
-
-    inputs = processor.apply_chat_template(
-        messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt"
-    )
-
-    inputs = {k: v.to(model.device) for k, v in inputs.items()}
-
-    generated_ids = model.generate(**inputs, max_new_tokens=128)
-    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
-    )
-    return output_text[0]
-
-def analyze_media(media_path, prompt, model_path, interval=10):
-    global status, start_time, model_cache, processor_cache, system_prompt, current_frame, total_frames
-    torch.cuda.empty_cache()
-    status = "Loading model"
-    start_time = time.time()
-    if model_path not in model_cache:
-        kwargs = {"device_map": "auto", "low_cpu_mem_usage": True}
-        if flash_attn:
-            if flash_attn_available:
-                kwargs["attn_implementation"] = "flash_attention_2"
-                kwargs["dtype"] = torch.float16
-            else:
-                print("Flash Attention 2 requested but flash_attn not installed. Install with: pip install flash-attn")
-        if os.path.exists(model_path):
-            try:
-                model = Qwen2_5_VLForConditionalGeneration.from_pretrained(model_path, **kwargs)
-                proc_path = model_path
-            except:
-                model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", **kwargs)
-                proc_path = "Qwen/Qwen2.5-VL-7B-Instruct"
-        else:
-            model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", **kwargs)
-            proc_path = "Qwen/Qwen2.5-VL-7B-Instruct"
-        model_cache[model_path] = model
-        processor_cache[model_path] = AutoProcessor.from_pretrained(proc_path)
-    else:
-        model = model_cache[model_path]
-        proc_path = model_path if os.path.exists(model_path) else "Qwen/Qwen2.5-VL-7B-Instruct"
-    processor = processor_cache[model_path]
-    status = "Model loaded"
-    full_prompt = system_prompt + " " + prompt if system_prompt else prompt
-    if is_video(media_path):
-        status = "Extracting frames"
-        frames, output_dir = extract_frames(media_path)
-        total_frames = len(frames)
-        status = f"Analyzing frames (0/{total_frames})"
-        descriptions = []
-        for i, (frame_path, ts) in enumerate(frames):
-            if cancel:
-                break
-            current_frame = i + 1
-            status = f"Analyzing frame {current_frame}/{total_frames}"
-            desc = analyze_single_image(frame_path, full_prompt, model, processor)
-            descriptions.append(f"At {ts:.2f}s: {desc}")
-            os.unlink(frame_path)  # Remove frame after analysis to save disk space
-        if output_dir:
-            shutil.rmtree(output_dir)
-        status = "Summarizing video"
-        summary_prompt = f"Summarize the video based on frame descriptions, indicating scene changes and timestamps: {' '.join(descriptions)}"
-        messages = [{"role": "user", "content": [{"type": "text", "text": summary_prompt}]}]
-        inputs = processor.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt")
-        inputs = {k: v.to(model.device) for k, v in inputs.items()}
-        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)
-        summary = output_text[0]
-        result = f"Frame Descriptions:\n" + "\n".join(descriptions) + f"\n\nSummary:\n{summary}"
-        if len(descriptions) > 5:
-            mid_ts = frames[len(frames)//2][1]
-            cmd1 = ["ffmpeg", "-y", "-i", media_path, "-ss", "0", "-t", str(mid_ts), "-c", "copy", "static/segment1.mp4"]
-            subprocess.run(cmd1, check=True)
-            cmd2 = ["ffmpeg", "-y", "-i", media_path, "-ss", str(mid_ts), "-c", "copy", "static/segment2.mp4"]
-            subprocess.run(cmd2, check=True)
-            result += f"\nVideo split into segments: <a href='/static/segment1.mp4'>Segment 1</a>, <a href='/static/segment2.mp4'>Segment 2</a>"
-        status = "Idle"
-        return result
-    else:
-        result = analyze_single_image(media_path, full_prompt, model, processor)
-        torch.cuda.empty_cache()
-        status = "Idle"
-        current_frame = 0
-        total_frames = 0
-        return result
-
-def list_files(directory):
-    files = []
-    for root, dirs, filenames in os.walk(directory):
-        for filename in filenames:
-            files.append(os.path.join(root, filename))
-    return files
-
-@app.route('/', methods=['GET', 'POST'])
-def index():
-    global system_prompt, analysis_result
-    result = None
-    model_path_default = "./VideoModel" if os.path.exists("./VideoModel") else model_default
-    if analysis_result is not None:
-        result = analysis_result
-        analysis_result = None
-    if request.method == 'POST':
-        model_path = request.form.get('model_path', model_path_default)
-        prompt = request.form.get('prompt', 'Describe this image.')
-        uploaded_file = request.files.get('file')
-        local_path = request.form.get('local_path')
-
-        upload_id = request.form.get('upload_id')
-        if upload_id:
-            file_name = request.form.get('file_name', 'uploaded_file')
-            media_path = os.path.join(tempfile.gettempdir(), f"{upload_id}_{file_name}")
-        elif uploaded_file and uploaded_file.filename:
-            with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.filename)[1]) as tmp:
-                tmp.write(uploaded_file.read())
-                media_path = tmp.name
-        elif local_path and os.path.exists(local_path):
-            if allowed_dir and not os.path.abspath(local_path).startswith(allowed_dir):
-                result = "Access denied to file"
-            else:
-                media_path = local_path
-        else:
-            result = "Provide a file or path"
-
-        if 'media_path' in locals():
-            interval = int(request.form.get('interval', 10))
-            global analysis_thread, cancel
-            cancel = False
-            analysis_result = None
-            analysis_thread = threading.Thread(target=analyze_media_thread, args=(media_path, prompt, model_path, interval))
-            analysis_thread.start()
-            result = "Analysis started. Check status for progress."
-            if uploaded_file:
-                # Keep file for thread
-                pass
-
-    html = '''
-    <!DOCTYPE html>
-    <html>
-    <head>
-        <title>VideoModel AI</title>
-        <style>
-            body { font-family: Arial, sans-serif; background-color: #f4f4f4; margin: 0; padding: 20px; display: flex; justify-content: center; align-items: flex-start; }
-            .main { flex: 1; max-width: 800px; background: white; padding: 20px; border-radius: 8px; box-shadow: 0 0 10px rgba(0,0,0,0.1); margin-right: 20px; }
-            .sidebar { width: 300px; background: white; padding: 20px; border-radius: 8px; box-shadow: 0 0 10px rgba(0,0,0,0.1); }
-            h1 { color: #333; text-align: center; }
-            nav { text-align: center; margin-bottom: 20px; }
-            nav a { margin: 0 10px; text-decoration: none; color: #007bff; }
-            form { margin-bottom: 20px; }
-            label { display: block; margin-bottom: 5px; }
-            input[type="text"], input[type="file"], textarea { width: 100%; padding: 8px; margin-bottom: 10px; border: 1px solid #ccc; border-radius: 4px; }
-            input[type="submit"] { background: #007bff; color: white; padding: 10px; border: none; border-radius: 4px; cursor: pointer; }
-            input[type="submit"]:hover { background: #0056b3; }
-            .result { background: #e9ecef; padding: 10px; border-radius: 4px; }
-            .stats { font-size: 14px; }
-        </style>
-        <script>
-            function openFileBrowser() {
-                window.open('/files', 'filebrowser', 'width=600,height=400');
-            }
-            async function updateStats() {
-                try {
-                    const response = await fetch('/stats');
-                    const data = await response.json();
-                    let html = '<h3>GPU Stats</h3>';
-                    html += `<p style="color: ${data.status === 'Idle' ? 'green' : 'orange'};">Status: ${data.status}</p>`;
-                    if (data.elapsed > 0) {
-                        html += `<p>Elapsed: ${data.elapsed.toFixed(1)}s</p>`;
-                    }
-                    if (data.gpu_count > 0) {
-                        data.gpus.forEach((gpu, i) => {
-                            let memPercent = (gpu.memory_used / gpu.memory_total * 100).toFixed(1);
-                            html += `<p>GPU ${i}: ${gpu.name}<br>Memory: <progress value="${gpu.memory_used}" max="${gpu.memory_total}"></progress> ${gpu.memory_used.toFixed(2)} / ${gpu.memory_total.toFixed(2)} GB (${memPercent}%)<br>Utilization: ${gpu.utilization}%</p>`;
-                        });
-                    } else {
-                        html += '<p>No GPUs detected</p>';
-                    }
-                    html += `<p>CPU: ${data.cpu_percent.toFixed(1)}%</p>`;
-                    html += `<p>RAM: ${data.ram_used.toFixed(2)} / ${data.ram_total.toFixed(2)} GB</p>`;
-                    document.getElementById('stats').innerHTML = html;
-                    if (data.result) {
-                        document.getElementById('result_div').innerHTML = '<h3>Result:</h3><p>' + data.result + '</p>';
-                        document.getElementById('result_div').style.display = 'block';
-                    }
-                } catch (e) {
-                    document.getElementById('stats').innerHTML = '<p>Error loading stats</p>';
-                }
-            }
-            setInterval(updateStats, 5000);
-            window.onload = updateStats;
-
-            function cancelAnalysis() {
-                fetch('/cancel', {method: 'POST'}).then(() => updateStats());
-            }
-
-            // Upload progress
-            const fileInput = document.getElementById('fileInput');
-            if (fileInput) {
-                fileInput.addEventListener('change', function() {
-                const file = this.files[0];
-                if (file) {
-                    const xhr = new XMLHttpRequest();
-                    const formData = new FormData();
-                    formData.append('file', file);
-                    const startTime = Date.now();
-                    xhr.upload.addEventListener('progress', function(e) {
-                        if (e.lengthComputable) {
-                            const percent = (e.loaded / e.total) * 100;
-                            document.getElementById('uploadProgress').value = percent;
-                            document.getElementById('uploadProgress').style.display = 'block';
-                            const speed = e.loaded / ((Date.now() - startTime) / 1000);
-                            const remaining = (e.total - e.loaded) / speed;
-                            document.getElementById('progressText').innerText = `Uploaded ${(e.loaded / 1024 / 1024).toFixed(2)} MB of ${(e.total / 1024 / 1024).toFixed(2)} MB (${percent.toFixed(1)}%) - Speed: ${(speed / 1024 / 1024).toFixed(2)} MB/s - ETA: ${Math.round(remaining)}s`;
-                        }
-                    });
-                    xhr.addEventListener('load', function() {
-                        document.getElementById('progressText').innerText = 'Upload complete';
-                    });
-                    xhr.open('POST', '/upload_progress');
-                    xhr.send(formData);
-                }
-            });
-        }
-        </script>
-    </head>
-    <body>
-        <div class="main">
-            <h1>VideoModel AI Web Interface</h1>
-            <nav>
-                <a href="/">Analysis</a> | <a href="/train">Training</a>
-            </nav>
-            <h2>Analyze Image/Video</h2>
-            <form method="post" enctype="multipart/form-data">
-                <label>Model Path: <input type="text" name="model_path" value="{{ model_path_default }}"></label>
-                <p><a href="/system">Edit System Prompt</a></p>
-                <label>Upload File: <input type="file" name="file" accept="image/*,video/*" id="fileInput"></label>
-                <progress id="uploadProgress" value="0" max="100" style="display:none; width:100%;"></progress>
-                <div id="progressText"></div>
-                {% if allowed_dir %}
-                <label>Or Local Path: <input type="text" name="local_path" id="local_path"> <button type="button" onclick="openFileBrowser()">Browse</button></label>
-                {% endif %}
-                <label>Prompt: <textarea name="prompt" rows="5" cols="80">Describe this image.</textarea></label>
-                <input type="submit" value="Analyze">
-                <button type="button" onclick="cancelAnalysis()">Cancel Analysis</button>
-            </form>
-            <div class="result" id="result_div" style="display:none;"></div>
-            {% if result %}
-            <div class="result">
-                <h3>Result:</h3>
-                <p>{{ result }}</p>
-            </div>
-            {% endif %}
-        </div>
-        <div class="sidebar">
-            <div id="stats" class="stats">Loading stats...</div>
-        </div>
-    </body>
-    </html>
-    '''
-    html = '''
-<!DOCTYPE html>
-<html>
-<head>
-    <title>VideoModel AI</title>
-    <style>
-        body { font-family: Arial, sans-serif; background-color: #f4f4f4; margin: 0; padding: 20px; display: flex; justify-content: center; align-items: flex-start; }
-        .main { flex: 1; max-width: 800px; background: white; padding: 20px; border-radius: 8px; box-shadow: 0 0 10px rgba(0,0,0,0.1); margin-right: 20px; }
-        .sidebar { width: 300px; background: white; padding: 20px; border-radius: 8px; box-shadow: 0 0 10px rgba(0,0,0,0.1); }
-        h1 { color: #333; text-align: center; }
-        nav { text-align: center; margin-bottom: 20px; }
-        nav a { margin: 0 10px; text-decoration: none; color: #007bff; }
-        form { margin-bottom: 20px; }
-        label { display: block; margin-bottom: 5px; }
-        input[type="text"], input[type="file"], textarea { width: 100%; padding: 8px; margin-bottom: 10px; border: 1px solid #ccc; border-radius: 4px; }
-        input[type="submit"] { background: #007bff; color: white; padding: 10px; border: none; border-radius: 4px; cursor: pointer; }
-        input[type="submit"]:hover { background: #0056b3; }
-        .result { background: #e9ecef; padding: 10px; border-radius: 4px; }
-        .stats { font-size: 14px; }
-    </style>
-</head>
-<body>
-    <div class="main">
-        <h1>VideoModel AI Web Interface</h1>
-        <nav>
-            <a href="/">Analysis</a> | <a href="/train">Training</a>
-        </nav>
-        <h2>Analyze Image/Video</h2>
-        <form method="post" enctype="multipart/form-data">
-            <label>Model Path: <input type="text" name="model_path" value="{{ model_path_default }}"></label>
-            <p><a href="/system">Edit System Prompt</a></p>
-            <label>Upload File: <input type="file" name="file" accept="image/*,video/*" id="fileInput"></label>
-            <progress id="uploadProgress" value="0" max="100" style="display:none; width:100%;"></progress>
-            <div id="progressText"></div>
-            {% if allowed_dir %}
-            <label>Or Local Path: <input type="text" name="local_path" id="local_path"> <button type="button" onclick="openFileBrowser()">Browse</button></label>
-            {% endif %}
-            <label>Frame Interval (seconds): <input type="number" name="interval" value="10" min="1"></label>
-            <label>Prompt: <textarea name="prompt" rows="5" cols="80">Describe this image.</textarea></label>
-            <input type="submit" value="Analyze">
-            <button type="button" onclick="cancelAnalysis()">Cancel Analysis</button>
-        </form>
-        <div class="result" id="result_div" style="display:none;"></div>
-        {% if result %}
-        <div class="result">
-            <h3>Result:</h3>
-            <p>{{ result }}</p>
-        </div>
-        {% endif %}
-    </div>
-    <div class="sidebar">
-        <div id="stats" class="stats">Loading stats...</div>
-    </div>
-    <script>
-        function openFileBrowser() {
-            window.open('/files', 'filebrowser', 'width=600,height=400');
-        }
-        async function updateStats() {
-            try {
-                const response = await fetch('/stats');
-                const data = await response.json();
-                let html = '<h3>GPU Stats</h3>';
-                html += `<p style="color: ${data.status === 'Idle' ? 'green' : 'orange'};">Status: ${data.status}</p>`;
-                if (data.elapsed > 0) {
-                    html += `<p>Elapsed: ${data.elapsed.toFixed(1)}s</p>`;
-                }
-                if (data.gpu_count > 0) {
-                    data.gpus.forEach((gpu, i) => {
-                        let memPercent = (gpu.memory_used / gpu.memory_total * 100).toFixed(1);
-                        html += `<p>GPU ${i}: ${gpu.name}<br>Memory: <progress value="${gpu.memory_used}" max="${gpu.memory_total}"></progress> ${gpu.memory_used.toFixed(2)} / ${gpu.memory_total.toFixed(2)} GB (${memPercent}%)<br>Utilization: ${gpu.utilization}%</p>`;
-                    });
-                } else {
-                    html += '<p>No GPUs detected</p>';
-                }
-                html += `<p>CPU: ${data.cpu_percent.toFixed(1)}%</p>`;
-                html += `<p>RAM: ${data.ram_used.toFixed(2)} / ${data.ram_total.toFixed(2)} GB</p>`;
-                document.getElementById('stats').innerHTML = html;
-                if (data.result) {
-                    document.getElementById('result_div').innerHTML = '<h3>Result:</h3><p>' + data.result + '</p>';
-                    document.getElementById('result_div').style.display = 'block';
-                }
-            } catch (e) {
-                document.getElementById('stats').innerHTML = '<p>Error loading stats</p>';
-            }
-        }
-        setInterval(updateStats, 5000);
-        window.onload = updateStats;
-
-        function cancelAnalysis() {
-            fetch('/cancel', {method: 'POST'}).then(() => updateStats());
-        }
-
-        // Upload progress with chunked upload
-        const form = document.querySelector('form');
-        if (form) {
-            form.addEventListener('submit', async function(e) {
-                e.preventDefault();
-                const fileInput = document.getElementById('fileInput');
-                const file = fileInput.files[0];
-                if (!file) {
-                    // Submit form normally if no file
-                    const formData = new FormData(this);
-                    const xhr = new XMLHttpRequest();
-                    xhr.addEventListener('load', function() {
-                        window.location.reload();
-                    });
-                    xhr.open('POST', '/');
-                    xhr.send(formData);
-                    return;
-                }
-                const chunkSize = 1024 * 1024; // 1MB
-                const totalChunks = Math.ceil(file.size / chunkSize);
-                const uploadId = Date.now().toString();
-                const concurrency = 1;
-                let chunksSent = 0;
-                
-                async function sendChunk(index) {
-                    const start = index * chunkSize;
-                    const end = Math.min(start + chunkSize, file.size);
-                    const chunk = file.slice(start, end);
-                    const formData = new FormData();
-                    formData.append('chunk', chunk);
-                    formData.append('chunk_index', index);
-                    formData.append('total_chunks', totalChunks);
-                    formData.append('file_name', file.name);
-                    formData.append('upload_id', uploadId);
-                    
-                    return new Promise((resolve) => {
-                        const xhr = new XMLHttpRequest();
-                        xhr.upload.addEventListener('progress', function(e) {
-                            if (e.lengthComputable) {
-                                const percent = ((chunksSent * chunkSize + e.loaded) / file.size) * 100;
-                                document.getElementById('uploadProgress').value = percent;
-                                document.getElementById('uploadProgress').style.display = 'block';
-                                const speed = (chunksSent * chunkSize + e.loaded) / ((Date.now() - startTime) / 1000);
-                                const remaining = (file.size - (chunksSent * chunkSize + e.loaded)) / speed;
-                                document.getElementById('progressText').innerText = `Uploaded ${((chunksSent * chunkSize + e.loaded) / 1024 / 1024).toFixed(2)} MB of ${(file.size / 1024 / 1024).toFixed(2)} MB (${percent.toFixed(1)}%) - Speed: ${(speed / 1024 / 1024).toFixed(2)} MB/s - ETA: ${Math.round(remaining)}s`;
-                            }
-                        });
-                        xhr.addEventListener('load', function() {
-                            chunksSent++;
-                            resolve();
-                        });
-                        xhr.open('POST', '/upload_chunk');
-                        xhr.send(formData);
-                    });
-                }
-                
-                const startTime = Date.now();
-                for (let i = 0; i < totalChunks; i += concurrency) {
-                    const promises = [];
-                    for (let j = 0; j < concurrency && i + j < totalChunks; j++) {
-                        promises.push(sendChunk(i + j));
-                    }
-                    await Promise.all(promises);
-                }
-
-                // All chunks sent, reassembling
-                document.getElementById('progressText').innerText = 'Reassembling file...';
-                document.getElementById('uploadProgress').value = 100;
-
-                // Submit form
-                const formData2 = new FormData(form);
-                formData2.append('upload_id', uploadId);
-                formData2.append('file_name', file.name);
-                const xhr2 = new XMLHttpRequest();
-                xhr2.addEventListener('load', function() {
-                    window.location.reload();
-                });
-                xhr2.open('POST', '/');
-                xhr2.send(formData2);
-            });
-        }
-    </script>
-</body>
-</html>
-    '''
-    return render_template_string(html, result=result, model_path_default=model_path_default, allowed_dir=allowed_dir)
-
-@app.route('/train', methods=['GET', 'POST'])
-def train():
-    model_path_default = "./VideoModel" if os.path.exists("./VideoModel") else model_default
-    message = None
-    if request.method == 'POST':
-        uploaded_data = request.files.get('data')
-        train_dir = request.form.get('train_dir')
-        description = request.form.get('description')
-        output_model = request.form.get('output_model', './VideoModel')
-
-        if uploaded_data and uploaded_data.filename:
-            if is_video(uploaded_data.filename):
-                with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_data.filename)[1]) as tmp:
-                    tmp.write(uploaded_data.read())
-                    video_path = tmp.name
-                train_path = tempfile.mkdtemp()
-                frames, output_dir = extract_frames(video_path)
-                for frame_path, ts in frames:
-                    shutil.move(frame_path, os.path.join(train_path, f"frame_{ts:.2f}.jpg"))
-                if output_dir:
-                    shutil.rmtree(output_dir)
-                os.unlink(video_path)
-            else:
-                with tempfile.TemporaryDirectory() as tmp_dir:
-                    zip_path = os.path.join(tmp_dir, "data.zip")
-                    with open(zip_path, "wb") as f:
-                        f.write(uploaded_data.read())
-                    extract_dir = os.path.join(tmp_dir, "extracted")
-                    shutil.unpack_archive(zip_path, extract_dir)
-                    train_path = extract_dir
-        elif train_dir and os.path.isdir(train_dir):
-            if allowed_dir and not os.path.abspath(train_dir).startswith(allowed_dir):
-                message = "Access denied to directory"
-            else:
-                train_path = train_dir
-        else:
-            message = "Provide training data"
-
-        if 'train_path' in locals():
-            desc_file = os.path.join(train_path, "description.txt")
-            with open(desc_file, "w") as f:
-                f.write(description)
-
-            status = "Training"
-            cmd = ["python", "videotrain", train_path, "--output_dir", output_model]
-            result = subprocess.run(cmd, capture_output=True, text=True)
-            status = "Idle"
-            if result.returncode == 0:
-                message = "Training completed!"
-            else:
-                message = f"Training failed: {result.stderr}"
-
-    html = '''
-    <!DOCTYPE html>
-    <html>
-    <head>
-        <title>VideoModel AI - Training</title>
-        <style>
-            body { font-family: Arial, sans-serif; background-color: #f4f4f4; margin: 0; padding: 20px; display: flex; justify-content: center; align-items: flex-start; }
-            .main { flex: 1; max-width: 800px; background: white; padding: 20px; border-radius: 8px; box-shadow: 0 0 10px rgba(0,0,0,0.1); margin-right: 20px; }
-            .sidebar { width: 300px; background: white; padding: 20px; border-radius: 8px; box-shadow: 0 0 10px rgba(0,0,0,0.1); }
-            h1 { color: #333; text-align: center; }
-            nav { text-align: center; margin-bottom: 20px; }
-            nav a { margin: 0 10px; text-decoration: none; color: #007bff; }
-            form { margin-bottom: 20px; }
-            label { display: block; margin-bottom: 5px; }
-            input[type="text"], input[type="file"], textarea { width: 100%; padding: 8px; margin-bottom: 10px; border: 1px solid #ccc; border-radius: 4px; }
-            input[type="submit"] { background: #007bff; color: white; padding: 10px; border: none; border-radius: 4px; cursor: pointer; }
-            input[type="submit"]:hover { background: #0056b3; }
-            .message { background: #e9ecef; padding: 10px; border-radius: 4px; }
-            .stats { font-size: 14px; }
-        </style>
-        <script>
-            async function updateStats() {
-                try {
-                    const response = await fetch('/stats');
-                    const data = await response.json();
-                    let html = '<h3>GPU Stats</h3>';
-                    html += `<p style="color: ${data.status === 'Idle' ? 'green' : 'orange'};">Status: ${data.status}</p>`;
-                    if (data.gpu_count > 0) {
-                        data.gpus.forEach((gpu, i) => {
-                            let memPercent = (gpu.memory_used / gpu.memory_total * 100).toFixed(1);
-                            html += `<p>GPU ${i}: ${gpu.name}<br>Memory: <progress value="${gpu.memory_used}" max="${gpu.memory_total}"></progress> ${gpu.memory_used.toFixed(2)} / ${gpu.memory_total.toFixed(2)} GB (${memPercent}%)<br>Utilization: ${gpu.utilization}%</p>`;
-                        });
-                    } else {
-                        html += '<p>No GPUs detected</p>';
-                    }
-                    html += `<p>CPU: ${data.cpu_percent.toFixed(1)}%</p>`;
-                    html += `<p>RAM: ${data.ram_used.toFixed(2)} / ${data.ram_total.toFixed(2)} GB</p>`;
-                    document.getElementById('stats').innerHTML = html;
-                    if (data.result) {
-                        document.getElementById('result_div').innerHTML = '<h3>Result:</h3><p>' + data.result + '</p>';
-                        document.getElementById('result_div').style.display = 'block';
-                    }
-                } catch (e) {
-                    document.getElementById('stats').innerHTML = '<p>Error loading stats</p>';
-                }
-            }
-            setInterval(updateStats, 5000);
-            window.onload = updateStats;
-        </script>
-    </head>
-    <body>
-        <div class="main">
-            <h1>VideoModel AI Web Interface</h1>
-            <nav>
-                <a href="/">Analysis</a> | <a href="/train">Training</a>
-            </nav>
-            <h2>Train Model</h2>
-            <form method="post" enctype="multipart/form-data">
-                <label>Model Path: <input type="text" name="model_path" value="{{ model_path_default }}"></label>
-                <label>Upload Data (ZIP or Video): <input type="file" name="data" accept=".zip,.mp4,.avi"></label>
-                {% if allowed_dir %}
-                <label>Or Directory Path: <input type="text" name="train_dir"></label>
-                {% endif %}
-                <label>Description: <textarea name="description"></textarea></label>
-                <label>Output Model Path: <input type="text" name="output_model" value="./VideoModel"></label>
-                <input type="submit" value="Start Training">
-            </form>
-            <div class="message" id="result_div" style="display:none;"></div>
-            {% if message %}
-            <div class="message">
-                <p>{{ message }}</p>
-            </div>
-            {% endif %}
-        </div>
-        <div class="sidebar">
-            <div id="stats" class="stats">Loading stats...</div>
-        </div>
-    </body>
-    </html>
-    '''
-    return render_template_string(html, message=message, allowed_dir=allowed_dir, model_path_default=model_path_default)
-
-
-@app.route('/cancel', methods=['POST'])
-def cancel_analysis():
-    global cancel, status
-    cancel = True
-    status = "Cancelled"
-    return 'Analysis cancelled'
-
-@app.route('/upload_chunk', methods=['POST'])
-def upload_chunk():
-    chunk = request.files['chunk']
-    chunk_index = int(request.form['chunk_index'])
-    total_chunks = int(request.form['total_chunks'])
-    file_name = request.form['file_name']
-    upload_id = request.form['upload_id']
-    temp_dir = os.path.join(tempfile.gettempdir(), upload_id)
-    os.makedirs(temp_dir, exist_ok=True)
-    chunk_path = os.path.join(temp_dir, str(chunk_index))
-    chunk.save(chunk_path)
-    if len(os.listdir(temp_dir)) == total_chunks:
-        final_path = os.path.join(tempfile.gettempdir(), f"{upload_id}_{file_name}")
-        with open(final_path, 'wb') as f:
-            for i in range(total_chunks):
-                with open(os.path.join(temp_dir, str(i)), 'rb') as cf:
-                    f.write(cf.read())
-                os.unlink(os.path.join(temp_dir, str(i)))
-        os.rmdir(temp_dir)
-    return 'OK'
-
-@app.route('/system', methods=['GET', 'POST'])
-def system_page():
-    global system_prompt
-    if request.method == 'POST':
-        system_prompt = request.form.get('system_prompt', '')
-        with open(system_prompt_file, "w") as f:
-            f.write(system_prompt)
-    html = f'''
-    <!DOCTYPE html>
-    <html>
-    <head>
-        <title>System Prompt</title>
-        <style>
-            body {{ font-family: Arial, sans-serif; background-color: #f4f4f4; margin: 0; padding: 20px; }}
-            .container {{ max-width: 800px; margin: auto; background: white; padding: 20px; border-radius: 8px; box-shadow: 0 0 10px rgba(0,0,0,0.1); }}
-            h1 {{ color: #333; text-align: center; }}
-            textarea {{ width: 100%; height: 200px; }}
-            input[type="submit"] {{ background: #007bff; color: white; padding: 10px; border: none; border-radius: 4px; cursor: pointer; }}
-            input[type="submit"]:hover {{ background: #0056b3; }}
-        </style>
-    </head>
-    <body>
-        <div class="container">
-            <h1>Edit System Prompt</h1>
-            <form method="post">
-                <textarea name="system_prompt">{system_prompt}</textarea><br>
-                <input type="submit" value="Save">
-            </form>
-            <a href="/">Back to Analysis</a>
-        </div>
-    </body>
-    </html>
-    '''
-    return html
-
-@app.route('/static/<path:filename>')
-def serve_static(filename):
-    return send_from_directory('static', filename)
-
-@app.route('/stats')
-def stats():
-    global status, start_time, analysis_result
-    data = {'status': status}
-    if status != "Idle":
-        data['elapsed'] = time.time() - start_time
-    else:
-        data['elapsed'] = 0
-    data['result'] = analysis_result.replace('\n', '<br>') if analysis_result else ''
-    if psutil_available:
-        data['cpu_percent'] = psutil.cpu_percent()
-        ram = psutil.virtual_memory()
-        data['ram_used'] = ram.used / 1024**3
-        data['ram_total'] = ram.total / 1024**3
-    else:
-        data['cpu_percent'] = 0
-        data['ram_used'] = 0
-        data['ram_total'] = 0
-    if torch.cuda.is_available():
-        data['gpu_count'] = torch.cuda.device_count()
-        data['gpus'] = []
-        for i in range(torch.cuda.device_count()):
-            gpu = {
-                'name': torch.cuda.get_device_name(i),
-                'memory_used': torch.cuda.memory_allocated(i) / 1024**3,  # GB
-                'memory_total': torch.cuda.get_device_properties(i).total_memory / 1024**3,
-            }
-            if nvml_available:
-                try:
-                    handle = pynvml.nvmlDeviceGetHandleByIndex(i)
-                    util = pynvml.nvmlDeviceGetHandleByIndex(i)
-                    util = pynvml.nvmlDeviceGetUtilizationRates(handle)
-                    gpu['utilization'] = util.gpu
-                except:
-                    gpu['utilization'] = 0
-            data['gpus'].append(gpu)
-    else:
-        data['gpu_count'] = 0
-    return json.dumps(data)
-
-@app.route('/files')
-def files():
-    if not allowed_dir:
-        return "File browsing not enabled", 403
-    path = request.args.get('path', allowed_dir)
-    if not os.path.abspath(path).startswith(allowed_dir):
-        return "Access denied", 403
-    try:
-        items = os.listdir(path)
-        dirs = [os.path.join(path, i) for i in items if os.path.isdir(os.path.join(path, i))]
-        files_list = [os.path.join(path, i) for i in items if os.path.isfile(os.path.join(path, i))]
-    except:
-        dirs = []
-        files_list = []
-    html = f'''
-    <!DOCTYPE html>
-    <html>
-    <head>
-        <title>File Browser</title>
-        <style>
-            body {{ font-family: Arial, sans-serif; }}
-            ul {{ list-style: none; }}
-            li {{ margin: 5px 0; }}
-            a {{ text-decoration: none; color: #007bff; cursor: pointer; }}
-            a:hover {{ text-decoration: underline; }}
-        </style>
-    </head>
-    <body>
-        <h2>Files in {path}</h2>
-        <ul>
-    '''
-    for d in dirs:
-        html += f"<li><a href='/files?path={d}'>{os.path.basename(d)}/</a></li>"
-    for f in files_list:
-        html += f"<li><a onclick=\"window.opener.document.getElementById('local_path').value='{f}'; window.close();\">{os.path.basename(f)}</a></li>"
-    html += '''
-        </ul>
-    </body>
-    </html>
-    '''
-    return html
-
-if __name__ == "__main__":
-    app.run(host='0.0.0.0', debug=True)