Add one review or rating flow

data-science/analytics/rating_aggregator.py

@@ -6,16 +6,61 @@
 
 import pandas as pd
 from pathlib import Path
+import json
+
 
 DATASETS_DIR = Path(__file__).parent.parent.parent / "datasets"
 
 
 def aggregate_ratings() -> pd.DataFrame:
-    """Aggregate ratings by agent."""
+    """Aggregate ratings by agent.
+
+    Definition:
+    - num_ratings: number of ratings for a specific agent
+    - average_rating: review ratings added up / num_ratings
+    - Higher num_ratings = higher confidence score for a particular review
+    - Agents ranked by average_rating descending
+
+    """
     df = pd.read_csv(DATASETS_DIR / "agent_ratings.csv")
     # TODO: Add std dev, rating count, recent vs all-time
     return df.sort_values("avg_rating", ascending=False)
 
 
+def calculate_average_ratings() -> list[dict]:
+
+    """
+    Calculate average rating of an agent given review ratings
+    Read agent_reviews, get ratings and calculate num_ratings and average_rating
+    """
+
+
+    with open(DATASETS_DIR / "agent_reviews.json", encoding="utf-8") as f:
+        reviews = json.load(f)
+
+    totals = {}
+    for review in reviews:
+        agent_id = review["agent_id"]
+        if agent_id not in totals:
+            totals[agent_id] = {"sum": 0, "count": 0}
+        totals[agent_id]["sum"] += review["rating"]
+        totals[agent_id]["count"] += 1
+
+    results = []
+    for agent_id, data in totals.items():
+        results.append({
+            "agent_id": agent_id,
+            "average_rating": round(data["sum"] / data["count"], 2),
+            "num_ratings": data["count"],
+        })
+
+    return sorted(results, key=lambda x: x["average_rating"], reverse=True)
+
+
 if __name__ == "__main__":
     print(aggregate_ratings().to_string(index=False))
+
+    print("\nAverage Ratings from Reviews:")
+    print("-" * 40)
+    for agent in calculate_average_ratings():
+        print(f"{agent['agent_id']}: {agent['average_rating']} ({agent['num_ratings']} reviews)")

data-science/analytics/review_sentiment.py

@@ -34,5 +34,22 @@ def analyze_sentiment(reviews: list[dict]) -> list[dict]:
 if __name__ == "__main__":
     reviews = load_reviews()
     analyzed = analyze_sentiment(reviews)
+
+    # Group reviews by agent_id
+    from collections import defaultdict
+    grouped = defaultdict(list)
     for r in analyzed:
-        print(f"[{r['sentiment']}] {r['agent_id']}: {r['review'][:60]}...")
+        grouped[r["agent_id"]].append(r)
+
+    print("Reviews by Agent:")
+    print("=" * 50)
+    for agent_id, agent_reviews in grouped.items():
+        print(f"\nAgent: {agent_id}")
+        print(f"Number of reviews: {len(agent_reviews)}")
+        print("-" * 40)
+        for r in agent_reviews:
+            print(f"  Rating: {r['rating']}/5")
+            print(f"  Review: {r['review']}")
+            print(f"  Sentiment: {r['sentiment']}")
+            print(f"  Date: {r['date']}")
+            print()

datasets/agent_reviews.json

@@ -11,12 +11,24 @@
     "review": "Good summaries but sometimes misses important emails in long threads.",
     "date": "2026-05-22"
   },
+  {
+    "agent_id": "email_summarizer",
+    "rating": 1,
+    "review": "Missed critical emails entirely and the summaries were too vague to be useful.",
+    "date": "2026-06-07"
+  },
   {
     "agent_id": "github_issue_triage",
     "rating": 5,
     "review": "Perfect for our open source project. Label suggestions are spot on.",
     "date": "2026-05-18"
   },
+  {
+    "agent_id": "github_issue_triage",
+    "rating": 3,
+    "review": "Label suggestions are helpful but priority ranking feels off for complex issues.",
+    "date": "2026-05-27"
+  },
   {
     "agent_id": "meeting_notes",
     "rating": 5,
@@ -29,22 +41,46 @@
     "review": "Works well for standups. Would love better integration with calendar events.",
     "date": "2026-05-27"
   },
+  {
+    "agent_id": "meeting_notes",
+    "rating": 1,
+    "review": "Transcription was completely inaccurate and the action items were assigned to the wrong people.",
+    "date": "2026-05-26"
+  },
   {
     "agent_id": "resume_reviewer",
     "rating": 5,
     "review": "Got actionable feedback that helped me land interviews. Highly recommend.",
     "date": "2026-05-15"
   },
+  {
+    "agent_id": "resume_reviewer",
+    "rating": 3,
+    "review": "Caught formatting issues well but the content suggestions could be more tailored to my industry.",
+    "date": "2026-06-13"
+  },
   {
     "agent_id": "travel_deal_finder",
     "rating": 3,
     "review": "Found some deals but prices weren't always accurate. Needs improvement.",
     "date": "2026-05-19"
   },
+  {
+    "agent_id": "travel_deal_finder",
+    "rating": 2,
+    "review": "Found some flights but the price comparisons were outdated and hotel options were very limited.",
+    "date": "2026-06-12"
+  },
   {
     "agent_id": "study_buddy",
     "rating": 4,
     "review": "Flashcards are great for exam prep. Study plans could be more customizable.",
     "date": "2026-05-21"
+  },
+  {
+    "agent_id": "study_buddy",
+    "rating": 2,
+    "review": "Study plans were a good starting point but flashcards felt repetitive and lacked variety.",
+    "date": "2026-06-11"
   }
 ]