f

app.py

@@ -1,20 +1,29 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+from transformers.utils import logging
 
-# Load the model and tokenizer with `trust_remote_code=True`
+# Enable logging to see debug messages
+logging.set_verbosity_info()
+
+# Import custom configuration and model classes
+from transformers_modules.deepseek_ai.DeepSeek_R1.configuration_deepseek import DeepseekV3Config
+from transformers_modules.deepseek_ai.DeepSeek_R1.modeling_deepseek import DeepseekV3Model
+
+# Load model and tokenizer
 model_name = "deepseek-ai/DeepSeek-R1"
 tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True)
+config = DeepseekV3Config.from_pretrained(model_name, trust_remote_code=True)
+model = DeepseekV3Model.from_pretrained(model_name, config=config, trust_remote_code=True)
 
 def classify_text(input_text):
-    # Tokenize the input
+    # Tokenize input
     inputs = tokenizer(input_text, return_tensors="pt")
-    # Get predictions
+    # Get model output
     outputs = model(**inputs)
     probabilities = outputs.logits.softmax(dim=-1).detach().numpy()
     return {f"Class {i}": prob for i, prob in enumerate(probabilities[0])}
 
-# Create the Gradio interface
+# Create Gradio interface
 interface = gr.Interface(
     fn=classify_text,
     inputs=gr.Textbox(label="Enter Text"),