Create app.py

app.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+import os
+import sys
+import subprocess
+import gradio as gr
+import torch
+
+sys.path.insert(0, '/app/FunGen')
+
+def process_video(video, mode, overwrite, autotune):
+    """Process video with FunGen"""
+    print(f"[DEBUG] Called with video={video}, mode={mode}, overwrite={overwrite}, autotune={autotune}")
+    
+    if video is None:
+        return "❌ No video uploaded", None
+    
+    try:
+        input_path = video
+        print(f"[DEBUG] Input path: {input_path}")
+        
+        if not os.path.exists(input_path):
+            return f"❌ File not found: {input_path}", None
+        
+        file_size_mb = os.path.getsize(input_path) / (1024**2)
+        status = f"🚀 Processing: {os.path.basename(input_path)}\n"
+        status += f"📁 Size: {file_size_mb:.1f} MB\n"
+        status += f"⚙️  Mode: {mode}\n"
+        status += f"⏳ Starting...\n\n"
+        
+        print(f"[DEBUG] Status: {status}")
+        
+        # Build command without fake flags
+        cmd = ["python", "/app/FunGen/main.py", input_path, "--mode", mode]
+        if overwrite:
+            cmd.append("--overwrite")
+        if not autotune:
+            cmd.append("--no-autotune")
+        
+        print(f"[DEBUG] Command: {' '.join(cmd)}")
+        
+        # Set environment
+        env = os.environ.copy()
+        
+        result = subprocess.run(cmd, cwd="/app/FunGen", capture_output=True, text=True, timeout=3600, env=env)
+        
+        print(f"[DEBUG] Return code: {result.returncode}")
+        print(f"[DEBUG] STDOUT:\n{result.stdout}")
+        print(f"[DEBUG] STDERR:\n{result.stderr}")
+        
+        # Look for output - FunGen saves relative to input or in current directory
+        output_file = None
+        search_paths = [
+            os.path.dirname(input_path),  # Same folder as input
+            "/app/FunGen",
+            "/tmp/outputs",
+            os.getcwd()
+        ]
+        
+        for search_path in search_paths:
+            if os.path.exists(search_path):
+                print(f"[DEBUG] Searching: {search_path}")
+                for root, dirs, files in os.walk(search_path):
+                    for f in files:
+                        print(f"[DEBUG] Found file: {os.path.join(root, f)}")
+                        if f.endswith(".funscript") and not f.endswith(".roll.funscript"):
+                            output_file = os.path.join(root, f)
+                            print(f"[DEBUG] ✓ Matched funscript: {output_file}")
+                            break
+                    if output_file:
+                        break
+            if output_file:
+                break
+        
+        if output_file and os.path.exists(output_file):
+            status += f"✅ Complete!\n📜 {os.path.basename(output_file)}"
+            print(f"[DEBUG] Returning: {output_file}")
+            return status, output_file
+        
+        # Not found - show the full output for debugging
+        status += f"⚠️ No output generated\n\nFull Output:\n{result.stdout}\n\nErrors:\n{result.stderr}"
+        return status, None
+    
+    except Exception as e:
+        error = f"❌ Exception: {str(e)}"
+        print(f"[DEBUG] Exception: {error}")
+        import traceback
+        print(traceback.format_exc())
+        return error, None
+
+with gr.Blocks(title="FunGen") as demo:
+    gr.Markdown("# 🎬 FunGen - Funscript Generator")
+    
+    with gr.Row():
+        gpu_info = "✅ GPU Available" if torch.cuda.is_available() else "❌ No GPU"
+        gr.Textbox(value=gpu_info, label="Status", interactive=False)
+    
+    with gr.Row():
+        with gr.Column():
+            video_input = gr.File(label="Upload Video", file_types=["video"], type="filepath")
+            mode_input = gr.Dropdown(
+                ["Hybrid Intelligence Tracker", "Oscillation Detector (Legacy)", "YOLO ROI Tracker"],
+                value="Hybrid Intelligence Tracker",
+                label="Mode"
+            )
+            overwrite_input = gr.Checkbox(label="Overwrite", value=False)
+            autotune_input = gr.Checkbox(label="Apply Autotune", value=True)
+            process_btn = gr.Button("Process Video", variant="primary")
+        
+        with gr.Column():
+            status_output = gr.Textbox(label="Status", lines=10, interactive=False)
+            file_output = gr.File(label="Download", interactive=False)
+    
+    process_btn.click(
+        fn=process_video,
+        inputs=[video_input, mode_input, overwrite_input, autotune_input],
+        outputs=[status_output, file_output]
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch(server_name="0.0.0.0", server_port=7860, show_error=True)