Add Gradio app

app.py

@@ -1,56 +1,49 @@
+# app.py
+
 import gradio as gr
+from analyze_aspects import analyze_quickwin, visualize_aspects
 from pathlib import Path
+import tempfile
 import shutil
-import uuid
-import subprocess
-
-def analyze_sqlite(sqlite_file, isbn, languages):
-    # Arbeitsverzeichnis vorbereiten
-    run_id = str(uuid.uuid4())
-    workdir = Path(f"/tmp/{run_id}")
-    output_dir = Path("output")
-    workdir.mkdir(parents=True, exist_ok=True)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # DB-Datei speichern
-    db_path = workdir / "buch_datenbank.sqlite"
-    shutil.copy(sqlite_file.name, db_path)
-
-    # Sprachliste korrekt übergeben
-    lang_args = []
-    for lang in languages:
-        lang_args.append(lang)
-
-    try:
-        subprocess.run(
-            [
-                "python3", "analyze_aspects.py",
-                "--db-path", str(db_path),
-                "--isbn", isbn,
-                "--languages", *lang_args
-            ],
-            check=True
+
+def run_analysis(db_file, isbn, languages):
+    if not isbn.strip():
+        return "❗ Bitte ISBN angeben.", None
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        tmp_path = Path(tmpdir) / "db.sqlite"
+        shutil.copy(db_file.name, tmp_path)
+
+        # Analyse
+        results = analyze_quickwin(
+            db_path=tmp_path,
+            isbn=isbn,
+            device=-1,
+            languages=languages
         )
-    except subprocess.CalledProcessError as e:
-        return f"Analyse fehlgeschlagen: {e}", None
 
-    chart_path = output_dir / "sentiment_aspekte.png"
-    return "Analyse abgeschlossen ✅", str(chart_path) if chart_path.exists() else None
+        if not results:
+            return "⚠️ Keine relevanten Aspekte gefunden oder Fehler in der Analyse.", None
+
+        # Diagramm erzeugen
+        visualize_aspects(results, Path(tmpdir))
+        chart_path = Path(tmpdir) / "sentiment_aspekte.png"
+        return "✅ Analyse abgeschlossen!", chart_path
 
+# Gradio Interface
 iface = gr.Interface(
-    fn=analyze_sqlite,
+    fn=run_analysis,
     inputs=[
-        gr.File(label="SQLite-Datenbank (.sqlite)"),
-        gr.Textbox(label="ISBN", placeholder="9783446264199"),
-        gr.CheckboxGroup(["de", "en"], label="Sprachen", value=["de"])
+        gr.File(label="SQLite-Datenbank (.sqlite)", file_types=[".sqlite"]),
+        gr.Text(label="ISBN", placeholder="z. B. 9783446264199"),
+        gr.CheckboxGroup(choices=["de", "en"], label="Sprachen", value=["de"])
     ],
     outputs=[
         gr.Text(label="Status"),
-        gr.Image(type="filepath", label="Sentiment-Diagramm")
+        gr.Image(label="Sentiment-Diagramm", type="filepath")
     ],
-    title="Aspekt-Sentimentanalyse",
-    description="Lade eine bereinigte SQLite-Datenbank hoch und erhalte eine visuelle Analyse der Sentiment-Scores nach Aspekten."
+    title="📖 Aspekt-Sentiment-Analyse",
+    description="Lade eine SQLite-Datenbank hoch, gib eine ISBN an und analysiere die wichtigsten inhaltlichen Aspekte und deren Sentiment."
 )
 
-if __name__ == "__main__":
-    iface.launch()
+iface.launch()

aspect-sentiment-analyzer

@@ -0,0 +1 @@
+Subproject commit de51e3830863b035c34001bde80aaee1e115d2ab