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import time
import logging
import gradio as gr
import cv2
import os
from pathlib import Path
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
from llama_cpp.llama_chat_format import Llava15ChatHandler
import base64
import gc
# ----------------------------------------
# Model configurations: per-size prefixes and repos
MODELS = {
"256M": {
"model_repo": "mradermacher/SmolVLM2-256M-Video-Instruct-GGUF",
"clip_repo": "ggml-org/SmolVLM2-256M-Video-Instruct-GGUF",
"model_prefix": "SmolVLM2-256M-Video-Instruct",
"clip_prefix": "mmproj-SmolVLM2-256M-Video-Instruct",
"model_variants": ["Q2_K","Q8_0", "f16"],
"clip_variants": ["Q8_0", "f16"],
},
"500M": {
"model_repo": "mradermacher/SmolVLM2-500M-Video-Instruct-GGUF",
"clip_repo": "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF",
"model_prefix": "SmolVLM2-500M-Video-Instruct",
"clip_prefix": "mmproj-SmolVLM2-500M-Video-Instruct",
"model_variants": ["Q2_K","Q8_0", "f16"],
"clip_variants": ["Q8_0", "f16"],
},
"2.2B": {
"model_repo": "mradermacher/SmolVLM2-2.2B-Instruct-GGUF",
"clip_repo": "ggml-org/SmolVLM2-2.2B-Instruct-GGUF",
"model_prefix": "SmolVLM2-2.2B-Instruct",
"clip_prefix": "mmproj-SmolVLM2-2.2B-Instruct",
"model_variants": ["Q2_K","Q4_K_M", "Q8_0", "f16"],
"clip_variants": ["Q8_0", "f16"],
},
}
# ----------------------------------------
# Cache for loaded model instance
model_cache = {
'size': None,
'model_file': None,
'clip_file': None,
'llm': None
}
# Helper to download & symlink weights
def ensure_weights(size, model_file, clip_file):
cfg = MODELS[size]
if not os.path.exists(model_file):
logging.info(f"Downloading model file {model_file} from {cfg['model_repo']}...")
path = hf_hub_download(repo_id=cfg['model_repo'], filename=model_file)
os.symlink(path, model_file)
if not os.path.exists(clip_file):
logging.info(f"Downloading CLIP file {clip_file} from {cfg['clip_repo']}...")
path = hf_hub_download(repo_id=cfg['clip_repo'], filename=clip_file)
os.symlink(path, clip_file)
return model_file, clip_file
# Custom chat handler
class SmolVLM2ChatHandler(Llava15ChatHandler):
CHAT_FORMAT = (
"<|im_start|>"
"{% for message in messages %}"
"{{ message['role'] | capitalize }}"
"{% if message['role']=='user' and message['content'][0]['type']=='image_url' %}:"
"{% else %}: "
"{% endif %}"
"{% for content in message['content'] %}"
"{% if content['type']=='text' %}{{ content['text'] }}"
"{% elif content['type']=='image_url' %}"
"{% if content['image_url'] is string %}"
"{{ content['image_url'] }}\n"
"{% elif content['image_url'] is mapping %}"
"{{ content['image_url']['url'] }}\n"
"{% endif %}"
"{% endif %}"
"{% endfor %}"
"<end_of_utterance>\n"
"{% endfor %}"
"{% if add_generation_prompt %}Assistant:{% endif %}"
)
# Load and cache LLM (only on dropdown change)
def update_llm(size, model_file, clip_file):
if (model_cache['size'], model_cache['model_file'], model_cache['clip_file']) != (size, model_file, clip_file):
mf, cf = ensure_weights(size, model_file, clip_file)
handler = SmolVLM2ChatHandler(clip_model_path=cf, verbose=False)
llm = Llama(model_path=mf, chat_handler=handler, n_ctx=1024,
verbose=False, n_threads=min(2, os.cpu_count()))
model_cache.update({'size': size, 'model_file': mf, 'clip_file': cf, 'llm': llm})
return None # no UI output
# Build weight filename lists
def get_weight_files(size):
cfg = MODELS[size]
model_files = [f"{cfg['model_prefix']}.{v}.gguf" for v in cfg['model_variants']]
clip_files = [f"{cfg['clip_prefix']}-{v}.gguf" for v in cfg['clip_variants']]
return model_files, clip_files
# Caption using cached llm with real-time debug logs
def caption_frame(frame, size, model_file, clip_file, interval_ms, sys_prompt, usr_prompt):
debug_msgs = []
timestamp = time.strftime('%H:%M:%S')
debug_msgs.append(f"[{timestamp}] Received frame shape: {frame.shape}")
t_resize = time.time()
img = cv2.resize(frame.copy(), (384, 384))
elapsed = (time.time() - t_resize) * 1000
timestamp = time.strftime('%H:%M:%S')
debug_msgs.append(f"[{timestamp}] Resized to 384x384 in {elapsed:.1f} ms")
timestamp = time.strftime('%H:%M:%S')
debug_msgs.append(f"[{timestamp}] Sleeping for {interval_ms} ms")
time.sleep(interval_ms / 1000)
t_enc = time.time()
success, jpeg = cv2.imencode('.jpg', img)
elapsed = (time.time() - t_enc) * 1000
timestamp = time.strftime('%H:%M:%S')
debug_msgs.append(f"[{timestamp}] JPEG encode: success={success}, bytes={len(jpeg)} in {elapsed:.1f} ms")
uri = 'data:image/jpeg;base64,' + base64.b64encode(jpeg.tobytes()).decode()
messages = [
{"role": "system", "content": sys_prompt},
{"role": "user", "content": [
{"type": "image_url", "image_url": uri},
{"type": "text", "text": usr_prompt}
]}
]
timestamp = time.strftime('%H:%M:%S')
debug_msgs.append(f"[{timestamp}] Sending prompt of length {len(usr_prompt)} to LLM")
# re-init handler for image
model_cache['llm'].chat_handler = SmolVLM2ChatHandler(clip_model_path=clip_file, verbose=False)
timestamp = time.strftime('%H:%M:%S')
debug_msgs.append(f"[{timestamp}] Reinitialized chat handler")
debug_msgs.append(f"[{timestamp}] CPU count = {os.cpu_count()}")
t_start = time.time()
resp = model_cache['llm'].create_chat_completion(
messages=messages,
max_tokens=128,
temperature=0.1,
stop=["<end_of_utterance>"]
)
elapsed = (time.time() - t_start) * 1000
timestamp = time.strftime('%H:%M:%S')
debug_msgs.append(f"[{timestamp}] LLM response in {elapsed:.1f} ms")
content = resp.get('choices', [{}])[0].get('message', {}).get('content', '').strip()
timestamp = time.strftime('%H:%M:%S')
debug_msgs.append(f"[{timestamp}] Caption length: {len(content)} chars")
gc.collect()
timestamp = time.strftime('%H:%M:%S')
debug_msgs.append(f"[{timestamp}] Garbage collected")
return content, "\n".join(debug_msgs)
# Gradio UI
def main():
logging.basicConfig(level=logging.INFO)
default = '2.2B'
mf, cf = get_weight_files(default)
with gr.Blocks() as demo:
gr.Markdown("## 🎥 Real-Time Camera Captioning with Debug Logs")
with gr.Row():
size_dd = gr.Dropdown(list(MODELS.keys()), value=default, label='Model Size')
model_dd = gr.Dropdown(mf, value=mf[0], label='Decoder Weights')
clip_dd = gr.Dropdown(cf, value=cf[0], label='CLIP Weights')
# When size changes: update dropdowns AND preload llm with the new first weights
def on_size_change(sz):
mlist, clist = get_weight_files(sz)
# update dropdown choices and default values
update_ui = (
gr.update(choices=mlist, value=mlist[0]),
gr.update(choices=clist, value=clist[0])
)
# preload with first weights
update_llm(sz, mlist[0], clist[0])
return update_ui
size_dd.change(
fn=on_size_change,
inputs=[size_dd],
outputs=[model_dd, clip_dd]
)
model_dd.change(lambda sz, mf, cf: update_llm(sz, mf, cf), inputs=[size_dd, model_dd, clip_dd], outputs=[])
clip_dd.change(lambda sz, mf, cf: update_llm(sz, mf, cf), inputs=[size_dd, model_dd, clip_dd], outputs=[])
update_llm(default, mf[0], cf[0])
interval = gr.Slider(100, 20000, step=100, value=3000, label='Interval (ms)')
sys_p = gr.Textbox(lines=2, value="Focus on key dramatic action…", label='System Prompt')
usr_p = gr.Textbox(lines=1, value="What is happening in this image?", label='User Prompt')
cam = gr.Image(sources=['webcam'], streaming=True, label='Webcam Feed')
cap = gr.Textbox(interactive=False, label='Caption')
log_box = gr.Textbox(lines=8, interactive=False, label='Debug Log')
cam.stream(
fn=caption_frame,
inputs=[cam, size_dd, model_dd, clip_dd, interval, sys_p, usr_p],
outputs=[cap, log_box],
time_limit=600
)
demo.launch()
if __name__ == '__main__':
main()
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