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)) fps = cap.get(cv2.CAP_PROP_FPS) 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: # Calculate timestamp for this frame timestamp = frame_idx / fps if fps > 0 else frame_idx frames.append((frame, timestamp)) if len(frames) >= max_frames: break frame_idx += 1 cap.release() return frames, fps @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) def process_single_frame(frame, model_id, sys_prompt, usr_prompt, device, frame_id=0): """Process a single frame similar to webcam mode - optimized for reuse""" debug_msgs = [] temp_path = None try: # Ensure model is loaded update_model(model_id, device) processor = model_cache['processor'] model = model_cache['model'] # Preprocess frame t0 = time.time() rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) pil_img = Image.fromarray(rgb) temp_path = f'video_frame_{frame_id}.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_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 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() return caption, debug_msgs, None except Exception as e: return f"Error: {str(e)}", debug_msgs, str(e) finally: # Clean up temp file if temp_path and os.path.exists(temp_path): try: os.remove(temp_path) except Exception as cleanup_error: logging.warning(f"Failed to cleanup {temp_path}: {cleanup_error}") @spaces.GPU def process_video_with_interval(video_file, model_id, sys_prompt, usr_prompt, device, max_frames, interval_ms): """Process video file with interval-based processing similar to webcam mode""" if video_file is None: return "No video file uploaded", "" debug_msgs = [] all_captions = [] try: # Extract frames from video t0 = time.time() frames_with_timestamps, fps = extract_frames_from_video(video_file, max_frames) debug_msgs.append(f'Extracted {len(frames_with_timestamps)} frames in {int((time.time()-t0)*1000)} ms') debug_msgs.append(f'Video FPS: {fps:.2f}') if not frames_with_timestamps: return "No frames could be extracted from the video", '\n'.join(debug_msgs) # Process each frame with interval delay (similar to webcam mode) for i, (frame, timestamp) in enumerate(frames_with_timestamps): # Apply interval delay (similar to webcam mode) if i > 0: # Don't delay the first frame time.sleep(interval_ms / 1000) # Process frame using the same logic as webcam mode caption, frame_debug_msgs, error = process_single_frame( frame, model_id, sys_prompt, usr_prompt, device, frame_id=i ) # Add timing information timestamp_str = f"{timestamp:.2f}s" if error: all_captions.append(f"Frame {i+1} (t={timestamp_str}): ERROR - {error}") else: all_captions.append(f"Frame {i+1} (t={timestamp_str}): {caption}") # Add frame-specific debug info debug_msgs.extend([f"Frame {i+1}: {msg}" for msg in frame_debug_msgs]) return '\n\n'.join(all_captions), '\n'.join(debug_msgs) except Exception as e: return f"Error processing video: {str(e)}", '\n'.join(debug_msgs) 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: interval_video = gr.Slider(100, 10000, step=100, value=1000, label='Processing Interval (ms)') 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_with_interval, inputs=[video_file, model_dd, sys_p, usr_p, device_dd, max_frames, interval_video], outputs=[caption_tb, log_tb] ) # Enable Gradio's async event queue demo.queue() # Launch the app demo.launch() if __name__ == '__main__': main()