Initial commit for HF Space

app.py

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+import gradio as gr
+from pathlib import Path
+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
+        )
+    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
+
+iface = gr.Interface(
+    fn=analyze_sqlite,
+    inputs=[
+        gr.File(label="SQLite-Datenbank (.sqlite)"),
+        gr.Textbox(label="ISBN", placeholder="9783446264199"),
+        gr.CheckboxGroup(["de", "en"], label="Sprachen", value=["de"])
+    ],
+    outputs=[
+        gr.Text(label="Status"),
+        gr.Image(type="filepath", label="Sentiment-Diagramm")
+    ],
+    title="Aspekt-Sentimentanalyse",
+    description="Lade eine bereinigte SQLite-Datenbank hoch und erhalte eine visuelle Analyse der Sentiment-Scores nach Aspekten."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+gradio
+transformers
+nltk
+matplotlib
+torch