Create app.py

app.py

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+import os
+import tarfile
+import gdown
+import spacy
+import gradio as gr
+
+# URL dan path file model
+drive_url = "https://drive.google.com/uc?id=1bOJ3fN9UeOKVam8SHYxDUDpb7ihNph1L"
+tar_file = "xx_cv_parsing_ner.tar.gz"
+extract_path = "xx_cv_parsing_ner"
+
+# Cek apakah sudah diekstrak
+if not os.path.exists(extract_path):
+    print("Mengunduh model...")
+    gdown.download(drive_url, tar_file, quiet=False)
+    
+    print("Mengekstrak model...")
+    with tarfile.open(tar_file, "r:gz") as tar:
+        tar.extractall(path=extract_path)
+
+# Load spaCy model
+model_path = os.path.join(
+    extract_path, "xx_cv_parsing_ner-0.0.1", "xx_CV_Parsing_NER", "xx_CV_Parsing_NER-0.0.1"
+)
+nlp = spacy.load(model_path)
+
+# Fungsi ekstraksi entitas
+def extract_entities(text):
+    doc = nlp(text)
+    return [(ent.text, ent.label_) for ent in doc.ents]
+
+# Gradio UI
+demo = gr.Interface(
+    fn=extract_entities,
+    inputs=gr.Textbox(lines=15, label="Masukkan Teks CV"),
+    outputs=gr.Dataframe(headers=["Entity", "Label"], label="Hasil Ekstraksi"),
+    title="Ekstraksi Entitas CV",
+)
+
+# Jalankan
+if __name__ == "__main__":
+    demo.launch()