Update app.py

app.py

@@ -1,5 +1,5 @@
 """
-ShortSmith v2 - Gradio Application (Simplified)
+ShortSmith v2 - Gradio Application
 
 Hugging Face Space interface for video highlight extraction.
 """
@@ -10,19 +10,26 @@ import tempfile
 import shutil
 from pathlib import Path
 import time
+import traceback
 
 import gradio as gr
 
 # Add project root to path
 sys.path.insert(0, str(Path(__file__).parent))
 
-# Initialize logging first
-from utils.logger import setup_logging, get_logger
-setup_logging(log_level="INFO", log_to_console=True)
-logger = get_logger("app")
+# Initialize logging
+try:
+    from utils.logger import setup_logging, get_logger
+    setup_logging(log_level="INFO", log_to_console=True)
+    logger = get_logger("app")
+except Exception as e:
+    import logging
+    logging.basicConfig(level=logging.INFO)
+    logger = logging.getLogger("app")
+    logger.warning(f"Custom logger failed, using basic: {e}")
 
 
-def process_video(video_file, api_key, domain, num_clips, clip_duration, reference_image, custom_prompt):
+def process_video(video_file, domain, num_clips, clip_duration, reference_image, custom_prompt):
     """Main processing function."""
 
     log_messages = []
@@ -36,18 +43,18 @@ def process_video(video_file, api_key, domain, num_clips, clip_duration, referen
         log("Validating inputs...")
 
         if not video_file:
-            return [], "❌ Error: No video file provided", "\n".join(log_messages)
+            return None, "❌ Error: No video file provided", "\n".join(log_messages)
 
         video_path = Path(video_file)
         log(f"Video: {video_path.name}")
 
-        # Import here to avoid schema issues at startup
+        # Import here to avoid issues at startup
         from utils.helpers import validate_video_file, validate_image_file, format_duration
         from pipeline.orchestrator import PipelineOrchestrator
 
         validation = validate_video_file(video_file)
         if not validation.is_valid:
-            return [], f"❌ Error: {validation.error_message}", "\n".join(log_messages)
+            return None, f"❌ Error: {validation.error_message}", "\n".join(log_messages)
 
         log(f"Video size: {validation.file_size / (1024*1024):.1f} MB")
 
@@ -91,7 +98,6 @@ def process_video(video_file, api_key, domain, num_clips, clip_duration, referen
             domain=domain_value,
             reference_image=ref_path,
             custom_prompt=custom_prompt if custom_prompt and custom_prompt.strip() else None,
-            api_key=api_key if api_key and api_key.strip() else None,
         )
 
         # Handle result
@@ -109,111 +115,79 @@ def process_video(video_file, api_key, domain, num_clips, clip_duration, referen
             status = f"✅ Successfully extracted {len(clip_paths)} highlight clips!\nProcessing time: {result.processing_time:.1f}s"
 
             pipeline.cleanup()
-            return clip_paths, status, "\n".join(log_messages)
+            return clip_paths if clip_paths else None, status, "\n".join(log_messages)
         else:
             log(f"❌ Processing failed: {result.error_message}")
             pipeline.cleanup()
-            return [], f"❌ Error: {result.error_message}", "\n".join(log_messages)
+            return None, f"❌ Error: {result.error_message}", "\n".join(log_messages)
 
     except Exception as e:
         error_msg = f"Unexpected error: {str(e)}"
         log(f"❌ {error_msg}")
-        logger.exception("Pipeline error")
-        return [], f"❌ {error_msg}", "\n".join(log_messages)
+        log(traceback.format_exc())
+        return None, f"❌ {error_msg}", "\n".join(log_messages)
 
 
-def create_interface():
-    """Create the Gradio interface."""
+# Create interface
+with gr.Blocks(title="ShortSmith v2", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🎬 ShortSmith v2
+    ### AI-Powered Video Highlight Extractor
 
-    domains = ["Sports", "Vlogs", "Music Videos", "Podcasts", "Gaming", "General"]
+    Upload a video and extract the most engaging highlight clips automatically.
+    """)
 
-    with gr.Blocks(
-        title="ShortSmith v2",
-        theme=gr.themes.Soft(),
-    ) as demo:
-        gr.Markdown("""
-        # 🎬 ShortSmith v2
-        ### AI-Powered Video Highlight Extractor
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📤 Input")
 
-        Upload a video and extract the most engaging highlight clips automatically.
-        """)
+            video_input = gr.Video(label="Upload Video")
 
-        with gr.Row():
-            with gr.Column(scale=1):
-                gr.Markdown("### 📤 Input")
+            domain_dropdown = gr.Dropdown(
+                choices=["Sports", "Vlogs", "Music Videos", "Podcasts", "Gaming", "General"],
+                value="General",
+                label="Content Domain",
+            )
 
-                video_input = gr.Video(label="Upload Video")
+            with gr.Row():
+                num_clips_slider = gr.Slider(
+                    minimum=1, maximum=10, value=3, step=1,
+                    label="Number of Clips",
+                )
+                duration_slider = gr.Slider(
+                    minimum=5, maximum=30, value=15, step=1,
+                    label="Clip Duration (seconds)",
+                )
 
-                with gr.Accordion("⚙️ Settings", open=True):
-                    domain_dropdown = gr.Dropdown(
-                        choices=domains,
-                        value="General",
-                        label="Content Domain",
-                    )
+            with gr.Accordion("👤 Person Filtering (Optional)", open=False):
+                reference_image = gr.Image(label="Reference Image", type="filepath")
 
-                    with gr.Row():
-                        num_clips_slider = gr.Slider(
-                            minimum=1, maximum=10, value=3, step=1,
-                            label="Number of Clips",
-                        )
-                        duration_slider = gr.Slider(
-                            minimum=5, maximum=30, value=15, step=1,
-                            label="Clip Duration (seconds)",
-                        )
+            with gr.Accordion("📝 Custom Instructions (Optional)", open=False):
+                custom_prompt = gr.Textbox(
+                    label="Additional Instructions",
+                    placeholder="E.g., 'Focus on crowd reactions'",
+                    lines=2,
+                )
 
-                with gr.Accordion("👤 Person Filtering (Optional)", open=False):
-                    reference_image = gr.Image(label="Reference Image", type="filepath")
+            process_btn = gr.Button("🚀 Extract Highlights", variant="primary", size="lg")
 
-                with gr.Accordion("📝 Custom Instructions (Optional)", open=False):
-                    custom_prompt = gr.Textbox(
-                        label="Additional Instructions",
-                        placeholder="E.g., 'Focus on crowd reactions'",
-                        lines=2,
-                    )
+        with gr.Column(scale=1):
+            gr.Markdown("### 📥 Output")
 
-                process_btn = gr.Button("🚀 Extract Highlights", variant="primary", size="lg")
+            status_output = gr.Textbox(label="Status", lines=3, interactive=False)
+            clip_files = gr.Files(label="Extracted Clips")
 
-                # Hidden API key input (for future use)
-                api_key_input = gr.Textbox(visible=False, value="")
+            with gr.Accordion("📋 Processing Log", open=False):
+                log_output = gr.Textbox(label="Log", lines=10, interactive=False)
 
-            with gr.Column(scale=1):
-                gr.Markdown("### 📥 Output")
+    gr.Markdown("---\n**ShortSmith v2** | Powered by Qwen2-VL, InsightFace, and Librosa")
 
-                status_output = gr.Textbox(label="Status", lines=3, interactive=False)
-
-                # Output clips as downloadable files
-                clip_files = gr.Files(label="Extracted Clips")
-
-                with gr.Accordion("📋 Processing Log", open=False):
-                    log_output = gr.Textbox(label="Log", lines=10, interactive=False)
-
-        gr.Markdown("---\n**ShortSmith v2** | Powered by Qwen2-VL, InsightFace, and Librosa")
-
-        # Event handler
-        def on_process(video, api_key, domain, num_clips, duration, ref_img, prompt):
-            clips, status, logs = process_video(video, api_key, domain, num_clips, duration, ref_img, prompt)
-            return status, clips, logs
-
-        process_btn.click(
-            fn=on_process,
-            inputs=[video_input, api_key_input, domain_dropdown, num_clips_slider, duration_slider, reference_image, custom_prompt],
-            outputs=[status_output, clip_files, log_output],
-        )
-
-    return demo
-
-
-def main():
-    """Main entry point."""
-    logger.info("Starting ShortSmith v2 Gradio interface...")
-
-    demo = create_interface()
-    demo.queue()
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
+    process_btn.click(
+        fn=process_video,
+        inputs=[video_input, domain_dropdown, num_clips_slider, duration_slider, reference_image, custom_prompt],
+        outputs=[clip_files, status_output, log_output],
     )
 
-
 if __name__ == "__main__":
-    main()
+    demo.queue()
+    demo.launch(server_name="0.0.0.0", server_port=7860)