Update app.py

app.py

@@ -1,23 +1,7 @@
 from typing import Dict, Union
 import sys
-sys.path.extend(["./GLiNER"])
-from GLiNER.model import GLiNER
+from gliner import GLiNER
 import gradio as gr
-import os
-from transformers import AutoModel, AutoTokenizer, logging
-from huggingface_hub import HfFolder
-
-# Set the Hugging Face logging to error only to avoid sensitive info in logs
-logging.set_verbosity_error()
-
-# Retrieve the HF API key from the environment
-hf_api_key = os.getenv('HF_KEY')
-
-# If the key is found, use it to authenticate
-if hf_api_key:
-    HfFolder.save_token(hf_api_key)  # This authenticates you for this session
-else:
-    print("No HF_KEY found. Please make sure you've set up your Hugging Face API key as an environment variable.")
 
 
 model = GLiNER.from_pretrained("DeepMount00/universal_ner_ita").eval()
@@ -76,7 +60,9 @@ examples = [
     ],
 ]
 
-def ner(text, labels: str, nested_ner: bool) -> Dict[str, Union[str, int, float]]:
+def ner(
+    text, labels: str, threshold: float, nested_ner: bool
+) -> Dict[str, Union[str, int, float]]:
     labels = labels.split(",")
     return {
         "text": text,
@@ -88,7 +74,9 @@ def ner(text, labels: str, nested_ner: bool) -> Dict[str, Union[str, int, float]
                 "end": entity["end"],
                 "score": 0,
             }
-            for entity in model.predict_entities(text, labels, flat_ner=not nested_ner)
+            for entity in model.predict_entities(
+                text, labels, flat_ner=not nested_ner, threshold=threshold
+            )
         ],
     }