Upload app.py

app.py

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+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)