import spaces import subprocess # Install flash attention, skipping CUDA build if necessary subprocess.run( "pip install flash-attn --no-build-isolation", env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, shell=True, ) import time import logging import gradio as gr import cv2 import os from transformers import AutoProcessor, AutoModelForImageTextToText import torch from PIL import Image import numpy as np from pathlib import Path # Cache for loaded model and processor default_cache = {'model_id': None, 'processor': None, 'model': None, 'device': None} model_cache = default_cache.copy() # Check for XPU availability has_xpu = hasattr(torch, 'xpu') and torch.xpu.is_available() def update_model(model_id, device): if model_cache['model_id'] != model_id or model_cache['device'] != device: logging.info(f'Loading model {model_id} on {device}') try: processor = AutoProcessor.from_pretrained(model_id) # Load model with appropriate precision for each device if device == 'cuda': # Use bfloat16 for CUDA for performance model = AutoModelForImageTextToText.from_pretrained( model_id, torch_dtype=torch.bfloat16, _attn_implementation='flash_attention_2' ).to('cuda') elif device == 'xpu' and has_xpu: # Use float32 on XPU to avoid bfloat16 layernorm issues model = AutoModelForImageTextToText.from_pretrained( model_id, torch_dtype=torch.float32 ).to('xpu') else: # Default to float32 on CPU model = AutoModelForImageTextToText.from_pretrained(model_id).to('cpu') model.eval() model_cache.update({'model_id': model_id, 'processor': processor, 'model': model, 'device': device}) except Exception as e: logging.error(f'Error loading model: {e}') raise e def extract_frames_from_video(video_path, max_frames=10): """Extract frames from video file for processing""" if not os.path.exists(video_path): raise FileNotFoundError(f"Video file not found: {video_path}") # Validate video file if not video_path.lower().endswith(('.mp4', '.avi', '.mov', '.mkv', '.webm')): raise ValueError("Unsupported video format. Please use MP4, AVI, MOV, MKV, or WEBM.") cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise ValueError(f"Cannot open video file: {video_path}") frames = [] frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) if frame_count == 0: cap.release() raise ValueError("Video file appears to be empty or corrupted") # Calculate step size to extract evenly distributed frames step = max(1, frame_count // max_frames) frame_idx = 0 while cap.isOpened(): ret, frame = cap.read() if not ret: break if frame_idx % step == 0: frames.append(frame) if len(frames) >= max_frames: break frame_idx += 1 cap.release() return frames @spaces.GPU def caption_frame(frame, model_id, interval_ms, sys_prompt, usr_prompt, device): """Caption a single frame (used for webcam streaming)""" debug_msgs = [] try: update_model(model_id, device) processor = model_cache['processor'] model = model_cache['model'] # Control capture interval time.sleep(interval_ms / 1000) # Preprocess frame t0 = time.time() rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) pil_img = Image.fromarray(rgb) temp_path = 'frame.jpg' pil_img.save(temp_path, format='JPEG', quality=50) debug_msgs.append(f'Preprocess: {int((time.time()-t0)*1000)} ms') # Prepare multimodal chat messages messages = [ {'role': 'system', 'content': [{'type': 'text', 'text': sys_prompt}]}, {'role': 'user', 'content': [ {'type': 'image', 'url': temp_path}, {'type': 'text', 'text': usr_prompt} ]} ] # Tokenize and encode t1 = time.time() inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors='pt' ) # Move inputs to correct device and dtype (matching model parameters) param_dtype = next(model.parameters()).dtype cast_inputs = {} for k, v in inputs.items(): if isinstance(v, torch.Tensor): if v.dtype.is_floating_point: # cast floating-point tensors to model's parameter dtype cast_inputs[k] = v.to(device=model.device, dtype=param_dtype) else: # move integer/mask tensors without changing dtype cast_inputs[k] = v.to(device=model.device) else: cast_inputs[k] = v inputs = cast_inputs debug_msgs.append(f'Tokenize: {int((time.time()-t1)*1000)} ms') # Inference t2 = time.time() outputs = model.generate(**inputs, do_sample=False, max_new_tokens=128) debug_msgs.append(f'Inference: {int((time.time()-t2)*1000)} ms') # Decode and strip history t3 = time.time() raw = processor.batch_decode(outputs, skip_special_tokens=True)[0] debug_msgs.append(f'Decode: {int((time.time()-t3)*1000)} ms') if "Assistant:" in raw: caption = raw.split("Assistant:")[-1].strip() else: lines = raw.splitlines() caption = lines[-1].strip() if len(lines) > 1 else raw.strip() # Clean up temp file if os.path.exists(temp_path): os.remove(temp_path) return caption, '\n'.join(debug_msgs) except Exception as e: return f"Error: {str(e)}", '\n'.join(debug_msgs) @spaces.GPU def process_video_file(video_file, model_id, sys_prompt, usr_prompt, device, max_frames): """Process uploaded video file and return captions for multiple frames""" if video_file is None: return "No video file uploaded", "" debug_msgs = [] temp_files = [] # Track temporary files for cleanup try: update_model(model_id, device) processor = model_cache['processor'] model = model_cache['model'] # Extract frames from video t0 = time.time() frames = extract_frames_from_video(video_file, max_frames) debug_msgs.append(f'Extracted {len(frames)} frames in {int((time.time()-t0)*1000)} ms') if not frames: return "No frames could be extracted from the video", '\n'.join(debug_msgs) captions = [] for i, frame in enumerate(frames): # Preprocess frame t1 = time.time() rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) pil_img = Image.fromarray(rgb) temp_path = f'frame_{i}.jpg' temp_files.append(temp_path) # Track for cleanup pil_img.save(temp_path, format='JPEG', quality=50) # Prepare multimodal chat messages messages = [ {'role': 'system', 'content': [{'type': 'text', 'text': sys_prompt}]}, {'role': 'user', 'content': [ {'type': 'image', 'url': temp_path}, {'type': 'text', 'text': usr_prompt} ]} ] # Tokenize and encode inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors='pt' ) # Move inputs to correct device and dtype param_dtype = next(model.parameters()).dtype cast_inputs = {} for k, v in inputs.items(): if isinstance(v, torch.Tensor): if v.dtype.is_floating_point: cast_inputs[k] = v.to(device=model.device, dtype=param_dtype) else: cast_inputs[k] = v.to(device=model.device) else: cast_inputs[k] = v inputs = cast_inputs # Inference outputs = model.generate(**inputs, do_sample=False, max_new_tokens=128) # Decode and strip history raw = processor.batch_decode(outputs, skip_special_tokens=True)[0] if "Assistant:" in raw: caption = raw.split("Assistant:")[-1].strip() else: lines = raw.splitlines() caption = lines[-1].strip() if len(lines) > 1 else raw.strip() captions.append(f"Frame {i+1}: {caption}") debug_msgs.append(f'Frame {i+1} processed in {int((time.time()-t1)*1000)} ms') return '\n\n'.join(captions), '\n'.join(debug_msgs) except Exception as e: return f"Error processing video: {str(e)}", '\n'.join(debug_msgs) finally: # Clean up all temporary files for temp_file in temp_files: if os.path.exists(temp_file): try: os.remove(temp_file) except Exception as cleanup_error: logging.warning(f"Failed to cleanup {temp_file}: {cleanup_error}") def toggle_input_mode(input_mode): """Toggle between webcam and video file input""" if input_mode == "Webcam": return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False) else: # Video File return gr.update(visible=False), gr.update(visible=True), gr.update(visible=True) def main(): logging.basicConfig(level=logging.INFO) model_choices = [ 'HuggingFaceTB/SmolVLM2-256M-Video-Instruct', 'HuggingFaceTB/SmolVLM2-500M-Video-Instruct', 'HuggingFaceTB/SmolVLM2-2.2B-Instruct' ] # Determine available devices device_options = ['cpu'] if torch.cuda.is_available(): device_options.append('cuda') if has_xpu: device_options.append('xpu') default_device = 'cuda' if torch.cuda.is_available() else ('xpu' if has_xpu else 'cpu') with gr.Blocks() as demo: gr.Markdown('## 🎥 Real-Time Webcam & Video File Captioning with SmolVLM2 (Transformers)') with gr.Row(): input_mode = gr.Radio( choices=["Webcam", "Video File"], value="Webcam", label="Input Mode" ) with gr.Row(): model_dd = gr.Dropdown(model_choices, value=model_choices[0], label='Model ID') device_dd = gr.Dropdown(device_options, value=default_device, label='Device') # Webcam-specific controls with gr.Row() as webcam_controls: interval = gr.Slider(100, 20000, step=100, value=3000, label='Interval (ms)') # Video file-specific controls with gr.Row(visible=False) as video_controls: max_frames = gr.Slider(1, 20, step=1, value=5, label='Max Frames to Process') sys_p = gr.Textbox(lines=2, value='Describe the key action', label='System Prompt') usr_p = gr.Textbox(lines=1, value='What is happening in this image?', label='User Prompt') # Input components cam = gr.Image(sources=['webcam'], streaming=True, label='Webcam Feed') video_file = gr.File( label="Upload Video File", file_types=[".mp4", ".avi", ".mov", ".mkv", ".webm"], visible=False ) # Process button for video files process_btn = gr.Button("Process Video", visible=False) # Output components caption_tb = gr.Textbox(interactive=False, label='Caption') log_tb = gr.Textbox(lines=4, interactive=False, label='Debug Log') # Toggle input mode input_mode.change( fn=toggle_input_mode, inputs=[input_mode], outputs=[cam, video_file, process_btn] ) # Also toggle the control panels input_mode.change( fn=lambda mode: (gr.update(visible=mode=="Webcam"), gr.update(visible=mode=="Video File")), inputs=[input_mode], outputs=[webcam_controls, video_controls] ) # Webcam streaming cam.stream( fn=caption_frame, inputs=[cam, model_dd, interval, sys_p, usr_p, device_dd], outputs=[caption_tb, log_tb], time_limit=600 ) # Video file processing process_btn.click( fn=process_video_file, inputs=[video_file, model_dd, sys_p, usr_p, device_dd, max_frames], outputs=[caption_tb, log_tb] ) # Enable Gradio's async event queue demo.queue() # Launch the app demo.launch() if __name__ == '__main__': main()