Update app.py

app.py

@@ -1,3 +1,11 @@
+<!-- FULL UPDATED CODE - Copy and replace your entire script with this -->
+<!DOCTYPE html>
+<html>
+<head>
+    <title>RIFE + Boomerang + Enhanced Stitch</title>
+</head>
+<body>
+<pre><code>
 import spaces
 import os
 import shutil
@@ -7,6 +15,7 @@ import importlib.util
 import sys
 import numpy as np
 import gradio as gr
+import time  # for any fallback timing
 
 # --- Constants ---
 BASE_DIR = os.getcwd()
@@ -20,6 +29,33 @@ if not hasattr(np, 'int'):
     np.int = int
 print("Applied global NumPy compatibility patch for scikit-video.")
 
+# --- NEW: Thumbnail helper (used by stitching tab) ---
+def create_thumbnails(video_paths):
+    """Extract first frame thumbnail for each video + show position number"""
+    if not video_paths:
+        return []
+    temp_dir = os.path.join(BASE_DIR, "temp_thumbs")
+    os.makedirs(temp_dir, exist_ok=True)
+    
+    gallery_items = []
+    for i, vid_path in enumerate(video_paths):
+        thumb_name = f"thumb_{i:03d}_{os.path.basename(vid_path)}.jpg"
+        thumb_path = os.path.join(temp_dir, thumb_name)
+        
+        cmd = ['ffmpeg', '-i', vid_path, '-ss', '00:00:00.5', '-vframes', '1',
+               '-vf', 'scale=280:-1', '-y', thumb_path]
+        try:
+            subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, timeout=8)
+            if os.path.exists(thumb_path):
+                display_text = f"{i+1}. {os.path.basename(vid_path)}"
+                gallery_items.append((thumb_path, display_text))
+            else:
+                gallery_items.append((None, f"{i+1}. {os.path.basename(vid_path)}"))
+        except Exception as e:
+            print(f"Thumbnail failed for {vid_path}: {e}")
+            gallery_items.append((None, f"{i+1}. {os.path.basename(vid_path)}"))
+    return gallery_items
+
 # --- Setup Functions ---
 def run_command(command):
     subprocess.run(command, shell=True, check=True)
@@ -28,7 +64,6 @@ def patch_skvideo_fully():
     """Full recursive patch for ALL skvideo files (fixes np.int / np.float everywhere)"""
     print("Applying FULL NumPy compatibility patch to entire skvideo package...")
     try:
-        # Find skvideo install location
         skvideo_dir = subprocess.check_output(
             [sys.executable, "-c", "import skvideo; print(skvideo.__path__[0])"],
             text=True
@@ -52,24 +87,30 @@ def patch_skvideo_fully():
         print(f"⚠️ skvideo patch failed: {e}")
 
 def setup_environment():
-    """Full setup with complete skvideo fix"""
+    """Full setup with complete skvideo fix + thumbnail cleanup"""
     print("--- Starting Environment Setup ---")
     
-    # 1. Install dependencies
+    # 1. Clean old thumbnails (keeps disk clean)
+    thumb_dir = os.path.join(BASE_DIR, "temp_thumbs")
+    if os.path.exists(thumb_dir):
+        shutil.rmtree(thumb_dir, ignore_errors=True)
+    os.makedirs(thumb_dir, exist_ok=True)
+    
+    # 2. Install dependencies
     print("Installing scikit-video, imageio[ffmpeg], and RIFE requirements...")
     subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", 
                          "scikit-video", "imageio[ffmpeg]", "tqdm", "opencv-python>=4.1.2", 
                          "moviepy>=1.0.3", "torchvision"])
 
-    # 2. FULL skvideo patch (this is the key fix for your error)
+    # 3. FULL skvideo patch
     patch_skvideo_fully()
 
-    # 3. Clone RIFE
+    # 4. Clone RIFE
     if not os.path.exists(RIFE_DIR):
         print("Cloning Practical-RIFE...")
         run_command(f"git clone https://github.com/hzwer/Practical-RIFE {RIFE_DIR}")
 
-    # 4. Download + fix model (unchanged)
+    # 5. Download + fix model
     hdv3_path = os.path.join(RIFE_DIR, "HDv3")
     if not os.path.exists(hdv3_path) or not os.path.exists(os.path.join(RIFE_DIR, "train_log", "RIFE_HDv3.py")):
         print("Downloading RIFE v4.26 model weights...")
@@ -97,7 +138,7 @@ def setup_environment():
         if os.path.exists(zip_path):
             os.remove(zip_path)
 
-    # 5. Patch RIFE for high-quality output
+    # 6. Patch RIFE for high-quality output
     inference_script = os.path.join(RIFE_DIR, "inference_video.py")
     if os.path.exists(inference_script):
         with open(inference_script, 'r') as f:
@@ -111,7 +152,7 @@ def setup_environment():
                 f.write(new_content)
         print("✅ RIFE patched for libx264")
 
-    # 6. Verification
+    # 7. Verification
     if os.path.exists(os.path.join(RIFE_DIR, "train_log", "RIFE_HDv3.py")) and os.path.exists(os.path.join(hdv3_path, "flownet.pkl")):
         print("✅ Model files verified")
     else:
@@ -152,7 +193,7 @@ def create_boomerang_loop(input_path, output_path, fps):
         print(f"❌ Boomerang Failed: {e}")
         return False
 
-# --- Processing Function (with full error logging) ---
+# --- Processing Function (unchanged) ---
 @spaces.GPU(required=True)
 def interpolate_video(input_video_path, multi_factor, create_boomerang=False):
     if input_video_path is None:
@@ -163,13 +204,11 @@ def interpolate_video(input_video_path, multi_factor, create_boomerang=False):
     final_output_path = os.path.join(BASE_DIR, "final_interpolated.mp4")
     boomerang_path = os.path.join(BASE_DIR, "final_boomerang.mp4")
 
-    # Clean previous files
     for p in [output_path, final_output_path, boomerang_path]:
         if os.path.exists(p): os.remove(p)
     expected_no_audio = output_path.replace(".mp4", "_noaudio.mp4")
     if os.path.exists(expected_no_audio): os.remove(expected_no_audio)
 
-    # Torch diagnostics
     import torch
     print(f"🔥 CUDA available: {torch.cuda.is_available()}")
     if torch.cuda.is_available():
@@ -217,7 +256,7 @@ def interpolate_video(input_video_path, multi_factor, create_boomerang=False):
     finally:
         os.chdir(BASE_DIR)
 
-# --- Stitch function (unchanged) ---
+# --- Stitch function (unchanged - now receives ordered list of paths) ---
 def stitch_videos(video_files, resolution_choice):
     if not video_files: return None
     try:
@@ -226,7 +265,7 @@ def stitch_videos(video_files, resolution_choice):
     except:
         target_w, target_h = "1920", "1080"
 
-    print(f"Stitching {len(video_files)} videos into {target_w}x{target_h}...")
+    print(f"Stitching {len(video_files)} videos (in your chosen order) into {target_w}x{target_h}...")
     stitch_list_path = os.path.join(BASE_DIR, "stitch_list.txt")
     output_stitched = os.path.join(BASE_DIR, "final_stitched.mp4")
     temp_dir = os.path.join(BASE_DIR, "temp_stitch")
@@ -249,10 +288,55 @@ def stitch_videos(video_files, resolution_choice):
     shutil.rmtree(temp_dir)
     return output_stitched
 
+# --- Reordering helper functions (NEW) ---
+def handle_upload(files):
+    """Convert uploaded files to ordered paths + generate thumbnails"""
+    if not files:
+        return [], [], 0
+    # Gradio File objects have .name = temp path
+    paths = [f.name if hasattr(f, "name") else str(f) for f in files]
+    thumbs = create_thumbnails(paths)
+    return paths, thumbs, 0
+
+def reorder_clips(paths, idx, direction):
+    """Move clip up or down"""
+    if not paths:
+        return paths, [], 0
+    idx = int(idx)
+    if idx < 0 or idx >= len(paths):
+        return paths, create_thumbnails(paths), idx
+    
+    new_paths = paths.copy()
+    if direction == "up" and idx > 0:
+        new_paths[idx], new_paths[idx-1] = new_paths[idx-1], new_paths[idx]
+        new_idx = idx - 1
+    elif direction == "down" and idx < len(new_paths)-1:
+        new_paths[idx], new_paths[idx+1] = new_paths[idx+1], new_paths[idx]
+        new_idx = idx + 1
+    else:
+        new_idx = idx
+    
+    return new_paths, create_thumbnails(new_paths), new_idx
+
+def remove_selected(paths, idx):
+    """Remove selected clip"""
+    if not paths:
+        return [], [], 0
+    idx = int(idx)
+    if 0 <= idx < len(paths):
+        new_paths = paths.copy()
+        del new_paths[idx]
+        new_idx = max(0, min(idx, len(new_paths)-1))
+        return new_paths, create_thumbnails(new_paths), new_idx
+    return paths, create_thumbnails(paths), idx
+
+def clear_all():
+    return [], [], 0
+
 # --- Gradio UI ---
-with gr.Blocks(title="RIFE + Boomerang") as demo:
-    gr.Markdown("# 🎞️ RIFE: Interpolate & Boomerang Loop")
-    gr.Markdown("**✅ Fixed skvideo np.int error** – now works on latest NumPy!")
+with gr.Blocks(title="RIFE + Boomerang + Enhanced Stitch") as demo:
+    gr.Markdown("# 🎞️ RIFE: Interpolate & Boomerang + Smart Stitch")
+    gr.Markdown("**✅ Fixed skvideo np.int error** – now works on latest NumPy!  \n**New:** Stitch tab now shows **thumbnails** + **reorder with arrows**!")
     
     with gr.Tabs():
         with gr.TabItem("1. Smooth Video + Boomerang"):
@@ -268,14 +352,79 @@ with gr.Blocks(title="RIFE + Boomerang") as demo:
             interp_btn.click(interpolate_video, inputs=[video_input, multi_select, boomerang_check], outputs=video_output)
 
         with gr.TabItem("2. Stitch Videos"):
-            gr.Markdown("Upload multiple videos → normalized to 60fps")
+            gr.Markdown("### 📋 Upload clips → see thumbnails → **reorder** → stitch")
+            
+            stitch_inputs = gr.File(
+                label="Upload Video Clips (multiple)",
+                file_count="multiple",
+                file_types=["video"],
+                height=100
+            )
+            
+            clip_paths_state = gr.State([])  # Ordered list of full video paths
+            
+            gr.Markdown("**Current Order (thumbnails show position)**")
+            clip_gallery = gr.Gallery(
+                label="Thumbnails - Click one then use arrow buttons below",
+                columns=4,
+                height=320,
+                object_fit="contain"
+            )
+            
             with gr.Row():
-                with gr.Column():
-                    stitch_inputs = gr.File(label="Upload Clips", file_count="multiple")
-                    res_select = gr.Dropdown(choices=["1920x1080", "1280x1280", "1024x1024"], value="1920x1080", label="Resolution")
-                    stitch_btn = gr.Button("Stitch Videos", variant="primary")
-                with gr.Column():
-                    stitch_output = gr.Video(label="Stitched Result")
-            stitch_btn.click(stitch_videos, inputs=[stitch_inputs, res_select], outputs=stitch_output)
+                selected_index = gr.Number(label="Selected Index (0 = first clip)", value=0, precision=0, minimum=0)
+            
+            with gr.Row():
+                up_btn = gr.Button("↑ Move Up", size="small")
+                down_btn = gr.Button("↓ Move Down", size="small")
+                remove_btn = gr.Button("🗑️ Remove", variant="stop", size="small")
+                clear_btn = gr.Button("Clear All", variant="stop", size="small")
+            
+            res_select = gr.Dropdown(
+                choices=["1920x1080", "1280x1280", "1024x1024"], 
+                value="1920x1080", 
+                label="Output Resolution"
+            )
+            stitch_btn = gr.Button("🎬 Stitch Videos in Shown Order", variant="primary")
+            stitch_output = gr.Video(label="Stitched Result")
+
+# --- Connect all stitching events (NEW) ---
+stitch_inputs.change(
+    fn=handle_upload,
+    inputs=stitch_inputs,
+    outputs=[clip_paths_state, clip_gallery, selected_index]
+)
+
+up_btn.click(
+    fn=lambda p, i: reorder_clips(p, i, "up"),
+    inputs=[clip_paths_state, selected_index],
+    outputs=[clip_paths_state, clip_gallery, selected_index]
+)
+
+down_btn.click(
+    fn=lambda p, i: reorder_clips(p, i, "down"),
+    inputs=[clip_paths_state, selected_index],
+    outputs=[clip_paths_state, clip_gallery, selected_index]
+)
+
+remove_btn.click(
+    fn=remove_selected,
+    inputs=[clip_paths_state, selected_index],
+    outputs=[clip_paths_state, clip_gallery, selected_index]
+)
+
+clear_btn.click(
+    fn=clear_all,
+    outputs=[clip_paths_state, clip_gallery, selected_index]
+)
+
+stitch_btn.click(
+    fn=stitch_videos,
+    inputs=[clip_paths_state, res_select],
+    outputs=stitch_output
+)
 
-demo.launch()
+demo.launch()
+</code></pre>
+</body>
+</html>