feat: complete sequential CLI workflow with questionary and rich

src/faceblur/app.py

@@ -45,3 +45,183 @@ def open_directory(path: Path) -> None:
             subprocess.run(["xdg-open", str(path)], check=False)
     except Exception as e:
         console.print(f"[yellow]Could not automatically open directory: {e}[/yellow]")
+
+def run() -> None:
+    """Main CLI entry point."""
+    console.print(Panel.fit("[bold blue]PyFaceBlur[/bold blue]", border_style="blue"))
+
+    # 1. Input gathering
+    video_str = questionary.path(
+        "Enter path to video file:",
+        validate=lambda p: Path(p).is_file() or "File does not exist",
+    ).ask()
+
+    if not video_str:
+        return
+
+    video_path = Path(video_str)
+
+    interval_str = questionary.text(
+        "Frame interval for face detection (default: 30):",
+        default="30",
+        validate=lambda text: text.isdigit() and int(text) > 0 or "Must be a positive integer",
+    ).ask()
+
+    if not interval_str:
+        return
+
+    interval = int(interval_str)
+    temp_dir = tempfile.mkdtemp(prefix="pyfaceblur_")
+
+    try:
+        # 2. Processing (Extraction & Detection)
+        frames_dir = str(Path(temp_dir) / "frames")
+        
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            BarColumn(),
+            TaskProgressColumn(),
+            TimeElapsedColumn(),
+            console=console,
+        ) as progress:
+            
+            task_extract = progress.add_task("[cyan]Extracting frames...", total=None)
+            frames = extract_frames(str(video_path), frames_dir, interval)
+            progress.update(task_extract, completed=100, total=100, description="[green]Frames extracted")
+
+            if not frames:
+                console.print("[red]Error: No frames extracted.[/red]")
+                return
+
+            task_detect = progress.add_task("[cyan]Detecting faces...", total=len(frames))
+            detector = FaceDetector()
+            all_faces = []
+            
+            for i, frame in enumerate(frames):
+                try:
+                    faces = detector.detect_faces(frame.path, frame.index)
+                    all_faces.extend(faces)
+                except Exception:
+                    pass
+                progress.update(task_detect, advance=1, description=f"[cyan]Detecting faces ({len(all_faces)} found)...")
+            
+            detector.close()
+            progress.update(task_detect, description="[green]Detection complete")
+
+            if not all_faces:
+                console.print("[yellow]No faces detected in the video.[/yellow]")
+                return
+
+            task_cluster = progress.add_task("[cyan]Clustering faces...", total=None)
+            clusters = cluster_faces(all_faces)
+            real_clusters = [c for c in clusters if c.id >= 0]
+            progress.update(task_cluster, completed=100, total=100, description=f"[green]Found {len(real_clusters)} people")
+
+        # 3. Face Selection
+        samples_dir = Path(temp_dir) / "face_samples"
+        samples_dir.mkdir(exist_ok=True)
+        face_choices = []
+
+        for cluster in real_clusters:
+            best_face = max(cluster.faces, key=lambda f: f.confidence)
+            image = cv2.imread(str(best_face.frame_path))
+            if image is not None:
+                x1, y1, x2, y2 = best_face.bbox
+                crop = image[y1:y2, x1:x2]
+                if crop.size > 0:
+                    sample_path = samples_dir / f"person_{cluster.id + 1:02d}.jpg"
+                    cv2.imwrite(str(sample_path), crop)
+                    
+            face_choices.append(
+                questionary.Choice(
+                    title=f"Person {cluster.id + 1} ({len(cluster.faces)} detections)",
+                    value=cluster.id,
+                    checked=True,
+                )
+            )
+
+        console.print("\n[bold]Face Selection[/bold]")
+        console.print(f"Face sample images have been saved to: [blue]{samples_dir}[/blue]")
+        open_directory(samples_dir)
+        console.print("Please review the images, then select who to blur in the terminal.")
+
+        if not face_choices:
+            console.print("[yellow]No valid face clusters found to select.[/yellow]")
+            return
+
+        selected_cluster_ids = questionary.checkbox(
+            "Select faces to blur (Space to toggle, Enter to confirm):",
+            choices=face_choices,
+        ).ask()
+
+        if selected_cluster_ids is None:
+            return  # User cancelled
+
+        # Always include noise cluster (-1) if it exists
+        selected_ids_set = set(selected_cluster_ids)
+        for cluster in clusters:
+            if cluster.id == -1:
+                selected_ids_set.add(-1)
+
+        blur_method = questionary.select(
+            "Select blur method:",
+            choices=["gaussian", "pixelate", "blackout", "elliptical", "median"],
+            default="gaussian"
+        ).ask()
+
+        if not blur_method:
+            return
+
+        # 4. Encoding
+        console.print("\n[bold]Encoding Video[/bold]")
+        
+        # Probe early to get total frames for progress bar
+        best_enc = find_best_encoder()
+        encoder_name = best_enc[0]
+        console.print(f"Using hardware/software encoder: [cyan]{encoder_name}[/cyan]")
+        
+        stem = video_path.stem
+        suffix = video_path.suffix
+        output_path = video_path.parent / f"{stem}_blurred{suffix}"
+
+        # We will track progress via a callback
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            BarColumn(),
+            TaskProgressColumn(),
+            TimeElapsedColumn(),
+            console=console,
+        ) as progress:
+            
+            encode_task = progress.add_task("[cyan]Encoding...", total=100)
+
+            def on_progress(current: int, total: int) -> None:
+                if total > 0:
+                    progress.update(encode_task, total=total, completed=current)
+
+            try:
+                encode_video(
+                    input_path=video_path,
+                    output_path=output_path,
+                    clusters=clusters,
+                    selected_cluster_ids=selected_ids_set,
+                    frame_interval=interval,
+                    blur_method=blur_method,
+                    progress_callback=on_progress,
+                    encoder_override=best_enc,
+                )
+                progress.update(encode_task, description="[green]Encoding complete!")
+            except Exception as e:
+                console.print(f"[red]Encoding failed: {e}[/red]")
+                return
+
+        console.print(f"\n[bold green]Done![/bold green] Saved to: [blue]{output_path}[/blue]")
+
+    finally:
+        shutil.rmtree(temp_dir, ignore_errors=True)
+
+
+if __name__ == "__main__":
+    run()