SysOps: voice-agy-webbuilder-backup — 2026-06-23 10:04 UTC

documents.py

@@ -0,0 +1,96 @@
+from pathlib import Path
+
+from fastapi import APIRouter, Request
+from fastapi.templating import Jinja2Templates
+
+from app.db import fetch_all, fetch_one
+
+router = APIRouter(prefix="/documents", tags=["documents"])
+BASE_DIR = Path(__file__).resolve().parent.parent.parent
+templates = Jinja2Templates(directory=str(BASE_DIR / "templates"))
+
+
+def _iso_rows(rows: list) -> list:
+    for row in rows:
+        for key, val in list(row.items()):
+            if hasattr(val, "isoformat"):
+                row[key] = val.isoformat()
+    return rows
+
+
+@router.get("")
+async def documents_page(request: Request):
+    summary = {
+        "documents": 0,
+        "total_words": 0,
+        "unique_words": 0,
+        "avg_sentiment": 0.0,
+        "positive": 0,
+        "neutral": 0,
+        "negative": 0,
+    }
+    try:
+        row = fetch_one(
+            """
+            SELECT COUNT(*) AS docs,
+                   COALESCE(SUM(word_count), 0) AS words,
+                   COALESCE(AVG(sentiment_compound), 0) AS avg_sent
+            FROM document_analytics
+            """
+        )
+        if row:
+            summary["documents"] = int(row["docs"] or 0)
+            summary["total_words"] = int(row["words"] or 0)
+            summary["avg_sentiment"] = round(float(row["avg_sent"] or 0), 3)
+        row = fetch_one("SELECT COUNT(DISTINCT lemma) AS c FROM document_word_counts WHERE NOT is_stopword")
+        summary["unique_words"] = int(row["c"] or 0) if row else 0
+        for label in ("positive", "neutral", "negative"):
+            row = fetch_one(
+                "SELECT COUNT(*) AS c FROM document_analytics WHERE sentiment_label = %s",
+                (label,),
+            )
+            summary[label] = int(row["c"] or 0) if row else 0
+    except Exception:
+        pass
+
+    top_words: list = []
+    documents: list = []
+    try:
+        top_words = fetch_all(
+            """
+            SELECT lemma, MAX(token) AS token, SUM(count) AS total_count,
+                   COUNT(DISTINCT storage_path) AS doc_count
+            FROM document_word_counts
+            WHERE NOT is_stopword
+            GROUP BY lemma
+            ORDER BY total_count DESC
+            LIMIT 30
+            """
+        )
+        documents = _iso_rows(
+            fetch_all(
+                """
+                SELECT filename, storage_path, doc_type, language, word_count,
+                       unique_lemmas, sentiment_label, sentiment_compound,
+                       sentiment_positive, sentiment_negative, sentiment_neutral,
+                       extraction_method, analyzed_at,
+                       COALESCE(user_labels, '{}') AS user_labels, label_notes, labeled_at
+                FROM document_analytics
+                ORDER BY analyzed_at DESC
+                LIMIT 50
+                """
+            )
+        )
+    except Exception:
+        pass
+
+    return templates.TemplateResponse(
+        "documents.html",
+        {
+            "request": request,
+            "page_title": "Documents & Sentiment",
+            "summary": summary,
+            "top_words": top_words,
+            "documents": documents,
+        },
+    )