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Update app.py
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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()