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style: run python linter & prettifier on backend code

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This commit is contained in:
Dylan De Faoite
2026-03-25 19:34:43 +00:00
parent aae10c4d9d
commit 376773a0cc
17 changed files with 408 additions and 315 deletions
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68
server/analysis/cultural.py
View File

@@ -15,7 +15,8 @@ class CulturalAnalysis:
emotion_exclusions = {"emotion_neutral", "emotion_surprise"}
emotion_cols = [
c for c in df.columns
c
for c in df.columns
if c.startswith("emotion_") and c not in emotion_exclusions
]
@@ -40,7 +41,6 @@ class CulturalAnalysis:
"out_group_usage": out_count,
"in_group_ratio": round(in_count / max(total_tokens, 1), 5),
"out_group_ratio": round(out_count / max(total_tokens, 1), 5),
"in_group_posts": int(in_mask.sum()),
"out_group_posts": int(out_mask.sum()),
"tie_posts": int(tie_mask.sum()),
@@ -49,20 +49,34 @@ class CulturalAnalysis:
if emotion_cols:
emo = df[emotion_cols].apply(pd.to_numeric, errors="coerce").fillna(0.0)
in_avg = emo.loc[in_mask].mean() if in_mask.any() else pd.Series(0.0, index=emotion_cols)
out_avg = emo.loc[out_mask].mean() if out_mask.any() else pd.Series(0.0, index=emotion_cols)
in_avg = (
emo.loc[in_mask].mean()
if in_mask.any()
else pd.Series(0.0, index=emotion_cols)
)
out_avg = (
emo.loc[out_mask].mean()
if out_mask.any()
else pd.Series(0.0, index=emotion_cols)
)
result["in_group_emotion_avg"] = in_avg.to_dict()
result["out_group_emotion_avg"] = out_avg.to_dict()
return result
def get_stance_markers(self, df: pd.DataFrame) -> dict[str, Any]:
s = df[self.content_col].fillna("").astype(str)
hedge_pattern = re.compile(r"\b(maybe|perhaps|possibly|probably|likely|seems|seem|i think|i feel|i guess|kind of|sort of|somewhat)\b")
certainty_pattern = re.compile(r"\b(definitely|certainly|clearly|obviously|undeniably|always|never)\b")
deontic_pattern = re.compile(r"\b(must|should|need|needs|have to|has to|ought|required|require)\b")
hedge_pattern = re.compile(
r"\b(maybe|perhaps|possibly|probably|likely|seems|seem|i think|i feel|i guess|kind of|sort of|somewhat)\b"
)
certainty_pattern = re.compile(
r"\b(definitely|certainly|clearly|obviously|undeniably|always|never)\b"
)
deontic_pattern = re.compile(
r"\b(must|should|need|needs|have to|has to|ought|required|require)\b"
)
permission_pattern = re.compile(r"\b(can|allowed|okay|ok|permitted)\b")
hedge_counts = s.str.count(hedge_pattern)
@@ -70,20 +84,32 @@ class CulturalAnalysis:
deontic_counts = s.str.count(deontic_pattern)
perm_counts = s.str.count(permission_pattern)
token_counts = s.apply(lambda t: len(re.findall(r"\b[a-z]{2,}\b", t))).replace(0, 1)
token_counts = s.apply(lambda t: len(re.findall(r"\b[a-z]{2,}\b", t))).replace(
0, 1
)
return {
"hedge_total": int(hedge_counts.sum()),
"certainty_total": int(certainty_counts.sum()),
"deontic_total": int(deontic_counts.sum()),
"permission_total": int(perm_counts.sum()),
"hedge_per_1k_tokens": round(1000 * hedge_counts.sum() / token_counts.sum(), 3),
"certainty_per_1k_tokens": round(1000 * certainty_counts.sum() / token_counts.sum(), 3),
"deontic_per_1k_tokens": round(1000 * deontic_counts.sum() / token_counts.sum(), 3),
"permission_per_1k_tokens": round(1000 * perm_counts.sum() / token_counts.sum(), 3),
"hedge_per_1k_tokens": round(
1000 * hedge_counts.sum() / token_counts.sum(), 3
),
"certainty_per_1k_tokens": round(
1000 * certainty_counts.sum() / token_counts.sum(), 3
),
"deontic_per_1k_tokens": round(
1000 * deontic_counts.sum() / token_counts.sum(), 3
),
"permission_per_1k_tokens": round(
1000 * perm_counts.sum() / token_counts.sum(), 3
),
}
def get_avg_emotions_per_entity(self, df: pd.DataFrame, top_n: int = 25, min_posts: int = 10) -> dict[str, Any]:
def get_avg_emotions_per_entity(
self, df: pd.DataFrame, top_n: int = 25, min_posts: int = 10
) -> dict[str, Any]:
if "ner_entities" not in df.columns:
return {"entity_emotion_avg": {}}
@@ -92,9 +118,13 @@ class CulturalAnalysis:
entity_df = df[["ner_entities"] + emotion_cols].explode("ner_entities")
entity_df["entity_text"] = entity_df["ner_entities"].apply(
lambda e: e.get("text").strip()
if isinstance(e, dict) and isinstance(e.get("text"), str) and len(e.get("text")) >= 3
else None
lambda e: (
e.get("text").strip()
if isinstance(e, dict)
and isinstance(e.get("text"), str)
and len(e.get("text")) >= 3
else None
)
)
entity_df = entity_df.dropna(subset=["entity_text"])
@@ -114,4 +144,4 @@ class CulturalAnalysis:
"emotion_avg": emo_means,
}
return {"entity_emotion_avg": entity_emotion_avg}
return {"entity_emotion_avg": entity_emotion_avg}

15
server/analysis/enrichment.py
View File

@@ -2,6 +2,7 @@ import pandas as pd
from server.analysis.nlp import NLP
class DatasetEnrichment:
def __init__(self, df: pd.DataFrame, topics: dict):
self.df = self._explode_comments(df)
@@ -10,7 +11,9 @@ class DatasetEnrichment:
def _explode_comments(self, df) -> pd.DataFrame:
comments_df = df[["id", "comments"]].explode("comments")
comments_df = comments_df[comments_df["comments"].apply(lambda x: isinstance(x, dict))]
comments_df = comments_df[
comments_df["comments"].apply(lambda x: isinstance(x, dict))
]
comments_df = pd.json_normalize(comments_df["comments"])
posts_df = df.drop(columns=["comments"])
@@ -24,16 +27,16 @@ class DatasetEnrichment:
df.drop(columns=["post_id"], inplace=True, errors="ignore")
return df
def enrich(self) -> pd.DataFrame:
self.df['timestamp'] = pd.to_numeric(self.df['timestamp'], errors='raise')
self.df['date'] = pd.to_datetime(self.df['timestamp'], unit='s').dt.date
self.df["timestamp"] = pd.to_numeric(self.df["timestamp"], errors="raise")
self.df["date"] = pd.to_datetime(self.df["timestamp"], unit="s").dt.date
self.df["dt"] = pd.to_datetime(self.df["timestamp"], unit="s", utc=True)
self.df["hour"] = self.df["dt"].dt.hour
self.df["weekday"] = self.df["dt"].dt.day_name()
self.nlp.add_emotion_cols()
self.nlp.add_topic_col()
self.nlp.add_ner_cols()
return self.df
return self.df

15
server/analysis/interactional.py
View File

@@ -1,6 +1,7 @@
import pandas as pd
import re
class InteractionAnalysis:
def __init__(self, word_exclusions: set[str]):
self.word_exclusions = word_exclusions
@@ -51,7 +52,7 @@ class InteractionAnalysis:
return 0
return round(sum(depths) / len(depths), 2)
def top_interaction_pairs(self, df: pd.DataFrame, top_n=10):
graph = self.interaction_graph(df)
pairs = []
@@ -62,7 +63,7 @@ class InteractionAnalysis:
pairs.sort(key=lambda x: x[1], reverse=True)
return pairs[:top_n]
def conversation_concentration(self, df: pd.DataFrame) -> dict:
if "type" not in df.columns:
return {}
@@ -76,12 +77,16 @@ class InteractionAnalysis:
total_authors = len(author_counts)
top_10_pct_n = max(1, int(total_authors * 0.1))
top_10_pct_share = round(author_counts.head(top_10_pct_n).sum() / total_comments, 4)
top_10_pct_share = round(
author_counts.head(top_10_pct_n).sum() / total_comments, 4
)
return {
"total_commenting_authors": total_authors,
"top_10pct_author_count": top_10_pct_n,
"top_10pct_comment_share": float(top_10_pct_share),
"single_comment_authors": int((author_counts == 1).sum()),
"single_comment_author_ratio": float(round((author_counts == 1).sum() / total_authors, 4)),
}
"single_comment_author_ratio": float(
round((author_counts == 1).sum() / total_authors, 4)
),
}

5
server/analysis/linguistic.py
View File

@@ -64,7 +64,10 @@ class LinguisticAnalysis:
def lexical_diversity(self, df: pd.DataFrame) -> dict:
tokens = (
df["content"].fillna("").astype(str).str.lower()
df["content"]
.fillna("")
.astype(str)
.str.lower()
.str.findall(r"\b[a-z]{2,}\b")
.explode()
)

18
server/analysis/nlp.py
View File

@@ -6,6 +6,7 @@ from typing import Any
from transformers import pipeline
from sentence_transformers import SentenceTransformer
class NLP:
_topic_models: dict[str, SentenceTransformer] = {}
_emotion_classifiers: dict[str, Any] = {}
@@ -32,7 +33,7 @@ class NLP:
)
self.entity_recognizer = self._get_entity_recognizer(
self.device_str, self.pipeline_device
)
)
except RuntimeError as exc:
if self.use_cuda and "out of memory" in str(exc).lower():
torch.cuda.empty_cache()
@@ -90,7 +91,7 @@ class NLP:
)
cls._emotion_classifiers[device_str] = classifier
return classifier
@classmethod
def _get_entity_recognizer(cls, device_str: str, pipeline_device: int) -> Any:
recognizer = cls._entity_recognizers.get(device_str)
@@ -207,8 +208,7 @@ class NLP:
self.df.drop(columns=existing_drop, inplace=True)
remaining_emotion_cols = [
c for c in self.df.columns
if c.startswith("emotion_")
c for c in self.df.columns if c.startswith("emotion_")
]
if remaining_emotion_cols:
@@ -227,8 +227,6 @@ class NLP:
self.df[remaining_emotion_cols] = normalized.values
def add_topic_col(self, confidence_threshold: float = 0.3) -> None:
titles = self.df[self.title_col].fillna("").astype(str)
contents = self.df[self.content_col].fillna("").astype(str)
@@ -257,7 +255,7 @@ class NLP:
self.df.loc[self.df["topic_confidence"] < confidence_threshold, "topic"] = (
"Misc"
)
def add_ner_cols(self, max_chars: int = 512) -> None:
texts = (
self.df[self.content_col]
@@ -302,8 +300,4 @@ class NLP:
for label in all_labels:
col_name = f"entity_{label}"
self.df[col_name] = [
d.get(label, 0) for d in entity_count_dicts
]
self.df[col_name] = [d.get(label, 0) for d in entity_count_dicts]

71
server/analysis/user.py
View File

@@ -3,6 +3,7 @@ import re
from collections import Counter
class UserAnalysis:
def __init__(self, word_exclusions: set[str]):
self.word_exclusions = word_exclusions
@@ -12,49 +13,49 @@ class UserAnalysis:
return [t for t in tokens if t not in self.word_exclusions]
def _vocab_richness_per_user(
self, df: pd.DataFrame, min_words: int = 20, top_most_used_words: int = 100
) -> list:
df = df.copy()
df["content"] = df["content"].fillna("").astype(str).str.lower()
df["tokens"] = df["content"].apply(self._tokenize)
self, df: pd.DataFrame, min_words: int = 20, top_most_used_words: int = 100
) -> list:
df = df.copy()
df["content"] = df["content"].fillna("").astype(str).str.lower()
df["tokens"] = df["content"].apply(self._tokenize)
rows = []
for author, group in df.groupby("author"):
all_tokens = [t for tokens in group["tokens"] for t in tokens]
rows = []
for author, group in df.groupby("author"):
all_tokens = [t for tokens in group["tokens"] for t in tokens]
total_words = len(all_tokens)
unique_words = len(set(all_tokens))
events = len(group)
total_words = len(all_tokens)
unique_words = len(set(all_tokens))
events = len(group)
# Min amount of words for a user, any less than this might give weird results
if total_words < min_words:
continue
# Min amount of words for a user, any less than this might give weird results
if total_words < min_words:
continue
# 100% = they never reused a word (excluding stop words)
vocab_richness = unique_words / total_words
avg_words = total_words / max(events, 1)
# 100% = they never reused a word (excluding stop words)
vocab_richness = unique_words / total_words
avg_words = total_words / max(events, 1)
counts = Counter(all_tokens)
top_words = [
{"word": w, "count": int(c)}
for w, c in counts.most_common(top_most_used_words)
]
counts = Counter(all_tokens)
top_words = [
{"word": w, "count": int(c)}
for w, c in counts.most_common(top_most_used_words)
]
rows.append(
{
"author": author,
"events": int(events),
"total_words": int(total_words),
"unique_words": int(unique_words),
"vocab_richness": round(vocab_richness, 3),
"avg_words_per_event": round(avg_words, 2),
"top_words": top_words,
}
)
rows.append(
{
"author": author,
"events": int(events),
"total_words": int(total_words),
"unique_words": int(unique_words),
"vocab_richness": round(vocab_richness, 3),
"avg_words_per_event": round(avg_words, 2),
"top_words": top_words,
}
)
rows = sorted(rows, key=lambda x: x["vocab_richness"], reverse=True)
rows = sorted(rows, key=lambda x: x["vocab_richness"], reverse=True)
return rows
return rows
def top_users(self, df: pd.DataFrame) -> list:
counts = df.groupby(["author", "source"]).size().sort_values(ascending=False)

100
server/app.py
View File

@@ -30,7 +30,9 @@ load_dotenv()
max_fetch_limit = int(get_env("MAX_FETCH_LIMIT"))
frontend_url = get_env("FRONTEND_URL")
jwt_secret_key = get_env("JWT_SECRET_KEY")
jwt_access_token_expires = int(os.getenv("JWT_ACCESS_TOKEN_EXPIRES", 1200)) # Default to 20 minutes
jwt_access_token_expires = int(
os.getenv("JWT_ACCESS_TOKEN_EXPIRES", 1200)
) # Default to 20 minutes
# Flask Configuration
CORS(app, resources={r"/*": {"origins": frontend_url}})
@@ -52,6 +54,7 @@ connectors = get_available_connectors()
with open("server/topics.json") as f:
default_topic_list = json.load(f)
@app.route("/register", methods=["POST"])
def register_user():
data = request.get_json()
@@ -107,9 +110,13 @@ def login_user():
def profile():
current_user = get_jwt_identity()
return jsonify(
message="Access granted", user=auth_manager.get_user_by_id(current_user)
), 200
return (
jsonify(
message="Access granted", user=auth_manager.get_user_by_id(current_user)
),
200,
)
@app.route("/user/datasets")
@jwt_required()
@@ -117,11 +124,13 @@ def get_user_datasets():
current_user = int(get_jwt_identity())
return jsonify(dataset_manager.get_user_datasets(current_user)), 200
@app.route("/datasets/sources", methods=["GET"])
def get_dataset_sources():
list_metadata = list(get_connector_metadata().values())
return jsonify(list_metadata)
@app.route("/datasets/scrape", methods=["POST"])
@jwt_required()
def scrape_data():
@@ -160,7 +169,7 @@ def scrape_data():
limit = int(limit)
except (ValueError, TypeError):
return jsonify({"error": "Limit must be an integer"}), 400
if limit > 1000:
limit = 1000
@@ -172,15 +181,13 @@ def scrape_data():
if category and not connector_metadata[name]["categories_enabled"]:
return jsonify({"error": f"Source {name} does not support categories"}), 400
if category and not connectors[name]().category_exists(category):
return jsonify({"error": f"Category does not exist for {name}"}), 400
try:
dataset_id = dataset_manager.save_dataset_info(
user_id,
dataset_name,
default_topic_list
user_id, dataset_name, default_topic_list
)
dataset_manager.set_dataset_status(
@@ -189,22 +196,21 @@ def scrape_data():
f"Data is being fetched from {', '.join(source['name'] for source in source_configs)}",
)
fetch_and_process_dataset.delay(
dataset_id,
source_configs,
default_topic_list
)
fetch_and_process_dataset.delay(dataset_id, source_configs, default_topic_list)
except Exception:
print(traceback.format_exc())
return jsonify({"error": "Failed to queue dataset processing"}), 500
return jsonify(
{
"message": "Dataset queued for processing",
"dataset_id": dataset_id,
"status": "processing",
}
), 202
return (
jsonify(
{
"message": "Dataset queued for processing",
"dataset_id": dataset_id,
"status": "processing",
}
),
202,
)
@app.route("/datasets/upload", methods=["POST"])
@@ -226,9 +232,12 @@ def upload_data():
if not post_file.filename.endswith(".jsonl") or not topic_file.filename.endswith(
".json"
):
return jsonify(
{"error": "Invalid file type. Only .jsonl and .json files are allowed."}
), 400
return (
jsonify(
{"error": "Invalid file type. Only .jsonl and .json files are allowed."}
),
400,
)
try:
current_user = int(get_jwt_identity())
@@ -241,13 +250,16 @@ def upload_data():
process_dataset.delay(dataset_id, posts_df.to_dict(orient="records"), topics)
return jsonify(
{
"message": "Dataset queued for processing",
"dataset_id": dataset_id,
"status": "processing",
}
), 202
return (
jsonify(
{
"message": "Dataset queued for processing",
"dataset_id": dataset_id,
"status": "processing",
}
),
202,
)
except ValueError as e:
return jsonify({"error": f"Failed to read JSONL file"}), 400
except Exception as e:
@@ -296,9 +308,12 @@ def update_dataset(dataset_id):
return jsonify({"error": "A valid name must be provided"}), 400
dataset_manager.update_dataset_name(dataset_id, new_name.strip())
return jsonify(
{"message": f"Dataset {dataset_id} renamed to '{new_name.strip()}'"}
), 200
return (
jsonify(
{"message": f"Dataset {dataset_id} renamed to '{new_name.strip()}'"}
),
200,
)
except NotAuthorisedException:
return jsonify({"error": "User is not authorised to access this content"}), 403
except NonExistentDatasetException:
@@ -321,11 +336,14 @@ def delete_dataset(dataset_id):
dataset_manager.delete_dataset_info(dataset_id)
dataset_manager.delete_dataset_content(dataset_id)
return jsonify(
{
"message": f"Dataset {dataset_id} metadata and content successfully deleted"
}
), 200
return (
jsonify(
{
"message": f"Dataset {dataset_id} metadata and content successfully deleted"
}
),
200,
)
except NotAuthorisedException:
return jsonify({"error": "User is not authorised to access this content"}), 403
except NonExistentDatasetException:
@@ -523,7 +541,8 @@ def get_interaction_analysis(dataset_id):
except Exception as e:
print(traceback.format_exc())
return jsonify({"error": f"An unexpected error occurred"}), 500
@app.route("/dataset/<int:dataset_id>/all", methods=["GET"])
@jwt_required()
def get_full_dataset(dataset_id: int):
@@ -546,5 +565,6 @@ def get_full_dataset(dataset_id: int):
print(traceback.format_exc())
return jsonify({"error": f"An unexpected error occurred"}), 500
if __name__ == "__main__":
app.run(debug=True)

18
server/connectors/base.py
View File

@@ -1,10 +1,11 @@
from abc import ABC, abstractmethod
from dto.post import Post
class BaseConnector(ABC):
# Each subclass declares these at the class level
source_name: str # machine-readable: "reddit", "youtube"
display_name: str # human-readable: "Reddit", "YouTube"
source_name: str # machine-readable: "reddit", "youtube"
display_name: str # human-readable: "Reddit", "YouTube"
required_env: list[str] = [] # env vars needed to activate
search_enabled: bool
@@ -14,16 +15,13 @@ class BaseConnector(ABC):
def is_available(cls) -> bool:
"""Returns True if all required env vars are set."""
import os
return all(os.getenv(var) for var in cls.required_env)
@abstractmethod
def get_new_posts_by_search(self,
search: str = None,
category: str = None,
post_limit: int = 10
) -> list[Post]:
...
def get_new_posts_by_search(
self, search: str = None, category: str = None, post_limit: int = 10
) -> list[Post]: ...
@abstractmethod
def category_exists(self, category: str) -> bool:
...
def category_exists(self, category: str) -> bool: ...

72
server/connectors/boards_api.py
View File

@@ -11,9 +11,8 @@ from server.connectors.base import BaseConnector
logger = logging.getLogger(__name__)
HEADERS = {
"User-Agent": "Mozilla/5.0 (compatible; ForumScraper/1.0)"
}
HEADERS = {"User-Agent": "Mozilla/5.0 (compatible; ForumScraper/1.0)"}
class BoardsAPI(BaseConnector):
source_name: str = "boards.ie"
@@ -25,19 +24,17 @@ class BoardsAPI(BaseConnector):
def __init__(self):
self.base_url = "https://www.boards.ie"
def get_new_posts_by_search(self,
search: str,
category: str,
post_limit: int
) -> list[Post]:
def get_new_posts_by_search(
self, search: str, category: str, post_limit: int
) -> list[Post]:
if search:
raise NotImplementedError("Search not compatible with boards.ie")
if category:
return self._get_posts(f"{self.base_url}/categories/{category}", post_limit)
else:
return self._get_posts(f"{self.base_url}/discussions", post_limit)
def category_exists(self, category: str) -> bool:
if not category:
return False
@@ -59,7 +56,7 @@ class BoardsAPI(BaseConnector):
except requests.RequestException as e:
logger.error(f"Error checking category '{category}': {e}")
return False
## Private
def _get_posts(self, url, limit) -> list[Post]:
urls = []
@@ -78,7 +75,7 @@ class BoardsAPI(BaseConnector):
href = a.get("href")
if href:
urls.append(href)
current_page += 1
logger.debug(f"Fetched {len(urls)} post URLs")
@@ -96,7 +93,9 @@ class BoardsAPI(BaseConnector):
for i, future in enumerate(as_completed(futures)):
post_url = futures[future]
logger.debug(f"Fetching Post {i + 1} / {len(urls)} details from URL: {post_url}")
logger.debug(
f"Fetching Post {i + 1} / {len(urls)} details from URL: {post_url}"
)
try:
post = future.result()
posts.append(post)
@@ -105,7 +104,6 @@ class BoardsAPI(BaseConnector):
return posts
def _fetch_page(self, url: str) -> str:
response = requests.get(url, headers=HEADERS)
response.raise_for_status()
@@ -113,7 +111,7 @@ class BoardsAPI(BaseConnector):
def _parse_thread(self, html: str, post_url: str) -> Post:
soup = BeautifulSoup(html, "html.parser")
# Author
author_tag = soup.select_one(".userinfo-username-title")
author = author_tag.text.strip() if author_tag else None
@@ -122,10 +120,16 @@ class BoardsAPI(BaseConnector):
timestamp_tag = soup.select_one(".postbit-header")
timestamp = None
if timestamp_tag:
match = re.search(r"\d{2}-\d{2}-\d{4}\s+\d{2}:\d{2}[AP]M", timestamp_tag.get_text())
match = re.search(
r"\d{2}-\d{2}-\d{4}\s+\d{2}:\d{2}[AP]M", timestamp_tag.get_text()
)
timestamp = match.group(0) if match else None
# convert to unix epoch
timestamp = datetime.datetime.strptime(timestamp, "%d-%m-%Y %I:%M%p").timestamp() if timestamp else None
timestamp = (
datetime.datetime.strptime(timestamp, "%d-%m-%Y %I:%M%p").timestamp()
if timestamp
else None
)
# Post ID
post_num = re.search(r"discussion/(\d+)", post_url)
@@ -133,7 +137,9 @@ class BoardsAPI(BaseConnector):
# Content
content_tag = soup.select_one(".Message.userContent")
content = content_tag.get_text(separator="\n", strip=True) if content_tag else None
content = (
content_tag.get_text(separator="\n", strip=True) if content_tag else None
)
# Title
title_tag = soup.select_one(".PageTitle h1")
@@ -150,7 +156,7 @@ class BoardsAPI(BaseConnector):
url=post_url,
timestamp=timestamp,
source=self.source_name,
comments=comments
comments=comments,
)
return post
@@ -168,9 +174,9 @@ class BoardsAPI(BaseConnector):
soup = BeautifulSoup(html, "html.parser")
next_link = soup.find("a", class_="Next")
if next_link and next_link.get('href'):
href = next_link.get('href')
current_url = href if href.startswith('http') else url + href
if next_link and next_link.get("href"):
href = next_link.get("href")
current_url = href if href.startswith("http") else url + href
else:
current_url = None
@@ -186,21 +192,29 @@ class BoardsAPI(BaseConnector):
comment_id = tag.get("id")
# Author
user_elem = tag.find('span', class_='userinfo-username-title')
user_elem = tag.find("span", class_="userinfo-username-title")
username = user_elem.get_text(strip=True) if user_elem else None
# Timestamp
date_elem = tag.find('span', class_='DateCreated')
date_elem = tag.find("span", class_="DateCreated")
timestamp = date_elem.get_text(strip=True) if date_elem else None
timestamp = datetime.datetime.strptime(timestamp, "%d-%m-%Y %I:%M%p").timestamp() if timestamp else None
timestamp = (
datetime.datetime.strptime(timestamp, "%d-%m-%Y %I:%M%p").timestamp()
if timestamp
else None
)
# Content
message_div = tag.find('div', class_='Message userContent')
message_div = tag.find("div", class_="Message userContent")
if message_div.blockquote:
message_div.blockquote.decompose()
content = message_div.get_text(separator="\n", strip=True) if message_div else None
content = (
message_div.get_text(separator="\n", strip=True)
if message_div
else None
)
comment = Comment(
id=comment_id,
@@ -209,10 +223,8 @@ class BoardsAPI(BaseConnector):
content=content,
timestamp=timestamp,
reply_to=None,
source=self.source_name
source=self.source_name,
)
comments.append(comment)
return comments

127
server/connectors/reddit_api.py
View File

@@ -9,6 +9,7 @@ from server.connectors.base import BaseConnector
logger = logging.getLogger(__name__)
class RedditAPI(BaseConnector):
source_name: str = "reddit"
display_name: str = "Reddit"
@@ -19,22 +20,18 @@ class RedditAPI(BaseConnector):
self.url = "https://www.reddit.com/"
# Public Methods #
def get_new_posts_by_search(self,
search: str,
category: str,
post_limit: int
) -> list[Post]:
def get_new_posts_by_search(
self, search: str, category: str, post_limit: int
) -> list[Post]:
prefix = f"r/{category}/" if category else ""
params = {'limit': post_limit}
params = {"limit": post_limit}
if search:
endpoint = f"{prefix}search.json"
params.update({
'q': search,
'sort': 'new',
'restrict_sr': 'on' if category else 'off'
})
params.update(
{"q": search, "sort": "new", "restrict_sr": "on" if category else "off"}
)
else:
endpoint = f"{prefix}new.json"
@@ -43,24 +40,24 @@ class RedditAPI(BaseConnector):
while len(posts) < post_limit:
batch_limit = min(100, post_limit - len(posts))
params['limit'] = batch_limit
params["limit"] = batch_limit
if after:
params['after'] = after
params["after"] = after
data = self._fetch_post_overviews(endpoint, params)
if not data or 'data' not in data or not data['data'].get('children'):
if not data or "data" not in data or not data["data"].get("children"):
break
batch_posts = self._parse_posts(data)
posts.extend(batch_posts)
after = data['data'].get('after')
after = data["data"].get("after")
if not after:
break
return posts[:post_limit]
def _get_new_subreddit_posts(self, subreddit: str, limit: int = 10) -> list[Post]:
posts = []
after = None
@@ -70,37 +67,36 @@ class RedditAPI(BaseConnector):
while len(posts) < limit:
batch_limit = min(100, limit - len(posts))
params = {
'limit': batch_limit,
'after': after
}
params = {"limit": batch_limit, "after": after}
data = self._fetch_post_overviews(url, params)
batch_posts = self._parse_posts(data)
logger.debug(f"Fetched {len(batch_posts)} new posts from subreddit {subreddit}")
logger.debug(
f"Fetched {len(batch_posts)} new posts from subreddit {subreddit}"
)
if not batch_posts:
break
posts.extend(batch_posts)
after = data['data'].get('after')
after = data["data"].get("after")
if not after:
break
return posts
def get_user(self, username: str) -> User:
data = self._fetch_post_overviews(f"user/{username}/about.json", {})
return self._parse_user(data)
def category_exists(self, category: str) -> bool:
try:
data = self._fetch_post_overviews(f"r/{category}/about.json", {})
return (
data is not None
and 'data' in data
and data['data'].get('id') is not None
and "data" in data
and data["data"].get("id") is not None
)
except Exception:
return False
@@ -109,25 +105,26 @@ class RedditAPI(BaseConnector):
def _parse_posts(self, data) -> list[Post]:
posts = []
total_num_posts = len(data['data']['children'])
total_num_posts = len(data["data"]["children"])
current_index = 0
for item in data['data']['children']:
for item in data["data"]["children"]:
current_index += 1
logger.debug(f"Parsing post {current_index} of {total_num_posts}")
post_data = item['data']
post_data = item["data"]
post = Post(
id=post_data['id'],
author=post_data['author'],
title=post_data['title'],
content=post_data.get('selftext', ''),
url=post_data['url'],
timestamp=post_data['created_utc'],
id=post_data["id"],
author=post_data["author"],
title=post_data["title"],
content=post_data.get("selftext", ""),
url=post_data["url"],
timestamp=post_data["created_utc"],
source=self.source_name,
comments=self._get_post_comments(post_data['id']))
post.subreddit = post_data['subreddit']
post.upvotes = post_data['ups']
comments=self._get_post_comments(post_data["id"]),
)
post.subreddit = post_data["subreddit"]
post.upvotes = post_data["ups"]
posts.append(post)
return posts
@@ -140,56 +137,62 @@ class RedditAPI(BaseConnector):
if len(data) < 2:
return comments
comment_data = data[1]['data']['children']
comment_data = data[1]["data"]["children"]
def _parse_comment_tree(items, parent_id=None):
for item in items:
if item['kind'] != 't1':
if item["kind"] != "t1":
continue
comment_info = item['data']
comment_info = item["data"]
comment = Comment(
id=comment_info['id'],
id=comment_info["id"],
post_id=post_id,
author=comment_info['author'],
content=comment_info.get('body', ''),
timestamp=comment_info['created_utc'],
reply_to=parent_id or comment_info.get('parent_id', None),
source=self.source_name
author=comment_info["author"],
content=comment_info.get("body", ""),
timestamp=comment_info["created_utc"],
reply_to=parent_id or comment_info.get("parent_id", None),
source=self.source_name,
)
comments.append(comment)
# Process replies recursively
replies = comment_info.get('replies')
replies = comment_info.get("replies")
if replies and isinstance(replies, dict):
reply_items = replies.get('data', {}).get('children', [])
reply_items = replies.get("data", {}).get("children", [])
_parse_comment_tree(reply_items, parent_id=comment.id)
_parse_comment_tree(comment_data)
return comments
def _parse_user(self, data) -> User:
user_data = data['data']
user = User(
username=user_data['name'],
created_utc=user_data['created_utc'])
user.karma = user_data['total_karma']
user_data = data["data"]
user = User(username=user_data["name"], created_utc=user_data["created_utc"])
user.karma = user_data["total_karma"]
return user
def _fetch_post_overviews(self, endpoint: str, params: dict) -> dict:
url = f"{self.url}{endpoint}"
max_retries = 15
backoff = 1 # seconds
backoff = 1 # seconds
for attempt in range(max_retries):
try:
response = requests.get(url, headers={'User-agent': 'python:ethnography-college-project:0.1 (by /u/ThisBirchWood)'}, params=params)
response = requests.get(
url,
headers={
"User-agent": "python:ethnography-college-project:0.1 (by /u/ThisBirchWood)"
},
params=params,
)
if response.status_code == 429:
wait_time = response.headers.get("Retry-After", backoff)
logger.warning(f"Rate limited by Reddit API. Retrying in {wait_time} seconds...")
logger.warning(
f"Rate limited by Reddit API. Retrying in {wait_time} seconds..."
)
time.sleep(wait_time)
backoff *= 2
@@ -205,4 +208,4 @@ class RedditAPI(BaseConnector):
return response.json()
except requests.RequestException as e:
print(f"Error fetching data from Reddit API: {e}")
return {}
return {}

19
server/connectors/registry.py
View File

@@ -3,6 +3,7 @@ import importlib
import server.connectors
from server.connectors.base import BaseConnector
def _discover_connectors() -> list[type[BaseConnector]]:
"""Walk the connectors package and collect all BaseConnector subclasses."""
for _, module_name, _ in pkgutil.iter_modules(server.connectors.__path__):
@@ -11,20 +12,24 @@ def _discover_connectors() -> list[type[BaseConnector]]:
importlib.import_module(f"server.connectors.{module_name}")
return [
cls for cls in BaseConnector.__subclasses__()
cls
for cls in BaseConnector.__subclasses__()
if cls.source_name # guard against abstract intermediaries
]
def get_available_connectors() -> dict[str, type[BaseConnector]]:
return {c.source_name: c for c in _discover_connectors() if c.is_available()}
def get_connector_metadata() -> dict[str, dict]:
res = {}
for id, obj in get_available_connectors().items():
res[id] = {"id": id,
"label": obj.display_name,
"search_enabled": obj.search_enabled,
"categories_enabled": obj.categories_enabled
}
res[id] = {
"id": id,
"label": obj.display_name,
"search_enabled": obj.search_enabled,
"categories_enabled": obj.categories_enabled,
}
return res
return res

104
server/connectors/youtube_api.py
View File

@@ -12,6 +12,7 @@ load_dotenv()
API_KEY = os.getenv("YOUTUBE_API_KEY")
class YouTubeAPI(BaseConnector):
source_name: str = "youtube"
display_name: str = "YouTube"
@@ -19,73 +20,72 @@ class YouTubeAPI(BaseConnector):
categories_enabled: bool = False
def __init__(self):
self.youtube = build('youtube', 'v3', developerKey=API_KEY)
self.youtube = build("youtube", "v3", developerKey=API_KEY)
def get_new_posts_by_search(self,
search: str,
category: str,
post_limit: int
) -> list[Post]:
videos = self._search_videos(search, post_limit)
posts = []
def get_new_posts_by_search(
self, search: str, category: str, post_limit: int
) -> list[Post]:
videos = self._search_videos(search, post_limit)
posts = []
for video in videos:
video_id = video['id']['videoId']
snippet = video['snippet']
title = snippet['title']
description = snippet['description']
published_at = datetime.datetime.strptime(snippet['publishedAt'], "%Y-%m-%dT%H:%M:%SZ").timestamp()
channel_title = snippet['channelTitle']
for video in videos:
video_id = video["id"]["videoId"]
snippet = video["snippet"]
title = snippet["title"]
description = snippet["description"]
published_at = datetime.datetime.strptime(
snippet["publishedAt"], "%Y-%m-%dT%H:%M:%SZ"
).timestamp()
channel_title = snippet["channelTitle"]
comments = []
comments_data = self._get_video_comments(video_id)
for comment_thread in comments_data:
comment_snippet = comment_thread['snippet']['topLevelComment']['snippet']
comment = Comment(
id=comment_thread['id'],
post_id=video_id,
content=comment_snippet['textDisplay'],
author=comment_snippet['authorDisplayName'],
timestamp=datetime.datetime.strptime(comment_snippet['publishedAt'], "%Y-%m-%dT%H:%M:%SZ").timestamp(),
reply_to=None,
source=self.source_name
)
comments.append(comment)
post = Post(
id=video_id,
content=f"{title}\n\n{description}",
author=channel_title,
timestamp=published_at,
url=f"https://www.youtube.com/watch?v={video_id}",
title=title,
comments = []
comments_data = self._get_video_comments(video_id)
for comment_thread in comments_data:
comment_snippet = comment_thread["snippet"]["topLevelComment"][
"snippet"
]
comment = Comment(
id=comment_thread["id"],
post_id=video_id,
content=comment_snippet["textDisplay"],
author=comment_snippet["authorDisplayName"],
timestamp=datetime.datetime.strptime(
comment_snippet["publishedAt"], "%Y-%m-%dT%H:%M:%SZ"
).timestamp(),
reply_to=None,
source=self.source_name,
comments=comments
)
posts.append(post)
comments.append(comment)
post = Post(
id=video_id,
content=f"{title}\n\n{description}",
author=channel_title,
timestamp=published_at,
url=f"https://www.youtube.com/watch?v={video_id}",
title=title,
source=self.source_name,
comments=comments,
)
posts.append(post)
return posts
return posts
def category_exists(self, category):
return True
def _search_videos(self, query, limit):
request = self.youtube.search().list(
q=query,
part='snippet',
type='video',
maxResults=limit
q=query, part="snippet", type="video", maxResults=limit
)
response = request.execute()
return response.get('items', [])
return response.get("items", [])
def _get_video_comments(self, video_id):
request = self.youtube.commentThreads().list(
part='snippet',
videoId=video_id,
textFormat='plainText'
part="snippet", videoId=video_id, textFormat="plainText"
)
try:
@@ -93,4 +93,4 @@ class YouTubeAPI(BaseConnector):
except HttpError as e:
print(f"Error fetching comments for video {video_id}: {e}")
return []
return response.get('items', [])
return response.get("items", [])

17
server/core/auth.py
View File

@@ -5,6 +5,7 @@ from flask_bcrypt import Bcrypt
EMAIL_REGEX = re.compile(r"[^@]+@[^@]+\.[^@]+")
class AuthManager:
def __init__(self, db: PostgresConnector, bcrypt: Bcrypt):
self.db = db
@@ -24,13 +25,13 @@ class AuthManager:
if len(username) < 3:
raise ValueError("Username must be longer than 3 characters")
if not EMAIL_REGEX.match(email):
raise ValueError("Please enter a valid email address")
if self.get_user_by_email(email):
raise ValueError("Email already registered")
if self.get_user_by_username(username):
raise ValueError("Username already taken")
@@ -38,20 +39,22 @@ class AuthManager:
def authenticate_user(self, username, password):
user = self.get_user_by_username(username)
if user and self.bcrypt.check_password_hash(user['password_hash'], password):
if user and self.bcrypt.check_password_hash(user["password_hash"], password):
return user
return None
def get_user_by_id(self, user_id):
query = "SELECT id, username, email FROM users WHERE id = %s"
result = self.db.execute(query, (user_id,), fetch=True)
return result[0] if result else None
def get_user_by_username(self, username) -> dict:
query = "SELECT id, username, email, password_hash FROM users WHERE username = %s"
query = (
"SELECT id, username, email, password_hash FROM users WHERE username = %s"
)
result = self.db.execute(query, (username,), fetch=True)
return result[0] if result else None
def get_user_by_email(self, email) -> dict:
query = "SELECT id, username, email, password_hash FROM users WHERE email = %s"
result = self.db.execute(query, (email,), fetch=True)

33
server/core/datasets.py
View File

@@ -3,6 +3,7 @@ from server.db.database import PostgresConnector
from psycopg2.extras import Json
from server.exceptions import NonExistentDatasetException
class DatasetManager:
def __init__(self, db: PostgresConnector):
self.db = db
@@ -15,18 +16,18 @@ class DatasetManager:
if dataset_info.get("user_id") != user_id:
return False
return True
def get_user_datasets(self, user_id: int) -> list[dict]:
query = "SELECT * FROM datasets WHERE user_id = %s"
return self.db.execute(query, (user_id, ), fetch=True)
return self.db.execute(query, (user_id,), fetch=True)
def get_dataset_content(self, dataset_id: int) -> pd.DataFrame:
query = "SELECT * FROM events WHERE dataset_id = %s"
result = self.db.execute(query, (dataset_id,), fetch=True)
return pd.DataFrame(result)
def get_dataset_info(self, dataset_id: int) -> dict:
query = "SELECT * FROM datasets WHERE id = %s"
result = self.db.execute(query, (dataset_id,), fetch=True)
@@ -35,14 +36,16 @@ class DatasetManager:
raise NonExistentDatasetException(f"Dataset {dataset_id} does not exist")
return result[0]
def save_dataset_info(self, user_id: int, dataset_name: str, topics: dict) -> int:
query = """
INSERT INTO datasets (user_id, name, topics)
VALUES (%s, %s, %s)
RETURNING id
"""
result = self.db.execute(query, (user_id, dataset_name, Json(topics)), fetch=True)
result = self.db.execute(
query, (user_id, dataset_name, Json(topics)), fetch=True
)
return result[0]["id"] if result else None
def save_dataset_content(self, dataset_id: int, event_data: pd.DataFrame):
@@ -113,7 +116,9 @@ class DatasetManager:
self.db.execute_batch(query, values)
def set_dataset_status(self, dataset_id: int, status: str, status_message: str | None = None):
def set_dataset_status(
self, dataset_id: int, status: str, status_message: str | None = None
):
if status not in ["fetching", "processing", "complete", "error"]:
raise ValueError("Invalid status")
@@ -137,24 +142,24 @@ class DatasetManager:
WHERE id = %s
"""
result = self.db.execute(query, (dataset_id, ), fetch=True)
result = self.db.execute(query, (dataset_id,), fetch=True)
if not result:
print(result)
raise NonExistentDatasetException(f"Dataset {dataset_id} does not exist")
return result[0]
def update_dataset_name(self, dataset_id: int, new_name: str):
query = "UPDATE datasets SET name = %s WHERE id = %s"
self.db.execute(query, (new_name, dataset_id))
def delete_dataset_info(self, dataset_id: int):
query = "DELETE FROM datasets WHERE id = %s"
self.db.execute(query, (dataset_id, ))
self.db.execute(query, (dataset_id,))
def delete_dataset_content(self, dataset_id: int):
query = "DELETE FROM events WHERE dataset_id = %s"
self.db.execute(query, (dataset_id, ))
self.db.execute(query, (dataset_id,))

8
server/db/database.py
View File

@@ -22,8 +22,10 @@ class PostgresConnector:
database=os.getenv("POSTGRES_DB", "postgres"),
)
except psycopg2.OperationalError as e:
raise DatabaseNotConfiguredException(f"Ensure database is up and running: {e}")
raise DatabaseNotConfiguredException(
f"Ensure database is up and running: {e}"
)
self.connection.autocommit = False
def execute(self, query, params=None, fetch=False) -> list:
@@ -48,4 +50,4 @@ class PostgresConnector:
def close(self):
if self.connection:
self.connection.close()
self.connection.close()

4
server/queue/celery_app.py
View File

@@ -5,6 +5,7 @@ from server.utils import get_env
load_dotenv()
REDIS_URL = get_env("REDIS_URL")
def create_celery():
celery = Celery(
"ethnograph",
@@ -16,6 +17,7 @@ def create_celery():
celery.conf.accept_content = ["json"]
return celery
celery = create_celery()
from server.queue import tasks
from server.queue import tasks

29
server/queue/tasks.py
View File

@@ -9,6 +9,7 @@ from server.connectors.registry import get_available_connectors
logger = logging.getLogger(__name__)
@celery.task(bind=True, max_retries=3)
def process_dataset(self, dataset_id: int, posts: list, topics: dict):
db = PostgresConnector()
@@ -21,15 +22,19 @@ def process_dataset(self, dataset_id: int, posts: list, topics: dict):
enriched_df = processor.enrich()
dataset_manager.save_dataset_content(dataset_id, enriched_df)
dataset_manager.set_dataset_status(dataset_id, "complete", "NLP Processing Completed Successfully")
dataset_manager.set_dataset_status(
dataset_id, "complete", "NLP Processing Completed Successfully"
)
except Exception as e:
dataset_manager.set_dataset_status(dataset_id, "error", f"An error occurred: {e}")
dataset_manager.set_dataset_status(
dataset_id, "error", f"An error occurred: {e}"
)
@celery.task(bind=True, max_retries=3)
def fetch_and_process_dataset(self,
dataset_id: int,
source_info: list[dict],
topics: dict):
def fetch_and_process_dataset(
self, dataset_id: int, source_info: list[dict], topics: dict
):
connectors = get_available_connectors()
db = PostgresConnector()
dataset_manager = DatasetManager(db)
@@ -44,9 +49,7 @@ def fetch_and_process_dataset(self,
connector = connectors[name]()
raw_posts = connector.get_new_posts_by_search(
search=search,
category=category,
post_limit=limit
search=search, category=category, post_limit=limit
)
posts.extend(post.to_dict() for post in raw_posts)
@@ -56,6 +59,10 @@ def fetch_and_process_dataset(self,
enriched_df = processor.enrich()
dataset_manager.save_dataset_content(dataset_id, enriched_df)
dataset_manager.set_dataset_status(dataset_id, "complete", "NLP Processing Completed Successfully")
dataset_manager.set_dataset_status(
dataset_id, "complete", "NLP Processing Completed Successfully"
)
except Exception as e:
dataset_manager.set_dataset_status(dataset_id, "error", f"An error occurred: {e}")
dataset_manager.set_dataset_status(
dataset_id, "error", f"An error occurred: {e}"
)