Add ReputationRadar community contribution (demo replaced by link)

community-contributions/Reputation_Radar/services/reddit_client.py

@@ -0,0 +1,141 @@
+"""Reddit data collection service using PRAW."""
+
+from __future__ import annotations
+
+import time
+from datetime import datetime, timezone
+from typing import Dict, Iterable, List, Optional
+
+import praw
+from praw.models import Comment, Submission
+
+from .utils import (
+    NormalizedItem,
+    ServiceError,
+    ServiceWarning,
+    ensure_timezone,
+    sanitize_text,
+)
+
+
+TIME_FILTER_MAP = {
+    "24h": "day",
+    "7d": "week",
+    "30d": "month",
+}
+
+
+def _iter_submissions(subreddit: praw.models.Subreddit, query: str, limit: int, time_filter: str) -> Iterable[Submission]:
+    return subreddit.search(query=query, sort="new", time_filter=time_filter, limit=limit * 3)
+
+
+def _iter_comments(submission: Submission) -> Iterable[Comment]:
+    submission.comments.replace_more(limit=0)
+    return submission.comments.list()
+
+
+def _normalize_submission(submission: Submission) -> NormalizedItem:
+    created = datetime.fromtimestamp(submission.created_utc, tz=timezone.utc)
+    return NormalizedItem(
+        source="reddit",
+        id=submission.id,
+        url=f"https://www.reddit.com{submission.permalink}",
+        author=str(submission.author) if submission.author else None,
+        timestamp=ensure_timezone(created),
+        text=f"{submission.title}\n\n{submission.selftext or ''}",
+        meta={
+            "score": submission.score,
+            "num_comments": submission.num_comments,
+            "subreddit": submission.subreddit.display_name,
+            "type": "submission",
+        },
+    )
+
+
+def _normalize_comment(comment: Comment, submission: Submission) -> NormalizedItem:
+    created = datetime.fromtimestamp(comment.created_utc, tz=timezone.utc)
+    return NormalizedItem(
+        source="reddit",
+        id=comment.id,
+        url=f"https://www.reddit.com{comment.permalink}",
+        author=str(comment.author) if comment.author else None,
+        timestamp=ensure_timezone(created),
+        text=comment.body,
+        meta={
+            "score": comment.score,
+            "subreddit": submission.subreddit.display_name,
+            "type": "comment",
+            "submission_title": submission.title,
+        },
+    )
+
+
+def fetch_mentions(
+    brand: str,
+    credentials: Dict[str, str],
+    limit: int = 25,
+    date_filter: str = "7d",
+    min_upvotes: int = 0,
+) -> List[NormalizedItem]:
+    """Fetch recent Reddit submissions/comments mentioning the brand."""
+    client_id = credentials.get("client_id")
+    client_secret = credentials.get("client_secret")
+    user_agent = credentials.get("user_agent")
+
+    if not all([client_id, client_secret, user_agent]):
+        raise ServiceWarning("Reddit credentials are missing. Provide them in the sidebar to enable this source.")
+
+    try:
+        reddit = praw.Reddit(
+            client_id=client_id,
+            client_secret=client_secret,
+            user_agent=user_agent,
+        )
+        reddit.read_only = True
+    except Exception as exc:  # noqa: BLE001
+        raise ServiceError(f"Failed to initialize Reddit client: {exc}") from exc
+
+    time_filter = TIME_FILTER_MAP.get(date_filter.lower(), "week")
+    subreddit = reddit.subreddit("all")
+    results: List[NormalizedItem] = []
+    seen_ids: set[str] = set()
+    try:
+        for submission in _iter_submissions(subreddit, query=brand, limit=limit, time_filter=time_filter):
+            if submission.id in seen_ids:
+                continue
+            if submission.score < min_upvotes:
+                continue
+            normalized_submission = _normalize_submission(submission)
+            normalized_submission["text"] = sanitize_text(normalized_submission["text"])
+            if normalized_submission["text"]:
+                results.append(normalized_submission)
+                seen_ids.add(submission.id)
+            if len(results) >= limit:
+                break
+
+            # Fetch comments mentioning the brand
+            match_count = 0
+            for comment in _iter_comments(submission):
+                if brand.lower() not in (comment.body or "").lower():
+                    continue
+                if comment.score < min_upvotes:
+                    continue
+                normalized_comment = _normalize_comment(comment, submission)
+                normalized_comment["text"] = sanitize_text(normalized_comment["text"])
+                if not normalized_comment["text"]:
+                    continue
+                if normalized_comment["id"] in seen_ids:
+                    continue
+                results.append(normalized_comment)
+                seen_ids.add(normalized_comment["id"])
+                match_count += 1
+                if len(results) >= limit:
+                    break
+            if len(results) >= limit:
+                break
+            # Respect rate limits
+            if match_count:
+                time.sleep(1)
+    except Exception as exc:  # noqa: BLE001
+        raise ServiceError(f"Error while fetching Reddit data: {exc}") from exc
+    return results