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from pathlib import Path
import sqlite3
from typing import Optional
import json
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")
import os
from pathlib import Path
DB_PATH = Path("/app/data/news.db")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=False,
allow_methods=["*"],
allow_headers=["*"],
)
LAST_14_DAYS_CONDITION = "date(published_at) >= date('now', '-14 days')"
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
AND label != 'not relevant to field'
AND """ + LAST_14_DAYS_CONDITION + """
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
AND label != 'not relevant to field'
AND """ + LAST_14_DAYS_CONDITION + """
ORDER BY source
"""
df = pd.read_sql_query(query, conn)
conn.close()
return df["source"].dropna().tolist()
@app.get("/summary/daily")
def get_daily_summary():
conn = get_connection()
query = """
SELECT
summary_date,
short_summary,
key_focus,
summary_json,
generated_at
FROM daily_summaries
ORDER BY summary_date DESC
LIMIT 1
"""
df = pd.read_sql_query(query, conn)
conn.close()
if df.empty:
return {}
row = df.iloc[0].to_dict()
result = {
"summary_date": row.get("summary_date"),
"generated_at": row.get("generated_at"),
}
summary_json = row.get("summary_json")
if summary_json:
try:
parsed_summary = json.loads(summary_json)
if isinstance(parsed_summary, dict):
result.update(parsed_summary)
except Exception:
pass
# fallback compatibility
if "executive_summary" not in result:
result["executive_summary"] = row.get("short_summary")
if "recommended_focus" not in result:
result["recommended_focus"] = row.get("key_focus")
if "decision_implications" not in result:
result["decision_implications"] = []
if "watchlist" not in result:
result["watchlist"] = []
if "top_stories" not in result:
result["top_stories"] = []
return result
@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
AND label != 'not relevant to field'
AND """ + LAST_14_DAYS_CONDITION
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
AND label != 'not relevant to field'
AND """ + LAST_14_DAYS_CONDITION
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,
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,
label_judgment,
label_confidence,
label_explanation,
overall_status,
requires_human_review,
judge_model,
raw_judge_response,
evaluated_at
FROM monitoring_results
WHERE 1=1
AND """ + LAST_14_DAYS_CONDITION
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 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
WHERE """ + LAST_14_DAYS_CONDITION,
conn
)["n"].iloc[0])
needs_review = int(pd.read_sql_query(
"""
SELECT COUNT(*) AS n
FROM monitoring_results
WHERE requires_human_review = 1
AND """ + LAST_14_DAYS_CONDITION,
conn
)["n"].iloc[0])
label_distribution = pd.read_sql_query(
"""
SELECT label_judgment, COUNT(*) AS count
FROM monitoring_results
WHERE """ + LAST_14_DAYS_CONDITION + """
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
WHERE """ + LAST_14_DAYS_CONDITION + """
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'
AND """ + LAST_14_DAYS_CONDITION + """
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
WHERE """ + LAST_14_DAYS_CONDITION + """
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,
"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,
label_judgment,
label_confidence,
label_explanation,
overall_status,
requires_human_review,
evaluated_at
FROM monitoring_results
WHERE requires_human_review = 1
AND """ + LAST_14_DAYS_CONDITION + """
ORDER BY evaluated_at DESC
LIMIT ?
"""
df = pd.read_sql_query(query, conn, params=[limit])
conn.close()
return df.to_dict(orient="records") |