Update app.py

app.py

@@ -1,38 +1,29 @@
 import gradio as gr
-import transformers
 
 # Load the model
-model_name = "alexue4/text-normalization-ru-new"
-model = transformers.AutoModelForSequenceClassification.from_pretrained(model_name)
-tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+interface = gr.load("models/alexue4/text-normalization-ru-new")
 
-# Define the custom function
+# Function to modify the behavior (example: limiting max tokens and setting examples)
 def custom_function(input_text):
-    max_tokens = 100  # Set your desired max token limit
-    tokens = tokenizer.tokenize(input_text)
+    # Custom processing here (e.g., limit max tokens)
+    max_tokens = 250  # Set your desired max token limit
+    if len(input_text.split()) > max_tokens:
+        input_text = ' '.join(input_text.split()[:max_tokens])
     
-    if len(tokens) > max_tokens:
-        tokens = tokens[:max_tokens]
-        input_text = tokenizer.convert_tokens_to_string(tokens)
-    
-    inputs = tokenizer(input_text, return_tensors="pt")
-    outputs = model(**inputs)
-    prediction = outputs.logits.argmax(dim=-1).item()
-    
-    return prediction
+    # Call the original function from the interface
+    original_output = interface.predict(input_text)
+    return original_output
 
 examples = [
     ["в 2006-2010 гг. Вася учился в МГУ"],
     ["я купил iphone 10X за 14990 руб без 3-x часов полдень и т.д."]
 ]
 
-interface = gr.Interface(
+gr.Interface(
     fn=custom_function,
     inputs="text",
     outputs="text",
     examples=examples
-)
+).launch()
 
-# Launch the interface
-interface.launch()