import spaces import gradio as gr import os import tempfile import huggingface_hub import shutil import logging import traceback from features import SHuBERTProcessor # Set up logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) # Set writable cache directories def setup_cache_directories(): """Set up cache directories with proper error handling""" try: cache_dirs = { 'MPLCONFIGDIR': '/tmp/matplotlib', 'TRANSFORMERS_CACHE': '/tmp/huggingface', 'HF_HOME': '/tmp/huggingface', 'FONTCONFIG_PATH': '/tmp/fontconfig', 'TORCH_HOME': '/tmp/torch', # PyTorch cache directory } for env_var, path in cache_dirs.items(): os.environ[env_var] = path os.makedirs(path, exist_ok=True, mode=0o777) logger.info(f"Cache directory created: {env_var} = {path}") # Also set XDG_CACHE_HOME to override default .cache location os.environ['XDG_CACHE_HOME'] = '/tmp/cache' os.makedirs('/tmp/cache', exist_ok=True, mode=0o777) logger.info(f"Cache directory created: XDG_CACHE_HOME = /tmp/cache") # Clear any existing PyTorch Hub cache to avoid corruption issues torch_hub_dir = '/tmp/torch/hub' if os.path.exists(torch_hub_dir): shutil.rmtree(torch_hub_dir) logger.info("Cleared existing PyTorch Hub cache") os.makedirs(torch_hub_dir, exist_ok=True, mode=0o777) logger.info(f"Created clean PyTorch Hub cache directory: {torch_hub_dir}") # Copy updated DINOv2 files to torch cache after clearing # This ensures they're available when PyTorch Hub downloads the repo try: src_dir = os.path.dirname(os.path.abspath(__file__)) target_dir = '/tmp/torch/hub/facebookresearch_dinov2_main/dinov2/layers' for filename in ['attention.py', 'block.py']: src_path = os.path.join(src_dir, filename) if os.path.exists(src_path): # We'll copy these after the initial hub download logger.info(f"Found {filename} in project directory - will copy after hub download") else: logger.warning(f"Could not find {filename} in project directory") except Exception as e: logger.warning(f"Error preparing DINOv2 files: {e}") return True except Exception as e: logger.error(f"Error creating cache directories: {str(e)}") return False # Configuration for Hugging Face Spaces MODEL_REPO = "ShesterG/SHuBERT" TOKEN = os.environ.get('HF_TOKEN') def validate_environment(): """Validate required environment variables and setup""" if not TOKEN: raise ValueError("HF_TOKEN environment variable not set. This is required to access private model repository.") # Check available disk space free_space = shutil.disk_usage('/').free / (1024*1024*1024) # GB logger.info(f"Available disk space: {free_space:.2f} GB") if free_space < 2: # Less than 2GB logger.warning("Low disk space available. This may cause issues.") return True def download_models(): """Download all required models from Hugging Face Hub with enhanced error handling""" logger.info("Starting model download process...") try: # Validate environment first validate_environment() logger.info("Downloading entire models folder...") # Download the entire models folder models_path = huggingface_hub.snapshot_download( repo_id=MODEL_REPO, allow_patterns="models/*", # Download everything in models folder token=TOKEN, cache_dir=os.environ['TRANSFORMERS_CACHE'] ) # Build config dict with expected file paths config = { 'yolov8_model_path': os.path.join(models_path, "models/yolov8n.pt"), 'dino_face_model_path': os.path.join(models_path, "models/dinov2face.pth"), 'dino_hands_model_path': os.path.join(models_path, "models/dinov2hand.pth"), 'mediapipe_face_model_path': os.path.join(models_path, "models/face_landmarker_v2_with_blendshapes.task"), 'mediapipe_hands_model_path': os.path.join(models_path, "models/hand_landmarker.task"), 'shubert_model_path': os.path.join(models_path, "models/checkpoint_836_400000.pt"), 'slt_model_config': os.path.join(models_path, "models/byt5_base/config.json"), 'slt_model_checkpoint': os.path.join(models_path, "models/checkpoint-11625"), 'slt_tokenizer_checkpoint': os.path.join(models_path, "models/byt5_base"), 'temp_dir': 'temp' } # Verify all required files and folders exist logger.info("Verifying downloaded files...") missing_files = [] for key, path in config.items(): if key == 'temp_dir': # Skip temp_dir check continue if not os.path.exists(path): missing_files.append(f"{key}: {path}") logger.error(f"Missing: {path}") else: logger.info(f"✓ Found: {path}") if missing_files: logger.error(f"Missing {len(missing_files)} required files/folders:") for missing in missing_files: logger.error(f" - {missing}") raise FileNotFoundError(f"Required files not found: {missing_files}") logger.info("All models downloaded and verified successfully!") logger.info(f"Models root path: {models_path}") return config except Exception as e: logger.error(f"Error downloading models: {str(e)}") logger.error(f"Traceback: {traceback.format_exc()}") # Additional debugging info try: cache_contents = os.listdir(os.environ['TRANSFORMERS_CACHE']) logger.info(f"Cache directory contents: {cache_contents}") except: logger.error("Cannot access cache directory") return None def download_example_videos(): """Download example videos from Hugging Face Hub with enhanced error handling""" # Download the entire example_videos folder example_video_path = huggingface_hub.snapshot_download( repo_id=MODEL_REPO, allow_patterns="example_video/*", # Download everything in example_videos folder token=TOKEN, cache_dir=os.environ['TRANSFORMERS_CACHE'] ) example_video_path_list = [ os.path.join(example_video_path, "rDUefZVPfmU_crop_1.mp4"), os.path.join(example_video_path, "rDUefZVPfmU_crop_2.mp4"), os.path.join(example_video_path, "rDUefZVPfmU_crop_3.mp4"), os.path.join(example_video_path, "rDUefZVPfmU_crop_4.mp4"), os.path.join(example_video_path, "rDUefZVPfmU_crop_5.mp4"), os.path.join(example_video_path, "rDUefZVPfmU_crop_6.mp4"), os.path.join(example_video_path, "rDUefZVPfmU_crop_7.mp4"), os.path.join(example_video_path, "rDUefZVPfmU_crop_8.mp4"), os.path.join(example_video_path, "rDUefZVPfmU_crop_9.mp4"), os.path.join(example_video_path, "rDUefZVPfmU_crop_10.mp4"), os.path.join(example_video_path, "L5hUxT5YbnY_crop_1.mp4"), os.path.join(example_video_path, "L5hUxT5YbnY_crop_2.mp4"), os.path.join(example_video_path, "L5hUxT5YbnY_crop_3.mp4"), os.path.join(example_video_path, "L5hUxT5YbnY_crop_4.mp4"), os.path.join(example_video_path, "L5hUxT5YbnY_crop_5.mp4"), os.path.join(example_video_path, "L5hUxT5YbnY_crop_6.mp4"), os.path.join(example_video_path, "L5hUxT5YbnY_crop_7.mp4"), ] return example_video_path_list def initialize_processor(config): """Initialize SHuBERT processor with error handling""" try: logger.info("Initializing SHuBERT processor...") processor = SHuBERTProcessor(config) logger.info("SHuBERT processor initialized successfully!") return processor except Exception as e: logger.error(f"Error initializing SHuBERT processor: {str(e)}") logger.error(f"Traceback: {traceback.format_exc()}") return None # Initialize the application def initialize_app(): """Initialize the entire application with comprehensive error handling""" try: # Setup cache directories if not setup_cache_directories(): raise RuntimeError("Failed to setup cache directories") # Download models config = download_models() if config is None: raise RuntimeError("Failed to download models") # Initialize processor processor = initialize_processor(config) if processor is None: raise RuntimeError("Failed to initialize SHuBERT processor") logger.info("Application initialized successfully!") return config, processor except Exception as e: error_msg = f"Application initialization failed: {str(e)}" logger.error(error_msg) logger.error(f"Full traceback: {traceback.format_exc()}") raise RuntimeError(error_msg) # Global variables for application state config = None processor = None initialization_error = None try: config, processor = initialize_app() except Exception as e: initialization_error = str(e) logger.error(f"Startup failed: {initialization_error}") def copy_dinov2_files_if_needed(): """Copy updated DINOv2 files after PyTorch Hub download if needed""" try: src_dir = os.path.dirname(os.path.abspath(__file__)) target_dir = '/tmp/torch/hub/facebookresearch_dinov2_main/dinov2/layers' # Check if PyTorch Hub has downloaded the repository hub_main_dir = '/tmp/torch/hub/facebookresearch_dinov2_main' if os.path.exists(hub_main_dir): # Ensure the target directory exists os.makedirs(target_dir, exist_ok=True) files_copied = 0 for filename in ['attention.py', 'block.py']: src_path = os.path.join(src_dir, filename) target_path = os.path.join(target_dir, filename) if os.path.exists(src_path): # Always overwrite with our robust versions shutil.copy2(src_path, target_path) # Make sure it's readable os.chmod(target_path, 0o644) logger.info(f"Replaced {filename} with robust version (numpy/Python 3.8 compatible)") files_copied += 1 else: logger.error(f"Source file not found: {src_path}") if files_copied > 0: # Clear Python's import cache to ensure new files are used import importlib import sys # Remove any cached imports of dinov2 modules modules_to_remove = [key for key in sys.modules.keys() if 'dinov2' in key] for module in modules_to_remove: del sys.modules[module] logger.info(f"Cleared cached import: {module}") logger.info(f"Successfully replaced {files_copied} DINOv2 files with robust versions") return True else: logger.info("PyTorch Hub repository not yet downloaded") return False except Exception as e: logger.error(f"Error copying DINOv2 files: {e}") logger.error(f"Traceback: {traceback.format_exc()}") return False @spaces.GPU def process_video(video_file): """Process uploaded video file with enhanced error handling""" # Check if initialization was successful if initialization_error: return f"Application initialization failed: {initialization_error}\n\nPlease check the logs for more details." if processor is None: return "Error: Model not initialized properly. Please check the logs." if video_file is None: return "Please upload a video file." logger.info(f"=== Starting video processing ===") logger.info(f"Video file input: {video_file}") logger.info(f"Video file type: {type(video_file)}") try: # Create temp directory with proper permissions temp_dir = config['temp_dir'] os.makedirs(temp_dir, exist_ok=True, mode=0o777) logger.info(f"Temp directory: {temp_dir}") # Generate unique filename to avoid conflicts import time timestamp = str(int(time.time() * 1000)) file_extension = '.mp4' # Default extension # Try to get original extension if available try: if hasattr(video_file, 'name') and video_file.name: file_extension = os.path.splitext(video_file.name)[1] or '.mp4' elif isinstance(video_file, str): file_extension = os.path.splitext(video_file)[1] or '.mp4' except: pass temp_video_path = os.path.join(temp_dir, f"video_{timestamp}{file_extension}") logger.info(f"Target temp video path: {temp_video_path}") # Handle Gradio file upload - video_file is typically a string path to temp file logger.info(f"Processing video file: {video_file} (type: {type(video_file)})") if isinstance(video_file, str): # Gradio provides a file path string source_path = video_file # Handle both absolute and relative paths if not os.path.isabs(source_path): # Try current working directory first abs_source_path = os.path.abspath(source_path) logger.info(f"Converting relative path {source_path} to absolute: {abs_source_path}") if os.path.exists(abs_source_path): source_path = abs_source_path else: # Try looking in common Gradio temp directories possible_paths = [ source_path, os.path.join('/tmp', os.path.basename(source_path)), os.path.join('/tmp/gradio', os.path.basename(source_path)), abs_source_path ] found_path = None for path in possible_paths: logger.info(f"Checking path: {path}") if os.path.exists(path): found_path = path logger.info(f"Found file at: {path}") break if found_path: source_path = found_path else: logger.error(f"Could not find source file in any expected location") logger.error(f"Tried paths: {possible_paths}") raise FileNotFoundError(f"Source video file not found in any expected location: {video_file}") logger.info(f"Final source file path: {source_path}") logger.info(f"Source file exists: {os.path.exists(source_path)}") if os.path.exists(source_path): try: # Check source file permissions and size stat_info = os.stat(source_path) logger.info(f"Source file size: {stat_info.st_size} bytes, mode: {oct(stat_info.st_mode)}") # Try to read the file content with open(source_path, 'rb') as src: content = src.read() logger.info(f"Successfully read {len(content)} bytes from source") # Write to destination (with a different name to avoid conflicts) final_temp_path = os.path.join(temp_dir, f"processed_{timestamp}{file_extension}") with open(final_temp_path, 'wb') as dst: dst.write(content) logger.info(f"Successfully wrote to destination: {final_temp_path}") # Update temp_video_path to the final location temp_video_path = final_temp_path except PermissionError as e: logger.error(f"Permission error reading source file: {e}") # Try alternative approach - use a completely different temp location try: import tempfile # Create a new temporary file in system temp directory with tempfile.NamedTemporaryFile(delete=False, suffix=file_extension) as tmp: alternative_temp_path = tmp.name logger.info(f"Trying alternative temp path: {alternative_temp_path}") # Try to copy using system copy command as fallback import subprocess result = subprocess.run(['cp', source_path, alternative_temp_path], capture_output=True, text=True) if result.returncode == 0: logger.info("Successfully copied using system cp command") temp_video_path = alternative_temp_path else: logger.error(f"System cp failed: {result.stderr}") raise PermissionError(f"Cannot read video file due to permission restrictions: {e}") except Exception as e2: logger.error(f"Alternative copy method also failed: {e2}") raise PermissionError(f"Cannot read video file due to permission restrictions: {e}") else: raise FileNotFoundError(f"Source video file not found: {source_path}") elif hasattr(video_file, 'read'): # If it's a file-like object with read method try: content = video_file.read() with open(temp_video_path, 'wb') as f: f.write(content) logger.info(f"Saved video from file object: {temp_video_path} ({len(content)} bytes)") except Exception as e: logger.error(f"Error reading from file object: {e}") raise ValueError(f"Cannot read from file object: {e}") else: # Handle other cases - try to extract file path or content logger.info(f"Attempting to handle unknown file type: {type(video_file)}") try: # Check if it has a name attribute (common for file objects) if hasattr(video_file, 'name'): source_path = video_file.name logger.info(f"Found name attribute: {source_path}") if os.path.exists(source_path): with open(source_path, 'rb') as src: content = src.read() with open(temp_video_path, 'wb') as dst: dst.write(content) logger.info(f"Successfully copied from name attribute") else: raise FileNotFoundError(f"File from name attribute not found: {source_path}") else: logger.error(f"Unsupported video file type: {type(video_file)}") raise ValueError(f"Unsupported video file type: {type(video_file)}") except Exception as e: logger.error(f"Failed to handle unknown file type: {e}") raise ValueError(f"Cannot process video file: {e}") # Set proper permissions on the saved file os.chmod(temp_video_path, 0o666) # Verify file exists and has content if not os.path.exists(temp_video_path) or os.path.getsize(temp_video_path) == 0: raise ValueError("Video file is empty or could not be saved") # Copy DINOv2 files if needed before processing # This needs to happen right after PyTorch Hub downloads but before model loading logger.info("Ensuring DINOv2 files are ready for processing...") copy_dinov2_files_if_needed() # Set up a monitoring patch for torch.hub.load to replace files immediately after download original_torch_hub_load = None try: import torch.hub original_torch_hub_load = torch.hub.load def patched_torch_hub_load(*args, **kwargs): logger.info(f"PyTorch Hub load called with: {args[0] if args else 'unknown'}") # Call the original function first result = original_torch_hub_load(*args, **kwargs) # If this was a DINOv2 call, immediately replace the files if args and 'dinov2' in str(args[0]): logger.info("DINOv2 downloaded! Immediately replacing with robust versions...") # Try multiple times to ensure files are replaced import time for attempt in range(5): if copy_dinov2_files_if_needed(): logger.info("Successfully replaced DINOv2 files!") break else: logger.info(f"Attempt {attempt + 1} failed, retrying in 1 second...") time.sleep(1) return result # Temporarily patch torch.hub.load torch.hub.load = patched_torch_hub_load logger.info("Patched torch.hub.load to replace DINOv2 files after download") except Exception as e: logger.warning(f"Could not patch torch.hub.load: {e}") logger.info(f"Processing video: {temp_video_path}") try: output_text = processor.process_video(temp_video_path) finally: # Restore original function if original_torch_hub_load: try: import torch.hub torch.hub.load = original_torch_hub_load logger.info("Restored original torch.hub.load") except: pass logger.info(f"Video processed successfully. Output: {output_text[:100]}...") # Clean up temp file if os.path.exists(temp_video_path): os.remove(temp_video_path) logger.info("Temporary video file cleaned up") return output_text except Exception as e: logger.error(f"Error processing video: {str(e)}") logger.error(f"Traceback: {traceback.format_exc()}") return f"Error processing video: {str(e)}\n\nPlease check that your video is a valid ASL video under 10 seconds." # # Create Gradio interface # def create_interface(): # """Create the Gradio interface""" # description = """ # Upload an ASL* video to get an English translation. *Sign languages belonging to the same sign language family as ASL (e.g. Ghanaian Sign Language, as well as others listed in Table 7, Row 1 of https://aclanthology.org/2023.findings-emnlp.664.pdf) might also have non-trivial performance, although the model is trained only on ASL data. # This app uses TTIC's foundation model SHuBERT (introduced in an ACL 2025 paper, see http://shubert.pals.ttic.edu). # **Requirements:** # - We recommend that videos be under 20 seconds. Performance for longer videos has not been tested. # - The signer should be the main part (e.g. 90% of the area) of the video. Videos recorded from a phone camera, tablet, or personal computer should work well. Studio recordings where the signer is farther from the camera may not work as well. # - Supported formats: MP4, MOV # **Note:** # - This is just a demo of a research project, and should NOT be used to replace an interpreter in any way. # - Videos will be deleted after the output is generated. # - Inquires or feedback? Please email us at shesterg@ttic.edu # """ # if initialization_error: # description += f"\n\n:warning: **Initialization Error:** {initialization_error}" # # dailymoth_pathlist = download_example_videos() # src_dir = os.path.dirname(os.path.abspath(__file__)) # dailymoth_pathlist = [] # L5hUxT5YbnY_crop_1 = "dailymoth_examples/L5hUxT5YbnY_crop_1.mp4" # L5hUxT5YbnY_crop_2 = "dailymoth_examples/L5hUxT5YbnY_crop_2.mp4" # L5hUxT5YbnY_crop_3 = "dailymoth_examples/L5hUxT5YbnY_crop_3.mp4" # L5hUxT5YbnY_crop_4 = "dailymoth_examples/L5hUxT5YbnY_crop_4.mp4" # L5hUxT5YbnY_crop_5 = "dailymoth_examples/L5hUxT5YbnY_crop_5.mp4" # L5hUxT5YbnY_crop_6 = "dailymoth_examples/L5hUxT5YbnY_crop_6.mp4" # L5hUxT5YbnY_crop_7 = "dailymoth_examples/L5hUxT5YbnY_crop_7.mp4" # rDUefZVPfmU_crop_1 = "dailymoth_examples/rDUefZVPfmU_crop_1.mp4" # rDUefZVPfmU_crop_2 = "dailymoth_examples/rDUefZVPfmU_crop_2.mp4" # rDUefZVPfmU_crop_3 = "dailymoth_examples/rDUefZVPfmU_crop_3.mp4" # rDUefZVPfmU_crop_4 = "dailymoth_examples/rDUefZVPfmU_crop_4.mp4" # rDUefZVPfmU_crop_5 = "dailymoth_examples/rDUefZVPfmU_crop_5.mp4" # rDUefZVPfmU_crop_6 = "dailymoth_examples/rDUefZVPfmU_crop_6.mp4" # rDUefZVPfmU_crop_7 = "dailymoth_examples/rDUefZVPfmU_crop_7.mp4" # rDUefZVPfmU_crop_8 = "dailymoth_examples/rDUefZVPfmU_crop_8.mp4" # rDUefZVPfmU_crop_9 = "dailymoth_examples/rDUefZVPfmU_crop_9.mp4" # rDUefZVPfmU_crop_10 = "dailymoth_examples/rDUefZVPfmU_crop_10.mp4" # dailymoth_filenames = [L5hUxT5YbnY_crop_1, L5hUxT5YbnY_crop_2, L5hUxT5YbnY_crop_3, L5hUxT5YbnY_crop_4, L5hUxT5YbnY_crop_5, L5hUxT5YbnY_crop_6, L5hUxT5YbnY_crop_7, rDUefZVPfmU_crop_1, rDUefZVPfmU_crop_2, rDUefZVPfmU_crop_3, rDUefZVPfmU_crop_4, rDUefZVPfmU_crop_5, rDUefZVPfmU_crop_6, rDUefZVPfmU_crop_7, rDUefZVPfmU_crop_8, rDUefZVPfmU_crop_9, rDUefZVPfmU_crop_10] # for filename in dailymoth_filenames: # src_path = os.path.join(src_dir, filename) # if os.path.exists(src_path): # dailymoth_pathlist.append(src_path) # else: # print(f"Warning: File not found at {src_path}") # with gr.Blocks(title="ASL Video to English Text Translation") as interface: # gr.Markdown(f"# ASL Video to English Text Translation\n\n{description}") # with gr.Row(): # with gr.Column(): # video_input = gr.Video(label="ASL Video (under 20 seconds)", format="mp4", height=480, width=640) # submit_btn = gr.Button("Translate", variant="primary") # with gr.Column(): # output_text = gr.Textbox(label="English Translation", lines=3) # # Add examples section # if dailymoth_pathlist: # Only show examples if we have valid files # gr.Examples( # examples=dailymoth_pathlist, # inputs=video_input, # label="Click a video to try an example" # ) # # Add attribution note for the examples # gr.Markdown(""" # --- # **Example Videos Attribution:** # The example videos used in this demo are from [The Daily Moth](https://www.youtube.com/@TheDailyMoth), # a popular ASL news channel made by deaf creators. Specifically, they are from the Previews of [July 10](https://www.youtube.com/watch?v=rDUefZVPfmU) and [July 11](https://www.youtube.com/watch?v=L5hUxT5YbnY) 2025 Top Stories. # The videos are only used for illustrative purposes, and all rights to the content belong to The Daily Moth. In this light, we encourage to subscribe to their [channel](https://members.dailymoth.com/about). # """) # else: # gr.Markdown("*No example videos available at this time.*") # # video_input = gr.Video(label="ASL Video (under 20 seconds)", format="mp4", height=480, width=640) # # text_output = gr.Textbox(label="English Translation", lines=5) # # interface = gr.Interface( # # fn=process_video, # # inputs=video_input, # # outputs=text_output, # # title="ASL Video to English Text Translation", # # description=description, # # article="", # # # examples=dailymoth_pathlist, # # # example_labels=["Officials with an EU force said they are searching for the missing."], # # allow_flagging="never", # # ) # # gr.Examples( # # examples=dailymoth_pathlist, # # inputs=video_input, # # label="Click a video to try an example" # # ) # submit_btn.click(fn=process_video, inputs=video_input, outputs=output_text) # return interface def create_interface(): """Create the Gradio interface""" description = """Upload an ASL* video to get an English translation. *Sign languages belonging to the same sign language family as ASL (e.g. Ghanaian Sign Language, as well as others listed in Table 7, Row 1 of https://aclanthology.org/2023.findings-emnlp.664.pdf) might also have non-trivial performance, although the model is trained only on ASL data. This app uses TTIC's foundation model SHuBERT (introduced in an ACL 2025 paper, see http://shubert.pals.ttic.edu). **Requirements:** - We recommend that videos be under 20 seconds. Performance for longer videos has not been tested. - The signer should be the main part (e.g. 90% of the area) of the video. Videos recorded from a phone camera, tablet, or personal computer should work well. Studio recordings where the signer is farther from the camera may not work as well. - Supported formats: MP4, MOV **Note:** - This is just a demo of a research project, and should NOT be used to replace an interpreter in any way. - Videos will be deleted after the output is generated. - Inquires or feedback? Please email us at shesterg@ttic.edu""" if initialization_error: description += f"\n\n:warning: **Initialization Error:** {initialization_error}" src_dir = os.path.dirname(os.path.abspath(__file__)) dailymoth_pathlist = [] L5hUxT5YbnY_crop_1 = "dailymoth_examples/L5hUxT5YbnY_crop_1.mp4" L5hUxT5YbnY_crop_2 = "dailymoth_examples/L5hUxT5YbnY_crop_2.mp4" L5hUxT5YbnY_crop_3 = "dailymoth_examples/L5hUxT5YbnY_crop_3.mp4" L5hUxT5YbnY_crop_4 = "dailymoth_examples/L5hUxT5YbnY_crop_4.mp4" L5hUxT5YbnY_crop_5 = "dailymoth_examples/L5hUxT5YbnY_crop_5.mp4" L5hUxT5YbnY_crop_6 = "dailymoth_examples/L5hUxT5YbnY_crop_6.mp4" L5hUxT5YbnY_crop_7 = "dailymoth_examples/L5hUxT5YbnY_crop_7.mp4" rDUefZVPfmU_crop_1 = "dailymoth_examples/rDUefZVPfmU_crop_1.mp4" rDUefZVPfmU_crop_2 = "dailymoth_examples/rDUefZVPfmU_crop_2.mp4" rDUefZVPfmU_crop_3 = "dailymoth_examples/rDUefZVPfmU_crop_3.mp4" rDUefZVPfmU_crop_4 = "dailymoth_examples/rDUefZVPfmU_crop_4.mp4" rDUefZVPfmU_crop_5 = "dailymoth_examples/rDUefZVPfmU_crop_5.mp4" rDUefZVPfmU_crop_6 = "dailymoth_examples/rDUefZVPfmU_crop_6.mp4" rDUefZVPfmU_crop_7 = "dailymoth_examples/rDUefZVPfmU_crop_7.mp4" rDUefZVPfmU_crop_8 = "dailymoth_examples/rDUefZVPfmU_crop_8.mp4" rDUefZVPfmU_crop_9 = "dailymoth_examples/rDUefZVPfmU_crop_9.mp4" rDUefZVPfmU_crop_10 = "dailymoth_examples/rDUefZVPfmU_crop_10.mp4" dailymoth_filenames = [L5hUxT5YbnY_crop_1, L5hUxT5YbnY_crop_2, L5hUxT5YbnY_crop_3, L5hUxT5YbnY_crop_4, L5hUxT5YbnY_crop_5, L5hUxT5YbnY_crop_6, L5hUxT5YbnY_crop_7, rDUefZVPfmU_crop_1, rDUefZVPfmU_crop_2, rDUefZVPfmU_crop_3, rDUefZVPfmU_crop_4, rDUefZVPfmU_crop_5, rDUefZVPfmU_crop_6, rDUefZVPfmU_crop_7, rDUefZVPfmU_crop_8, rDUefZVPfmU_crop_9, rDUefZVPfmU_crop_10] for filename in dailymoth_filenames: src_path = os.path.join(src_dir, filename) if os.path.exists(src_path): dailymoth_pathlist.append(src_path) else: print(f"Warning: File not found at {src_path}") with gr.Blocks(title="ASL Video to English Text Translation") as interface: gr.Markdown(f"# ASL Video to English Text Translation\n\n{description}") with gr.Row(): with gr.Column(): video_input = gr.Video(label="ASL Video (under 20 seconds)", format="mp4", height=480, width=640) submit_btn = gr.Button("Translate", variant="primary") with gr.Column(): output_text = gr.Textbox(label="English Translation", lines=3) # Add examples section in the right column if dailymoth_pathlist: # Only show examples if we have valid files gr.Examples( examples=dailymoth_pathlist, inputs=video_input, label="Click a video to try an example" ) # Add attribution note for the examples gr.Markdown(""" --- **Example Videos Attribution:** The example videos used in this demo are from [The Daily Moth](https://www.youtube.com/@TheDailyMoth), a popular ASL news channel made by deaf creators. Specifically, they are from the Previews of [July 10](https://www.youtube.com/watch?v=rDUefZVPfmU) and [July 11](https://www.youtube.com/watch?v=L5hUxT5YbnY) 2025 Top Stories. The videos are only used for illustrative purposes, and all rights to the content belong to The Daily Moth. In this light, we encourage to subscribe to their [channel](https://members.dailymoth.com/about). """) else: gr.Markdown("*No example videos available at this time.*") # Set up the button click handler AFTER both input and output are defined submit_btn.click(fn=process_video, inputs=video_input, outputs=output_text) return interface # Create the demo demo = create_interface() if __name__ == "__main__": # Launch with better configuration for Hugging Face Spaces logger.info("Launching Gradio interface...") demo.launch( server_name="0.0.0.0", server_port=7860, share=False, show_error=True )