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import streamlit as st
from streamlit_webrtc import VideoTransformerBase, webrtc_streamer, RTCConfiguration
from transformers import pipeline
from PIL import Image
import cv2
import numpy as np
import time
# Load TinyLLaVA pipeline once
pipe = pipeline(
task="image-to-text",
model="bczhou/tiny-llava-v1-hf",
trust_remote_code=True,
device_map="cpu"
)
st.set_page_config(page_title="TinyLLaVA Webcam", layout="centered")
st.title("🦙 TinyLLaVA — Webcam Captioning")
# Shared state
st_frame = st.empty()
result_box = st.empty()
class VideoProcessor(VideoTransformerBase):
def __init__(self):
self.last_run = 0
self.interval = 5 # seconds
self.last_caption = ""
def transform(self, frame):
img = frame.to_ndarray(format="bgr24")
now = time.time()
if now - self.last_run > self.interval:
self.last_run = now
# Convert BGR to RGB
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
pil_image = Image.fromarray(img_rgb)
# Run TinyLLaVA pipeline
prompt = "Describe this scene in detail."
query = f"USER: <image>\n{prompt}\nASSISTANT:"
with st.spinner("TinyLLaVA is thinking..."):
result = pipe(query, pil_image)
self.last_caption = result[0]["generated_text"]
# Return the same frame, unmodified
return img
# RTC config
rtc_config = RTCConfiguration(
{"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]}
)
webrtc_ctx = webrtc_streamer(
key="example",
video_processor_factory=VideoProcessor,
rtc_configuration=rtc_config,
media_stream_constraints={"video": True, "audio": False}
)
if webrtc_ctx.video_processor:
st.info("Keep your webcam on. The app captures 1 frame every 5 seconds and generates a caption.")
st.write("Latest Caption:")
st.write(webrtc_ctx.video_processor.last_caption)
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