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import gradio as gr
from PIL import Image
import yt_dlp
import cv2
import tempfile
import time
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
def get_video_frame(youtube_url, seek_time=0):
# Download een stukje van de video (paar seconden), pak een frame rond seek_time
with tempfile.NamedTemporaryFile(suffix=".mp4") as temp_video:
ydl_opts = {
'outtmpl': temp_video.name,
'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/mp4',
'quiet': True,
'noplaylist': True,
'download_ranges': f"*{seek_time}-{seek_time+1}",
'retries': 3,
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
try:
ydl.download([youtube_url])
except Exception as e:
return None, f"Download error: {str(e)}"
# Pak frame
vidcap = cv2.VideoCapture(temp_video.name)
vidcap.set(cv2.CAP_PROP_POS_MSEC, 500) # Pak frame halverwege het stukje
success, image = vidcap.read()
vidcap.release()
if success:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
pil_image = Image.fromarray(image)
return pil_image, None
else:
return None, "Kon geen frame uitlezen."
def analyse_stream(youtube_url, interval=10, num_frames=3):
results = []
for i in range(num_frames):
seek = i * interval
img, err = get_video_frame(youtube_url, seek)
if err or img is None:
results.append((f"Fout: {err}", None))
continue
# Caption
inputs = processor(images=img, return_tensors="pt")
out = model.generate(**inputs)
caption = processor.decode(out[0], skip_special_tokens=True)
results.append((caption, img))
return results
def gradio_multi(youtube_url):
res = analyse_stream(youtube_url, interval=10, num_frames=3)
texts = [r[0] for r in res]
imgs = [r[1] for r in res]
return texts, imgs
with gr.Blocks() as demo:
gr.Markdown("# 🎥 YouTube livestream analyse (meerdere frames)")
youtube_url = gr.Textbox(label="YouTube URL", value="https://www.youtube.com/watch?v=R5i7aeV8SB8")
run_btn = gr.Button("Analyseer 3 beelden (om de 10 sec)")
output = gr.Dataframe(label="Model antwoorden", headers=["Beschrijving"])
images = gr.Gallery(label="Frames")
run_btn.click(gradio_multi, inputs=youtube_url, outputs=[output, images])
demo.launch()
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