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import sqlite3
from typing import Optional
import pandas as pd
from fastapi import FastAPI, Query
from fastapi.middleware.cors import CORSMiddleware
app = FastAPI(title="Green Energy News API", version="1.0.0")
DB_PATH = Path("/app/data/news.db")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=False,
allow_methods=["*"],
allow_headers=["*"],
)
def get_connection() -> sqlite3.Connection:
return sqlite3.connect(DB_PATH)
@app.get("/health")
def health():
return {
"status": "ok",
"db_exists": DB_PATH.exists(),
"db_path": str(DB_PATH),
}
@app.get("/labels")
def get_labels():
conn = get_connection()
query = """
SELECT DISTINCT label
FROM classified_articles
WHERE label IS NOT NULL
ORDER BY label
"""
df = pd.read_sql_query(query, conn)
conn.close()
return df["label"].dropna().tolist()
@app.get("/sources")
def get_sources():
conn = get_connection()
query = """
SELECT DISTINCT source
FROM classified_articles
WHERE source IS NOT NULL
ORDER BY source
"""
df = pd.read_sql_query(query, conn)
conn.close()
return df["source"].dropna().tolist()
@app.get("/summary/daily-actions")
def daily_actions(
start_date: Optional[str] = None,
end_date: Optional[str] = None,
):
conn = get_connection()
query = """
SELECT
date(published_at) AS day,
label,
COUNT(*) AS count
FROM classified_articles
WHERE 1=1
"""
params = []
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)
query += """
GROUP BY date(published_at), label
ORDER BY day ASC, label ASC
"""
df = pd.read_sql_query(query, conn, params=params)
conn.close()
return df.to_dict(orient="records")
@app.get("/articles")
def get_articles(
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(50, ge=1, le=500),
offset: int = Query(0, ge=0),
):
conn = get_connection()
query = """
SELECT
article_id,
title,
description,
clean_text,
label,
raw_label,
source,
url,
published_at,
classified_at
FROM classified_articles
WHERE 1=1
"""
params = []
if label:
query += " AND label = ?"
params.append(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 published_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")
# =========================
# 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") |