Update app.py

app.py

@@ -1,22 +1,43 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import AutoTokenizer, AutoModelForTokenClassification, AutoConfig
 
-# Load model and tokenizer
+# Load configuration manually if it's missing in the repository
+try:
+    config = AutoConfig.from_pretrained("viv/UD_Greek-GUD")
+except OSError:
+    # Fallback: Create a configuration manually
+    config = AutoConfig.from_dict({
+        "model_type": "bert",  # Change this based on your model type
+        "hidden_size": 768,
+        "num_attention_heads": 12,
+        "num_hidden_layers": 12,
+        "vocab_size": 30000,
+        "max_position_embeddings": 512,
+        "type_vocab_size": 2,
+        "hidden_act": "gelu",
+        "layer_norm_eps": 1e-12,
+        "initializer_range": 0.02
+    })
+
+# Load model and tokenizer with the provided or fallback config
 tokenizer = AutoTokenizer.from_pretrained("viv/UD_Greek-GUD")
-model = AutoModelForTokenClassification.from_pretrained("viv/UD_Greek-GUD")
+model = AutoModelForTokenClassification.from_pretrained("viv/UD_Greek-GUD", config=config)
 
+# Prediction function
 def predict(text):
     inputs = tokenizer(text, return_tensors="pt")
     outputs = model(**inputs)
+    # Process and return predictions
     return outputs.logits.argmax(-1).tolist()
 
-# Interface
+# Gradio Interface
 interface = gr.Interface(
     fn=predict,
     inputs="text",
     outputs="text",
     title="UD Greek GUD Model",
-    description="Upload a text for analysis using the UD Greek GUD model.",
+    description="Analyze text using the UD Greek GUD model.",
 )
 
+# Launch interface
 interface.launch()