fix: upgrade to gradio 5.6.0 to fix gradio_client json_schema_to_python_type bug

README.md

@@ -5,7 +5,7 @@ emoji: 🎵
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: "4.44.0"
+sdk_version: "5.6.0"
 python_version: "3.10"
 app_file: app.py
 pinned: false

app.py

@@ -14,42 +14,51 @@ import tempfile
 
 token = os.getenv("HF_TOKEN")
 
+
 # Install madmom from GitHub
 def install_madmom():
-    subprocess.check_call([
-        sys.executable, "-m", "pip", "install", 
-        "git+https://github.com/CPJKU/madmom", "--no-cache-dir"
-    ])
+    subprocess.check_call(
+        [
+            sys.executable,
+            "-m",
+            "pip",
+            "install",
+            "git+https://github.com/CPJKU/madmom",
+            "--no-cache-dir",
+        ]
+    )
     print("madmom installed from GitHub")
 
+
 install_madmom()
 
 # Add current directory to Python path for ml_models
-sys.path.insert(0, '.')
-sys.path.insert(0, './ml_models')
+sys.path.insert(0, ".")
+sys.path.insert(0, "./ml_models")
+
 
 def download_data_from_hub():
     print("=== DOWNLOAD FUNCTION START ===")
     base_dir = Path(".")
     data_repo_id = "mippia/music-data"
-    
+
     print(f"Base directory: {base_dir.absolute()}")
     print(f"Repository: {data_repo_id}")
-    
+
     folders_to_check = ["covers80", "ml_models"]
     downloaded_folders = {}
-    
+
     # Check LFS file
     lfs_file = base_dir / "1005_e_4"
     print(f"Checking LFS file: {lfs_file}")
     if lfs_file.exists():
-        file_size = lfs_file.stat().st_size / (1024*1024)
+        file_size = lfs_file.stat().st_size / (1024 * 1024)
         print(f"LFS file found: {file_size:.1f} MB")
         downloaded_folders["1005_e_4"] = str(lfs_file)
     else:
         print("LFS file not found")
         downloaded_folders["1005_e_4"] = None
-    
+
     # Check existing folders
     print("=== CHECKING EXISTING FOLDERS ===")
     for folder in folders_to_check:
@@ -62,19 +71,21 @@ def download_data_from_hub():
                 print(f"  {folder} exists but is empty")
         else:
             print(f"  {folder} does not exist")
-    
-    all_folders_exist = all((base_dir / folder).exists() and any((base_dir / folder).iterdir()) 
-                           for folder in folders_to_check)
+
+    all_folders_exist = all(
+        (base_dir / folder).exists() and any((base_dir / folder).iterdir())
+        for folder in folders_to_check
+    )
     print(f"All folders exist: {all_folders_exist}")
-    
+
     if not all_folders_exist:
         print("=== STARTING DOWNLOAD ===")
-        
+
         # Download to a temporary directory first
         temp_dir = base_dir / "temp_download"
         print(f"Creating temp directory: {temp_dir}")
         temp_dir.mkdir(exist_ok=True)
-        
+
         print("Calling snapshot_download...")
         downloaded_path = snapshot_download(
             repo_id=data_repo_id,
@@ -82,11 +93,11 @@ def download_data_from_hub():
             local_dir=str(temp_dir),
             local_dir_use_symlinks=False,
             token=token,
-            ignore_patterns=["*.md", "*.txt", ".gitattributes", "README.md"]
+            ignore_patterns=["*.md", "*.txt", ".gitattributes", "README.md"],
         )
-        
+
         print(f"Download completed to: {downloaded_path}")
-        
+
         # Check what was downloaded
         print("=== CHECKING TEMP DOWNLOAD CONTENTS ===")
         print(f"Temp directory contents:")
@@ -96,31 +107,33 @@ def download_data_from_hub():
             if item.is_dir():
                 file_count = len([f for f in item.rglob("*") if f.is_file()])
                 print(f"    Contains {file_count} files")
-        
+
         # Move folders from temp to current directory
         print("=== MOVING FOLDERS ===")
         for folder_name in folders_to_check:
             temp_folder_path = temp_dir / folder_name
             target_folder_path = base_dir / folder_name
-            
+
             print(f"Processing {folder_name}:")
             print(f"  Source: {temp_folder_path}")
             print(f"  Target: {target_folder_path}")
             print(f"  Source exists: {temp_folder_path.exists()}")
-            
+
             if temp_folder_path.exists():
                 # Remove existing target if it exists
                 if target_folder_path.exists():
                     print(f"  Removing existing target directory")
                     shutil.rmtree(target_folder_path)
-                
+
                 # Move folder
                 print(f"  Moving folder...")
                 shutil.move(str(temp_folder_path), str(target_folder_path))
-                
+
                 # Verify move
                 if target_folder_path.exists():
-                    file_count = len([f for f in target_folder_path.rglob("*") if f.is_file()])
+                    file_count = len(
+                        [f for f in target_folder_path.rglob("*") if f.is_file()]
+                    )
                     print(f"  SUCCESS: {folder_name} moved with {file_count:,} files")
                     downloaded_folders[folder_name] = str(target_folder_path)
                 else:
@@ -129,13 +142,13 @@ def download_data_from_hub():
             else:
                 print(f"  ERROR: {folder_name} not found in temp download")
                 downloaded_folders[folder_name] = None
-        
+
         # Clean up temp directory
         print("=== CLEANING UP TEMP DIRECTORY ===")
         if temp_dir.exists():
             shutil.rmtree(temp_dir)
             print("Temp directory removed")
-        
+
     else:
         print("=== USING EXISTING FOLDERS ===")
         for folder_name in folders_to_check:
@@ -146,14 +159,15 @@ def download_data_from_hub():
                 downloaded_folders[folder_name] = str(folder_path)
             else:
                 downloaded_folders[folder_name] = None
-    
+
     print("=== FINAL STATUS ===")
     for key, value in downloaded_folders.items():
         print(f"{key}: {value}")
-    
+
     print("=== DOWNLOAD FUNCTION END ===")
     return downloaded_folders
 
+
 # Download data and check results
 print("Starting Music Plagiarism Detection App...")
 folders = download_data_from_hub()
@@ -179,96 +193,90 @@ if ml_models_path.exists():
 # Import updated inference
 print("=== IMPORTING INFERENCE ===")
 
+
 # Updated inference functions
 def inference(audio_path):
     from segment_transcription import segment_transcription
     from compare import get_one_result
-    
+
     segment_datas = segment_transcription(audio_path)
     result = get_one_result(segment_datas)
     final_result = result_formatting(result)
     return final_result
 
+
 def result_formatting(result):
     """
     get_one_result에서 나온 결과를 포맷팅
     result: sorted list of CompareHelper objects
     """
     if not result or len(result) == 0:
-        return {
-            'matches': [],
-            'message': 'No matches found'
-        }
+        return {"matches": [], "message": "No matches found"}
 
     # 에러 메시지 체크
     if isinstance(result, list) and len(result) > 0 and isinstance(result[0], str):
         return {
-            'matches': [],
-            'message': result[0] # "there is no note for this song"
+            "matches": [],
+            "message": result[0],  # "there is no note for this song"
         }
 
     # 상위 3개 결과 추출
     top_3_results = []
     for i, compare_helper in enumerate(result[:3]):
-        score = compare_helper.data[0] # similarity score
-        test_label = compare_helper.data[1] # test song info
-        library_label = compare_helper.data[2] # matched song info
+        score = compare_helper.data[0]  # similarity score
+        test_label = compare_helper.data[1]  # test song info
+        library_label = compare_helper.data[2]  # matched song info
 
         # 라이브러리 레이블에서 정보 추출
-        song_title = library_label.get('title', 'Unknown Song')
-        library_time = library_label.get('time', 0) # 매치된 구간의 시간
-        library_time2 = library_label.get('time2', 0)
+        song_title = library_label.get("title", "Unknown Song")
+        library_time = library_label.get("time", 0)  # 매치된 구간의 시간
+        library_time2 = library_label.get("time2", 0)
 
         # 테스트 레이블에서 정보 추출
-        test_time = test_label.get('time', 0) if test_label else 0 # 입력 곡의 시간
-        test_time2 = test_label.get('time2', 0) if test_label else 0
+        test_time = test_label.get("time", 0) if test_label else 0  # 입력 곡의 시간
+        test_time2 = test_label.get("time2", 0) if test_label else 0
 
         match_info = {
-            'rank': i + 1,
-            'score': float(score*100),
-            'song_title': song_title,
-            'test_time': float(test_time), # 입력 곡에서 매치된 시간
-            'test_time2' : float(test_time2),
-            'library_time': float(library_time), # 라이브러리 곡에서 매치된 시간
-            'library_time2': float(library_time2),
-            'confidence': f"{score * 100:.1f}%",
-            'time_match': f"Input: {test_time:.1f}s ↔ Library: {library_time:.1f}s"
+            "rank": i + 1,
+            "score": float(score * 100),
+            "song_title": song_title,
+            "test_time": float(test_time),  # 입력 곡에서 매치된 시간
+            "test_time2": float(test_time2),
+            "library_time": float(library_time),  # 라이브러리 곡에서 매치된 시간
+            "library_time2": float(library_time2),
+            "confidence": f"{score * 100:.1f}%",
+            "time_match": f"Input: {test_time:.1f}s ↔ Library: {library_time:.1f}s",
         }
         top_3_results.append(match_info)
 
-    return {
-        'matches': top_3_results,
-        'message': 'success'
-    }
+    return {"matches": top_3_results, "message": "success"}
+
 
 def find_song_file_by_title(song_title):
     covers80_path = Path("covers80")
-    
+
     if not covers80_path.exists():
         return None
-    
+
     # Try exact match patterns
-    exact_patterns = [
-        f"{song_title}.mp3",
-        f"*{song_title}.mp3",
-        f"{song_title}*.mp3"
-    ]
-    
+    exact_patterns = [f"{song_title}.mp3", f"*{song_title}.mp3", f"{song_title}*.mp3"]
+
     for pattern in exact_patterns:
         matches = list(covers80_path.glob(pattern))
         if matches:
             return str(matches[0])
-    
+
     # Try partial matches
-    song_parts = song_title.replace('_', ' ').split()
+    song_parts = song_title.replace("_", " ").split()
     for part in song_parts:
         if len(part) > 3:
             matches = list(covers80_path.glob(f"*{part}*.mp3"))
             if matches:
                 return str(matches[0])
-    
+
     return None
 
+
 def extract_audio_segment(audio_file_path, start_time, end_time):
     """
     오디오 파일에서 특정 구간을 추출하여 임시 파일로 저장
@@ -276,109 +284,124 @@ def extract_audio_segment(audio_file_path, start_time, end_time):
     try:
         # Load audio file
         y, sr = librosa.load(audio_file_path, sr=None)
-        
+
         # Convert time to samples
         start_sample = int(start_time * sr)
         end_sample = int(end_time * sr)
-        
+
         # Extract segment
         segment = y[start_sample:end_sample]
-        
+
         # Create temporary file
-        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.wav')
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
         temp_file.close()
-        
+
         # Save segment
         import soundfile as sf
+
         sf.write(temp_file.name, segment, sr)
-        
+
         return temp_file.name
-        
+
     except Exception as e:
         print(f"Error extracting segment: {e}")
         return None
 
+
 def format_time(seconds):
     """Convert seconds to MM:SS format"""
     if seconds is None or seconds < 0:
         return "0:00"
-    
+
     minutes = int(seconds // 60)
     seconds = int(seconds % 60)
     return f"{minutes}:{seconds:02d}"
 
+
 @spaces.GPU(duration=300)
 def process_audio_for_matching(audio_file):
     if audio_file is None:
-        return [None] * 9 + ["""
+        return [None] * 9 + [
+            """
         <div style='text-align: center; color: #dc2626; padding: 20px; background: #fef2f2; border-radius: 8px;'>
             <h3>No Audio File</h3>
             <p>Please upload an audio file to get started!</p>
         </div>
-        """]
+        """
+        ]
 
     result = inference(audio_file)
-    
-    if result.get('message') != 'success':
-        return [None] * 9 + [f"""
+
+    if result.get("message") != "success":
+        return [None] * 9 + [
+            f"""
         <div style="text-align: center; padding: 20px; background: #fefce8; border-radius: 8px;">
             <h3 style="color: #a16207;">No Matches Found</h3>
-            <p style="color: #a16207;">{result.get('message', 'Unknown error occurred')}</p>
+            <p style="color: #a16207;">{result.get("message", "Unknown error occurred")}</p>
         </div>
-        """]
-    
-    matches = result.get('matches', [])
+        """
+        ]
+
+    matches = result.get("matches", [])
     if not matches:
-        return [None] * 9 + ["""
+        return [None] * 9 + [
+            """
         <div style="text-align: center; padding: 20px; background: #fefce8; border-radius: 8px;">
             <h3 style="color: #a16207;">No Matches Found</h3>
             <p style="color: #a16207;">No matching vocals found in the dataset.</p>
         </div>
-        """]
-    
+        """
+        ]
+
     # Initialize audio outputs
     audio_outputs = [None] * 9  # Reduced from 10 to 9 (removed original audio)
-    
+
     # Get full songs and segments for top 3 matches
     for i, match in enumerate(matches[:3]):
-        song_title = match.get('song_title', 'Unknown Song')
+        song_title = match.get("song_title", "Unknown Song")
         song_file_path = find_song_file_by_title(song_title)
-        
-        print(f"Match {i+1}: {song_title}")
+
+        print(f"Match {i + 1}: {song_title}")
         print(f"  File path: {song_file_path}")
-        
+
         if song_file_path and os.path.exists(song_file_path):
             # Full matched song (indices 0, 1, 2)
             audio_outputs[i] = song_file_path
-            
+
             # Extract segments for input audio (indices 3, 5, 7)
-            input_start = match.get('test_time', 0)
-            input_end = match.get('test_time2', input_start + 10)  # Default 10 seconds if no end time
+            input_start = match.get("test_time", 0)
+            input_end = match.get(
+                "test_time2", input_start + 10
+            )  # Default 10 seconds if no end time
             input_segment = extract_audio_segment(audio_file, input_start, input_end)
             audio_outputs[3 + i * 2] = input_segment
-            
+
             # Extract segments for matched song (indices 4, 6, 8)
-            library_start = match.get('library_time', 0)
-            library_end = match.get('library_time2', library_start + 10)  # Default 10 seconds if no end time
-            library_segment = extract_audio_segment(song_file_path, library_start, library_end)
+            library_start = match.get("library_time", 0)
+            library_end = match.get(
+                "library_time2", library_start + 10
+            )  # Default 10 seconds if no end time
+            library_segment = extract_audio_segment(
+                song_file_path, library_start, library_end
+            )
             audio_outputs[4 + i * 2] = library_segment
-    
+
     # Generate results HTML
     matches_html = ""
     for i, match in enumerate(matches[:3]):
-        rank = match.get('rank', 0)
-        song_title = match.get('song_title', 'Unknown Song')
-        song_title = song_title.replace('_', ' ').replace(' temp','')
-        score = match.get('score', 0)  # Raw score instead of confidence
-        test_time = match.get('test_time', 0)
-        test_time2 = match.get('test_time2', 0)
-        library_time = match.get('library_time', 0)
-        library_time2 = match.get('library_time2', 0)
-        
+        rank = match.get("rank", 0)
+        song_title = match.get("song_title", "Unknown Song")
+        song_title = song_title.replace("_", " ").replace(" temp", "")
+        score = match.get("score", 0)  # Raw score instead of confidence
+        test_time = match.get("test_time", 0)
+        test_time2 = match.get("test_time2", 0)
+        library_time = match.get("library_time", 0)
+        library_time2 = match.get("library_time2", 0)
+
         # Ranking colors
-        rank_colors = {1: '#dc2626', 2: '#ea580c', 3: '#16a34a'}
-        rank_color = rank_colors.get(rank, '#6b7280')
-        
+        rank_colors = {1: "#dc2626", 2: "#ea580c", 3: "#16a34a"}
+        rank_color = rank_colors.get(rank, "#6b7280")
+
         matches_html += f"""
         <div style="background: #ffffff; border-radius: 8px; padding: 15px; margin: 10px 0; 
                     border-left: 4px solid {rank_color}; box-shadow: 0 2px 8px rgba(0,0,0,0.1);">
@@ -415,7 +438,7 @@ def process_audio_for_matching(audio_file):
             </div>
         </div>
         """
-    
+
     results_html = f"""
     <div style="background: #ffffff; border-radius: 12px; padding: 20px; 
                 box-shadow: 0 4px 15px rgba(0,0,0,0.08); border: 1px solid #e5e7eb;">
@@ -428,9 +451,10 @@ def process_audio_for_matching(audio_file):
         {matches_html}
     </div>
     """
-    
+
     return audio_outputs + [results_html]
 
+
 # CSS styles
 custom_css = """
 .gradio-container { 
@@ -464,9 +488,11 @@ custom_css = """
 """
 
 # Gradio interface
-with gr.Blocks(css=custom_css, theme=gr.themes.Soft(), title="Music Plagiarism Detection") as demo:
-    
-    gr.Markdown("""
+with gr.Blocks(
+    css=custom_css, theme=gr.themes.Soft(), title="Music Plagiarism Detection"
+) as demo:
+    gr.Markdown(
+        """
     <div style="text-align: center; margin-bottom: 20px;">
         <h1 style="color: #111827; font-size: 2.2em; margin-bottom: 10px;">Segment-level Detection Demo</h1>
         <p><strong>Music Plagiarism Detection: Problem Formulation and a Segment-based Solution</strong></p>
@@ -482,87 +508,139 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Soft(), title="Music Plagiarism D
         </p>
         <p style="color: #dc2626; font-weight: 600;">Processing can take up to 2 minutes per file</p>
     </div>
-    """, elem_classes=["main-container"])
-    
+    """,
+        elem_classes=["main-container"],
+    )
+
     # Input section
     with gr.Row():
-        audio_input = gr.Audio(type="filepath", label="Upload Your Audio File", elem_id="audio_input")
-    
+        audio_input = gr.Audio(
+            type="filepath", label="Upload Your Audio File", elem_id="audio_input"
+        )
+
     with gr.Row():
         submit_btn = gr.Button("Analyze Audio", variant="primary", size="lg")
-    
+
     # Output section
     with gr.Row():
         # Left column - Full Songs
         with gr.Column(scale=2):
             gr.Markdown("### 🎵 Matched Songs", elem_classes=["audio-section"])
-            
+
             with gr.Row():
-                match1_full = gr.Audio(label="Match #1 - Full Song", show_label=True, elem_id="match1_full")
-                match2_full = gr.Audio(label="Match #2 - Full Song", show_label=True, elem_id="match2_full")
-                match3_full = gr.Audio(label="Match #3 - Full Song", show_label=True, elem_id="match3_full")
-        
+                match1_full = gr.Audio(
+                    label="Match #1 - Full Song", show_label=True, elem_id="match1_full"
+                )
+                match2_full = gr.Audio(
+                    label="Match #2 - Full Song", show_label=True, elem_id="match2_full"
+                )
+                match3_full = gr.Audio(
+                    label="Match #3 - Full Song", show_label=True, elem_id="match3_full"
+                )
+
         # Right column - Results
         with gr.Column(scale=1):
             results = gr.HTML(label="Analysis Results")
-    
+
     # Segments section
     with gr.Row():
         with gr.Column():
-            gr.Markdown("### 🎯 Matched Segments Comparison", elem_classes=["audio-section"])
-            
+            gr.Markdown(
+                "### 🎯 Matched Segments Comparison", elem_classes=["audio-section"]
+            )
+
             # Match 1 segments
             with gr.Row():
                 with gr.Column():
-                    gr.Markdown("**Match #1 - Your Segment**", elem_classes=["segment-container"])
-                    match1_input_segment = gr.Audio(label="Your Audio Segment", show_label=False, elem_id="match1_input_seg")
+                    gr.Markdown(
+                        "**Match #1 - Your Segment**",
+                        elem_classes=["segment-container"],
+                    )
+                    match1_input_segment = gr.Audio(
+                        label="Your Audio Segment",
+                        show_label=False,
+                        elem_id="match1_input_seg",
+                    )
                 with gr.Column():
-                    gr.Markdown("**Match #1 - Matched Segment**", elem_classes=["segment-container"])
-                    match1_library_segment = gr.Audio(label="Library Segment", show_label=False, elem_id="match1_lib_seg")
-            
+                    gr.Markdown(
+                        "**Match #1 - Matched Segment**",
+                        elem_classes=["segment-container"],
+                    )
+                    match1_library_segment = gr.Audio(
+                        label="Library Segment",
+                        show_label=False,
+                        elem_id="match1_lib_seg",
+                    )
+
             # Match 2 segments
             with gr.Row():
                 with gr.Column():
-                    gr.Markdown("**Match #2 - Your Segment**", elem_classes=["segment-container"])
-                    match2_input_segment = gr.Audio(label="Your Audio Segment", show_label=False, elem_id="match2_input_seg")
+                    gr.Markdown(
+                        "**Match #2 - Your Segment**",
+                        elem_classes=["segment-container"],
+                    )
+                    match2_input_segment = gr.Audio(
+                        label="Your Audio Segment",
+                        show_label=False,
+                        elem_id="match2_input_seg",
+                    )
                 with gr.Column():
-                    gr.Markdown("**Match #2 - Matched Segment**", elem_classes=["segment-container"])
-                    match2_library_segment = gr.Audio(label="Library Segment", show_label=False, elem_id="match2_lib_seg")
-            
+                    gr.Markdown(
+                        "**Match #2 - Matched Segment**",
+                        elem_classes=["segment-container"],
+                    )
+                    match2_library_segment = gr.Audio(
+                        label="Library Segment",
+                        show_label=False,
+                        elem_id="match2_lib_seg",
+                    )
+
             # Match 3 segments
             with gr.Row():
                 with gr.Column():
-                    gr.Markdown("**Match #3 - Your Segment**", elem_classes=["segment-container"])
-                    match3_input_segment = gr.Audio(label="Your Audio Segment", show_label=False, elem_id="match3_input_seg")
+                    gr.Markdown(
+                        "**Match #3 - Your Segment**",
+                        elem_classes=["segment-container"],
+                    )
+                    match3_input_segment = gr.Audio(
+                        label="Your Audio Segment",
+                        show_label=False,
+                        elem_id="match3_input_seg",
+                    )
                 with gr.Column():
-                    gr.Markdown("**Match #3 - Matched Segment**", elem_classes=["segment-container"])
-                    match3_library_segment = gr.Audio(label="Library Segment", show_label=False, elem_id="match3_lib_seg")
-    
+                    gr.Markdown(
+                        "**Match #3 - Matched Segment**",
+                        elem_classes=["segment-container"],
+                    )
+                    match3_library_segment = gr.Audio(
+                        label="Library Segment",
+                        show_label=False,
+                        elem_id="match3_lib_seg",
+                    )
+
     # Define outputs list
     outputs = [
-        match1_full,             # 0
-        match2_full,             # 1  
-        match3_full,             # 2
-        match1_input_segment,    # 3
+        match1_full,  # 0
+        match2_full,  # 1
+        match3_full,  # 2
+        match1_input_segment,  # 3
         match1_library_segment,  # 4
-        match2_input_segment,    # 5
+        match2_input_segment,  # 5
         match2_library_segment,  # 6
-        match3_input_segment,    # 7
+        match3_input_segment,  # 7
         match3_library_segment,  # 8
-        results                  # 9
+        results,  # 9
     ]
-    
+
     submit_btn.click(
-        fn=process_audio_for_matching,
-        inputs=[audio_input],
-        outputs=outputs
+        fn=process_audio_for_matching, inputs=[audio_input], outputs=outputs
     )
 
 if __name__ == "__main__":
     demo.launch(
-        server_name="0.0.0.0", 
-        server_port=7860,  
-        show_api=False, 
+        server_name="0.0.0.0",
+        server_port=7860,
         show_error=True,
-        share=False
-    )
+        share=False,
+        ssr_mode=False,
+    )

requirements.txt

@@ -13,8 +13,7 @@ pretty_midi
 timm
 einops
 omegaconf
-huggingface_hub>=0.21.0,<0.25.0
-gradio_client==1.3.0
+huggingface_hub>=0.25.1
 soxr
 lameenc
 spaces