Source code for camel.toolkits.reddit_toolkit
# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
# Licensed under the Apache License, Version 2.0 (the “License”);
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an “AS IS” BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
import os
import time
from typing import Any, Dict, List, Union
from requests.exceptions import RequestException
from camel.toolkits import OpenAIFunction
from camel.toolkits.base import BaseToolkit
[docs]
class RedditToolkit(BaseToolkit):
r"""A class representing a toolkit for Reddit operations.
This toolkit provides methods to interact with the Reddit API, allowing
users to collect top posts, perform sentiment analysis on comments, and
track keyword discussions across multiple subreddits.
Attributes:
retries (int): Number of retries for API requests in case of failure.
delay (int): Delay between retries in seconds.
reddit (Reddit): An instance of the Reddit client.
"""
def __init__(self, retries: int = 3, delay: int = 0):
r"""Initializes the RedditToolkit with the specified number of retries
and delay.
Args:
retries (int): Number of times to retry the request in case of
failure. Defaults to `3`.
delay (int): Time in seconds to wait between retries. Defaults to
`0`.
"""
from praw import Reddit # type: ignore[import-untyped]
self.retries = retries
self.delay = delay
self.client_id = os.environ.get("REDDIT_CLIENT_ID", "")
self.client_secret = os.environ.get("REDDIT_CLIENT_SECRET", "")
self.user_agent = os.environ.get("REDDIT_USER_AGENT", "")
self.reddit = Reddit(
client_id=self.client_id,
client_secret=self.client_secret,
user_agent=self.user_agent,
request_timeout=30, # Set a timeout to handle delays
)
def _retry_request(self, func, *args, **kwargs):
r"""Retries a function in case of network-related errors.
Args:
func (callable): The function to be retried.
*args: Arguments to pass to the function.
**kwargs: Keyword arguments to pass to the function.
Returns:
Any: The result of the function call if successful.
Raises:
RequestException: If all retry attempts fail.
"""
for attempt in range(self.retries):
try:
return func(*args, **kwargs)
except RequestException as e:
print(f"Attempt {attempt + 1}/{self.retries} failed: {e}")
if attempt < self.retries - 1:
time.sleep(self.delay)
else:
raise
[docs]
def collect_top_posts(
self,
subreddit_name: str,
post_limit: int = 5,
comment_limit: int = 5,
) -> Union[List[Dict[str, Any]], str]:
r"""Collects the top posts and their comments from a specified
subreddit.
Args:
subreddit_name (str): The name of the subreddit to collect posts
from.
post_limit (int): The maximum number of top posts to collect.
Defaults to `5`.
comment_limit (int): The maximum number of top comments to collect
per post. Defaults to `5`.
Returns:
Union[List[Dict[str, Any]], str]: A list of dictionaries, each
containing the post title and its top comments if success.
String warming if credentials are not set.
"""
if not all([self.client_id, self.client_secret, self.user_agent]):
return (
"Reddit API credentials are not set. "
"Please set the environment variables."
)
subreddit = self._retry_request(self.reddit.subreddit, subreddit_name)
top_posts = self._retry_request(subreddit.top, limit=post_limit)
data = []
for post in top_posts:
post_data = {
"Post Title": post.title,
"Comments": [
{"Comment Body": comment.body, "Upvotes": comment.score}
for comment in self._retry_request(
lambda post=post: list(post.comments)
)[:comment_limit]
],
}
data.append(post_data)
time.sleep(self.delay) # Add a delay to avoid hitting rate limits
return data
[docs]
def perform_sentiment_analysis(
self, data: List[Dict[str, Any]]
) -> List[Dict[str, Any]]:
r"""Performs sentiment analysis on the comments collected from Reddit
posts.
Args:
data (List[Dict[str, Any]]): A list of dictionaries containing
Reddit post data and comments.
Returns:
List[Dict[str, Any]]: The original data with an added 'Sentiment
Score' for each comment.
"""
from textblob import TextBlob
for item in data:
# Sentiment analysis should be done on 'Comment Body'
item["Sentiment Score"] = TextBlob(
item["Comment Body"]
).sentiment.polarity
return data
[docs]
def track_keyword_discussions(
self,
subreddits: List[str],
keywords: List[str],
post_limit: int = 10,
comment_limit: int = 10,
sentiment_analysis: bool = False,
) -> Union[List[Dict[str, Any]], str]:
r"""Tracks discussions about specific keywords in specified subreddits.
Args:
subreddits (List[str]): A list of subreddit names to search within.
keywords (List[str]): A list of keywords to track in the subreddit
discussions.
post_limit (int): The maximum number of top posts to collect per
subreddit. Defaults to `10`.
comment_limit (int): The maximum number of top comments to collect
per post. Defaults to `10`.
sentiment_analysis (bool): If True, performs sentiment analysis on
the comments. Defaults to `False`.
Returns:
Union[List[Dict[str, Any]], str]: A list of dictionaries
containing the subreddit name, post title, comment body, and
upvotes for each comment that contains the specified keywords
if success. String warming if credentials are not set.
"""
if not all([self.client_id, self.client_secret, self.user_agent]):
return (
"Reddit API credentials are not set. "
"Please set the environment variables."
)
data = []
for subreddit_name in subreddits:
subreddit = self._retry_request(
self.reddit.subreddit, subreddit_name
)
top_posts = self._retry_request(subreddit.top, limit=post_limit)
for post in top_posts:
for comment in self._retry_request(
lambda post=post: list(post.comments)
)[:comment_limit]:
# Print comment body for debugging
if any(
keyword.lower() in comment.body.lower()
for keyword in keywords
):
comment_data = {
"Subreddit": subreddit_name,
"Post Title": post.title,
"Comment Body": comment.body,
"Upvotes": comment.score,
}
data.append(comment_data)
# Add a delay to avoid hitting rate limits
time.sleep(self.delay)
if sentiment_analysis:
data = self.perform_sentiment_analysis(data)
return data
[docs]
def get_tools(self) -> List[OpenAIFunction]:
r"""Returns a list of OpenAIFunction objects representing the
functions in the toolkit.
Returns:
List[OpenAIFunction]: A list of OpenAIFunction objects for the
toolkit methods.
"""
return [
OpenAIFunction(self.collect_top_posts),
OpenAIFunction(self.perform_sentiment_analysis),
OpenAIFunction(self.track_keyword_discussions),
]