Update app.py

app.py

@@ -152,4 +152,214 @@ def get_articles(
     df = pd.read_sql_query(query, conn, params=params)
     conn.close()
 
+    return df.to_dict(orient="records")
+
+
+# =========================
+# Monitoring endpoints
+# =========================
+
+@app.get("/monitoring/results")
+def get_monitoring_results(
+    overall_status: Optional[str] = None,
+    requires_human_review: Optional[int] = None,
+    relevance_judgment: Optional[str] = None,
+    label_judgment: Optional[str] = None,
+    predicted_label: Optional[str] = None,
+    source: Optional[str] = None,
+    start_date: Optional[str] = None,
+    end_date: Optional[str] = None,
+    search: Optional[str] = None,
+    limit: int = Query(100, ge=1, le=500),
+    offset: int = Query(0, ge=0),
+):
+    conn = get_connection()
+
+    query = """
+        SELECT
+            monitoring_id,
+            article_id,
+            title,
+            description,
+            clean_text,
+            predicted_label,
+            source,
+            url,
+            published_at,
+            classified_at,
+            relevance_judgment,
+            relevance_confidence,
+            relevance_explanation,
+            label_judgment,
+            label_confidence,
+            label_explanation,
+            overall_status,
+            requires_human_review,
+            judge_model,
+            raw_judge_response,
+            evaluated_at
+        FROM monitoring_results
+        WHERE 1=1
+    """
+    params = []
+
+    if overall_status:
+        query += " AND overall_status = ?"
+        params.append(overall_status)
+
+    if requires_human_review is not None:
+        query += " AND requires_human_review = ?"
+        params.append(requires_human_review)
+
+    if relevance_judgment:
+        query += " AND relevance_judgment = ?"
+        params.append(relevance_judgment)
+
+    if label_judgment:
+        query += " AND label_judgment = ?"
+        params.append(label_judgment)
+
+    if predicted_label:
+        query += " AND predicted_label = ?"
+        params.append(predicted_label)
+
+    if source:
+        query += " AND source = ?"
+        params.append(source)
+
+    if start_date:
+        query += " AND date(published_at) >= date(?)"
+        params.append(start_date)
+
+    if end_date:
+        query += " AND date(published_at) <= date(?)"
+        params.append(end_date)
+
+    if search:
+        query += " AND (lower(title) LIKE ? OR lower(description) LIKE ?)"
+        pattern = f"%{search.lower()}%"
+        params.extend([pattern, pattern])
+
+    query += " ORDER BY evaluated_at DESC LIMIT ? OFFSET ?"
+    params.extend([limit, offset])
+
+    df = pd.read_sql_query(query, conn, params=params)
+    conn.close()
+
+    return df.to_dict(orient="records")
+
+
+@app.get("/monitoring/summary")
+def get_monitoring_summary():
+    conn = get_connection()
+
+    total_monitored = int(pd.read_sql_query(
+        "SELECT COUNT(*) AS n FROM monitoring_results",
+        conn
+    )["n"].iloc[0])
+
+    needs_review = int(pd.read_sql_query(
+        "SELECT COUNT(*) AS n FROM monitoring_results WHERE requires_human_review = 1",
+        conn
+    )["n"].iloc[0])
+
+    relevance_distribution = pd.read_sql_query(
+        """
+        SELECT relevance_judgment, COUNT(*) AS count
+        FROM monitoring_results
+        GROUP BY relevance_judgment
+        ORDER BY count DESC
+        """,
+        conn
+    ).to_dict(orient="records")
+
+    label_distribution = pd.read_sql_query(
+        """
+        SELECT label_judgment, COUNT(*) AS count
+        FROM monitoring_results
+        GROUP BY label_judgment
+        ORDER BY count DESC
+        """,
+        conn
+    ).to_dict(orient="records")
+
+    status_distribution = pd.read_sql_query(
+        """
+        SELECT overall_status, COUNT(*) AS count
+        FROM monitoring_results
+        GROUP BY overall_status
+        ORDER BY count DESC
+        """,
+        conn
+    ).to_dict(orient="records")
+
+    common_problem_labels = pd.read_sql_query(
+        """
+        SELECT predicted_label, COUNT(*) AS count
+        FROM monitoring_results
+        WHERE overall_status != 'ok'
+        GROUP BY predicted_label
+        ORDER BY count DESC
+        """,
+        conn
+    ).to_dict(orient="records")
+
+    daily_issues = pd.read_sql_query(
+        """
+        SELECT
+            date(evaluated_at) AS day,
+            overall_status,
+            COUNT(*) AS count
+        FROM monitoring_results
+        GROUP BY date(evaluated_at), overall_status
+        ORDER BY day ASC, overall_status ASC
+        """,
+        conn
+    ).to_dict(orient="records")
+
+    conn.close()
+
+    return {
+        "total_monitored": total_monitored,
+        "needs_review": needs_review,
+        "relevance_distribution": relevance_distribution,
+        "label_distribution": label_distribution,
+        "status_distribution": status_distribution,
+        "common_problem_labels": common_problem_labels,
+        "daily_issues": daily_issues,
+    }
+
+
+@app.get("/monitoring/review-queue")
+def get_review_queue(limit: int = Query(100, ge=1, le=500)):
+    conn = get_connection()
+
+    query = """
+        SELECT
+            monitoring_id,
+            article_id,
+            title,
+            description,
+            predicted_label,
+            source,
+            url,
+            published_at,
+            relevance_judgment,
+            relevance_confidence,
+            relevance_explanation,
+            label_judgment,
+            label_confidence,
+            label_explanation,
+            overall_status,
+            requires_human_review,
+            evaluated_at
+        FROM monitoring_results
+        WHERE requires_human_review = 1
+        ORDER BY evaluated_at DESC
+        LIMIT ?
+    """
+
+    df = pd.read_sql_query(query, conn, params=[limit])
+    conn.close()
+
     return df.to_dict(orient="records")