app.py

import spacy
import pandas as pd
from transformers import pipeline
import gradio as gr
import subprocess
import sys

# Versuche, spaCy zu laden – falls nicht vorhanden, lade es herunter
try:
    nlp = spacy.load("de_core_news_sm")
except OSError:
    subprocess.run([sys.executable, "-m", "spacy", "download", "de_core_news_sm"])
    nlp = spacy.load("de_core_news_sm")

# Lade Sentimentmodell, cache lokal
sentiment_analyzer = pipeline(
    "sentiment-analysis", 
    model="oliverguhr/german-sentiment-bert"
)

def link_entities_with_sentiment(text):
    doc = nlp(text)
    sentences = list(doc.sents)
    entity_sentiment_links = []

    for i, sentence in enumerate(sentences):
        entities = [(ent.text, ent.label_) for ent in sentence.ents]
        sentiment = sentiment_analyzer(sentence.text)[0]

        for ent_text, ent_label in entities:
            entity_sentiment_links.append({
                "Entity": ent_text,
                "Label": ent_label,
                "Sentence Index": i,
                "Sentiment Label": sentiment["label"],
                "Sentiment Score": round(sentiment["score"], 3)
            })

    df = pd.DataFrame(entity_sentiment_links)
    return df if not df.empty else "Keine Entitäten gefunden."

# Gradio Interface
demo = gr.Interface(
    fn=link_entities_with_sentiment,
    inputs=gr.Textbox(lines=10, label="Gib deinen deutschen Text ein"),
    outputs=gr.Dataframe(label="Entitäten mit Sentiment"),
    title="NER + Sentiment Analyse (Deutsch)",
    description="Diese Demo verknüpft erkannte Entitäten mit Sentiment-Labels aus dem gleichen Satz.",
    allow_flagging="manual",
)

if __name__ == "__main__":
    demo.launch()