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import tempfile
import cv2
import torch
from pytube import YouTube
@tool
def web_search(query: str) -> str:
"""Performs a DuckDuckGo search for the given query and returns the results.
Args:
query: The search query.
Returns:
The top search results as a string.
"""
try:
if not query or not query.strip():
return "Error: Search query cannot be empty"
search_tool = DuckDuckGoSearchRun()
results = search_tool.invoke(query.strip())
# Clean up the results a bit
if len(results) > 2000: # Truncate very long results
results = results[:2000] + "... (truncated)"
return results
except Exception as e:
return f"Error performing web search: {str(e)}"
@tool
def get_max_bird_species_count_from_video(url: str) -> Dict:
"""
Downloads a YouTube video and returns the maximum number of unique bird species
visible in any frame, along with the timestamp.
Parameters:
url (str): YouTube video URL
Returns:
dict: {
"max_species_count": int,
"timestamp": str,
"species_list": List[str],
}
"""
# 1. Download YouTube video
yt = YouTube(url)
stream = yt.streams.filter(file_extension='mp4').get_highest_resolution()
temp_video_path = os.path.join(tempfile.gettempdir(), "video.mp4")
stream.download(filename=temp_video_path)
# 2. Load object detection model for bird species
# Load a fine-tuned YOLOv5 model or similar pretrained on bird species
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best_birds.pt') # path to your trained model
# 3. Process video frames
cap = cv2.VideoCapture(temp_video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_interval = int(fps * 1) # 1 frame per second
max_species_count = 0
max_species_frame_time = 0
species_at_max = []
frame_idx = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
if frame_idx % frame_interval == 0:
# Run detection
results = model(frame)
detected_species = set()
for *box, conf, cls in results.xyxy[0]:
species_name = model.names[int(cls)]
detected_species.add(species_name)
if len(detected_species) > max_species_count:
max_species_count = len(detected_species)
max_species_frame_time = int(cap.get(cv2.CAP_PROP_POS_MSEC)) // 1000
species_at_max = list(detected_species)
frame_idx += 1
cap.release()
os.remove(temp_video_path)
return {
"max_species_count": max_species_count,
"timestamp": f"{max_species_frame_time}s",
"species_list": species_at_max
}
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