Create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the pre-trained NER model
+model = pipeline("ner", model="/home/user/app/mendobert/", tokenizer="indolem/indobert-base-uncased")
+# basemodel = pipeline("ner", model="/home/user/app/base-model/", tokenizer="indolem/indobert-base-uncased")
+
+def text_analysis(text):
+    doc = model(text)
+    html = displacy.render(doc, style="dep", page=True)
+    html = (
+        "<div style='max-width:100%; max-height:360px; overflow:auto'>"
+        + html
+        + "</div>"
+    )
+    pos_count = {
+        "char_count": len(text),
+        "token_count": 0,
+    }
+    pos_tokens = []
+
+    for token in doc:
+        pos_tokens.extend([(token.text, token.pos_), (" ", None)])
+
+    return pos_tokens, pos_count, html
+
+demo = gr.Interface(
+    text_analysis,
+    gr.Textbox(placeholder="Enter sentence here..."),
+    ["highlight", "json", "html"],
+    examples=[
+        ["Aspartylglucosaminuria (AGU) adalah gangguan metabolisme glikoprotein langka."],
+        ["Mutasi germ - line dari gen BRCA1 membuat wanita cenderung mengalami kanker payudara dini dengan mengorbankan fungsi presumtif gen sebagai penekan tumor."],
+    ],
+)
+
+demo.launch()