Update app.py

app.py

@@ -22,16 +22,18 @@ 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:
+except Exception:
     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, domain, num_clips, clip_duration, reference_image, custom_prompt):
     """Main processing function."""
 
+    if video_file is None:
+        return None, "Please upload a video first.", ""
+
     log_messages = []
 
     def log(msg):
@@ -39,14 +41,8 @@ def process_video(video_file, domain, num_clips, clip_duration, reference_image,
         logger.info(msg)
 
     try:
-        # Validate inputs
-        log("Validating inputs...")
-
-        if not video_file:
-            return None, "❌ Error: No video file provided", "\n".join(log_messages)
-
         video_path = Path(video_file)
-        log(f"Video: {video_path.name}")
+        log(f"Processing video: {video_path.name}")
 
         # Import here to avoid issues at startup
         from utils.helpers import validate_video_file, validate_image_file, format_duration
@@ -54,19 +50,17 @@ def process_video(video_file, domain, num_clips, clip_duration, reference_image,
 
         validation = validate_video_file(video_file)
         if not validation.is_valid:
-            return None, 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")
 
         # Validate reference image if provided
         ref_path = None
-        if reference_image:
+        if reference_image is not None:
             ref_validation = validate_image_file(reference_image)
             if ref_validation.is_valid:
                 ref_path = reference_image
                 log(f"Reference image: {Path(reference_image).name}")
-            else:
-                log(f"Warning: Invalid reference image - {ref_validation.error_message}")
 
         # Map domain string
         domain_map = {
@@ -82,14 +76,13 @@ def process_video(video_file, domain, num_clips, clip_duration, reference_image,
 
         # Create output directory
         output_dir = Path(tempfile.mkdtemp(prefix="shortsmith_output_"))
-        log(f"Output directory: {output_dir}")
 
         # Initialize pipeline
         log("Initializing pipeline...")
         pipeline = PipelineOrchestrator()
 
         # Process video
-        log(f"Processing: {num_clips} clips @ {clip_duration}s each")
+        log(f"Processing: {int(num_clips)} clips @ {int(clip_duration)}s each")
 
         result = pipeline.process(
             video_path=video_path,
@@ -97,12 +90,12 @@ def process_video(video_file, domain, num_clips, clip_duration, reference_image,
             clip_duration=float(clip_duration),
             domain=domain_value,
             reference_image=ref_path,
-            custom_prompt=custom_prompt if custom_prompt and custom_prompt.strip() else None,
+            custom_prompt=custom_prompt if custom_prompt else None,
         )
 
         # Handle result
         if result.success:
-            log(f"✅ Processing complete in {result.processing_time:.1f}s")
+            log(f"Processing complete in {result.processing_time:.1f}s")
 
             clip_paths = []
             for i, clip in enumerate(result.clips):
@@ -110,84 +103,50 @@ def process_video(video_file, domain, num_clips, clip_duration, reference_image,
                     output_path = output_dir / f"highlight_{i+1}.mp4"
                     shutil.copy2(clip.clip_path, output_path)
                     clip_paths.append(str(output_path))
-                    log(f"  Clip {i+1}: {format_duration(clip.start_time)} - {format_duration(clip.end_time)} (score: {clip.hype_score:.2f})")
-
-            status = f"✅ Successfully extracted {len(clip_paths)} highlight clips!\nProcessing time: {result.processing_time:.1f}s"
+                    log(f"Clip {i+1}: {format_duration(clip.start_time)} - {format_duration(clip.end_time)}")
 
+            status = f"Successfully extracted {len(clip_paths)} clips in {result.processing_time:.1f}s"
             pipeline.cleanup()
-            return clip_paths if clip_paths else None, status, "\n".join(log_messages)
+
+            if clip_paths:
+                return clip_paths, status, "\n".join(log_messages)
+            else:
+                return None, "No clips extracted", "\n".join(log_messages)
         else:
-            log(f"❌ Processing failed: {result.error_message}")
+            log(f"Failed: {result.error_message}")
             pipeline.cleanup()
-            return None, 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}")
+        log(f"Error: {str(e)}")
         log(traceback.format_exc())
-        return None, f"❌ {error_msg}", "\n".join(log_messages)
-
-
-# Create interface
-with gr.Blocks(title="ShortSmith v2", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-    # 🎬 ShortSmith v2
-    ### AI-Powered Video Highlight Extractor
-
-    Upload a video and extract the most engaging highlight clips automatically.
-    """)
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### 📤 Input")
-
-            video_input = gr.Video(label="Upload Video")
-
-            domain_dropdown = gr.Dropdown(
-                choices=["Sports", "Vlogs", "Music Videos", "Podcasts", "Gaming", "General"],
-                value="General",
-                label="Content Domain",
-            )
-
-            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("👤 Person Filtering (Optional)", open=False):
-                reference_image = gr.Image(label="Reference Image", type="filepath")
-
-            with gr.Accordion("📝 Custom Instructions (Optional)", open=False):
-                custom_prompt = gr.Textbox(
-                    label="Additional Instructions",
-                    placeholder="E.g., 'Focus on crowd reactions'",
-                    lines=2,
-                )
-
-            process_btn = gr.Button("🚀 Extract Highlights", variant="primary", size="lg")
-
-        with gr.Column(scale=1):
-            gr.Markdown("### 📥 Output")
-
-            status_output = gr.Textbox(label="Status", lines=3, interactive=False)
-            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")
-
-    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__":
-    demo.queue()
-    demo.launch(server_name="0.0.0.0", server_port=7860)
+        return None, f"Error: {str(e)}", "\n".join(log_messages)
+
+
+# Create simple interface
+demo = gr.Interface(
+    fn=process_video,
+    inputs=[
+        gr.Video(label="Upload Video"),
+        gr.Dropdown(
+            choices=["Sports", "Vlogs", "Music Videos", "Podcasts", "Gaming", "General"],
+            value="General",
+            label="Content Domain"
+        ),
+        gr.Slider(minimum=1, maximum=10, value=3, step=1, label="Number of Clips"),
+        gr.Slider(minimum=5, maximum=30, value=15, step=1, label="Clip Duration (seconds)"),
+        gr.Image(label="Reference Image (Optional)", type="filepath"),
+        gr.Textbox(label="Custom Instructions (Optional)", placeholder="E.g., Focus on crowd reactions"),
+    ],
+    outputs=[
+        gr.Files(label="Extracted Clips"),
+        gr.Textbox(label="Status"),
+        gr.Textbox(label="Processing Log"),
+    ],
+    title="ShortSmith v2",
+    description="AI-Powered Video Highlight Extractor. Upload a video and extract the most engaging clips automatically.",
+    allow_flagging="never",
+)
+
+demo.queue()
+demo.launch()