Delete app.py

app.py

@@ -1,192 +0,0 @@
-import os
-import gradio as gr
-import torch
-from PIL import Image
-from pathlib import Path
-import io
-import sys
-import traceback
-from huggingface_hub import hf_hub_download
-# For live system monitoring
-import psutil
-import GPUtil
-
-# =========================================
-# 1. Define Hugging Face dataset + weights
-# =========================================
-
-HF_DATASET_REPO = "roll-ai/FloVD-weights"
-
-WEIGHT_FILES = {
-    "ckpt/FVSM/FloVD_FVSM_Controlnet.pt": "FVSM/FloVD_FVSM_Controlnet.pt",
-    "ckpt/OMSM/selected_blocks.safetensors": "OMSM/selected_blocks.safetensors",
-    "ckpt/OMSM/pytorch_lora_weights.safetensors": "OMSM/pytorch_lora_weights.safetensors",
-    "ckpt/others/depth_anything_v2_metric_hypersim_vitb.pth": "others/depth_anything_v2_metric_hypersim_vitb.pth"
-}
-
-print("\nDownloading model...", flush=True)
-
-def download_weights():
-    print("🔄 Downloading model weights via huggingface_hub...")
-    for hf_path, local_rel_path in WEIGHT_FILES.items():
-        local_path = Path("ckpt") / local_rel_path
-        if not local_path.exists():
-            print(f"📥 Downloading {hf_path}")
-            hf_hub_download(
-                repo_id=HF_DATASET_REPO,
-                repo_type="dataset",
-                filename=hf_path,
-                local_dir="./"
-            )
-        else:
-            print(f"✅ Already exists: {local_path}")
-
-download_weights()
-
-def print_ckpt_structure(base_path="ckpt"):
-    print(f"📂 Listing structure of: {base_path}", flush=True)
-    for root, dirs, files in os.walk(base_path):
-        level = root.replace(base_path, '').count(os.sep)
-        indent = ' ' * 2 * level
-        print(f"{indent}📁 {os.path.basename(root)}/", flush=True)
-        sub_indent = ' ' * 2 * (level + 1)
-        for f in files:
-            print(f"{sub_indent}📄 {f}", flush=True)
-
-print_ckpt_structure()
-
-# =========================================
-# 2. Import FloVD generation pipeline
-# =========================================
-
-from inference.flovd_demo import generate_video
-
-@spaces.GPU
-def run_inference(prompt, image, pose_type, speed, use_flow_integration, cam_pose_name):
-    log_buffer = io.StringIO()
-    sys_stdout = sys.stdout
-    sys.stdout = log_buffer
-
-    video_path = None
-    try:
-        print("🚀 Starting inference...", flush=True)
-        os.makedirs("input_images", exist_ok=True)
-        image_path = "input_images/input_image.png"
-
-        if not isinstance(image, Image.Image):
-            image = Image.fromarray(image.astype("uint8"))
-
-        image.save(image_path)
-        print(f"📸 Saved input image to {image_path}", flush=True)
-
-        generate_video(
-            prompt=prompt,
-            image_path=image_path,
-            fvsm_path="./ckpt/FVSM/FloVD_FVSM_Controlnet.pt",
-            omsm_path="./ckpt/OMSM",
-            output_path="./outputs",
-            num_frames=49,
-            fps=16,
-            width=None,
-            height=None,
-            seed=42,
-            guidance_scale=6.0,
-            dtype=torch.float16,
-            controlnet_guidance_end=0.4,
-            use_dynamic_cfg=False,
-            pose_type=pose_type,
-            speed=float(speed),
-            use_flow_integration=use_flow_integration,
-            cam_pose_name=cam_pose_name,
-            depth_ckpt_path="./ckpt/others/depth_anything_v2_metric_hypersim_vitb.pth"
-        )
-
-        video_name = f"{prompt[:30].strip().replace(' ', '_')}_{cam_pose_name or 'default'}.mp4"
-        video_path = f"./outputs/generated_videos/{video_name}"
-        print(f"✅ Inference complete. Video saved to {video_path}")
-
-    except Exception:
-        print("🔥 Inference failed with exception:")
-        traceback.print_exc()
-
-    sys.stdout = sys_stdout
-    logs = log_buffer.getvalue()
-    log_buffer.close()
-
-    return (video_path if video_path and os.path.exists(video_path) else None), logs
-
-
-# =========================================
-# 3. Define FloVD Gradio Interface
-# =========================================
-with gr.Blocks() as video_tab:
-    gr.Markdown("## 🎥 FloVD: Optical Flow + CogVideoX Video Generation")
-
-    prompt = gr.Textbox(label="Prompt", value="A girl riding a bicycle through a park.")
-    image = gr.Image(label="Input Image")
-    pose_type = gr.Radio(choices=["manual", "re10k"], value="manual", label="Camera Pose Type")
-    speed = gr.Slider(minimum=0.1, maximum=2.0, step=0.1, value=0.5, label="Camera Speed")
-    use_flow_integration = gr.Checkbox(label="Use Flow Integration", value=False)
-    cam_pose_name = gr.Textbox(label="Camera Trajectory", placeholder="e.g., zoom_in, custom_motion, etc.", lines=1)
-
-    generate_btn = gr.Button("🎬 Generate Video")
-
-    video_output = gr.Video(label="Generated Video")
-    log_output = gr.Textbox(label="Logs", lines=20, interactive=False)
-
-    generate_btn.click(
-        fn=run_inference,
-        inputs=[prompt, image, pose_type, speed, use_flow_integration, cam_pose_name],
-        outputs=[video_output, log_output]
-    )
-
-# =========================================
-# 4. Live System Monitor (Fixed)
-# =========================================
-
-def get_system_stats():
-    cpu = psutil.cpu_percent()
-    mem = psutil.virtual_memory()
-    disk = psutil.disk_usage('/')
-    try:
-        gpus = GPUtil.getGPUs()
-        gpu_info = "\n".join([
-            f"GPU {i}: {gpu.name}, {gpu.memoryUsed}MB / {gpu.memoryTotal}MB, Util: {gpu.load * 100:.1f}%"
-            for i, gpu in enumerate(gpus)
-        ]) if gpus else "No GPU detected"
-    except Exception as e:
-        gpu_info = f"GPU info error: {e}"
-
-    return (
-        f"🧠 CPU Usage: {cpu}%\n"
-        f"💾 RAM: {mem.used / 1e9:.2f} GB / {mem.total / 1e9:.2f} GB ({mem.percent}%)\n"
-        f"🗄️ Disk: {disk.used / 1e9:.2f} GB / {disk.total / 1e9:.2f} GB ({disk.percent}%)\n"
-        f"🎮 {gpu_info}"
-    )
-
-with gr.Blocks() as monitor_tab:
-    gr.Markdown("## 📊 Live System Resource Monitor")
-    stats_box = gr.Textbox(label="Live Stats", lines=10, interactive=False)
-
-    def update_stats():
-        return gr.update(value=get_system_stats())
-
-    stats_btn = gr.Button("🔄 Refresh Stats")
-    stats_btn.click(fn=update_stats, outputs=stats_box)
-
-# =========================================
-# 5. Combine Tabs: FloVD + Monitor
-# =========================================
-
-with gr.Blocks() as app:
-    with gr.Tab("🎥 Video Generator"):
-        video_tab.render()
-    with gr.Tab("📊 System Monitor"):
-        monitor_tab.render()
-
-# =========================================
-# 6. Launch App
-# =========================================
-
-if __name__ == "__main__":
-    app.launch(server_name="0.0.0.0", server_port=7860, debug=True, show_error=True)