Create app.py

app.py

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+import gradio as gr
+import torch
+from khmernltk import word_tokenize
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load your model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained(
+    "./final_model", 
+    # load_in_8bit=True,  # Use if you want to load in 8-bit quantized format
+    # torch_dtype=torch.float16,  # Use appropriate dtype based on your GPU
+    # device_map="cuda:0"  # Automatically map model to available devices
+)
+tokenizer = AutoTokenizer.from_pretrained("./final_model")
+
+# Ensure the model is in evaluation mode
+model.eval()
+
+class_labels = {
+    0: "non-accident",
+    1: "accident"
+    # Add more labels if you have more classes
+}
+
+# Define the inference function
+def classify(text):
+    
+    words = word_tokenize(text)
+    sent = ' '.join(words)
+    print(f'sent : {sent}')
+    encoded_dict = tokenizer.encode_plus(
+                    sent,                      # Sentence to encode.
+                    add_special_tokens = True, # Add '[CLS]' and '[SEP]'
+                    max_length = 512,      # 64 Pad & truncate all sentences.
+                    pad_to_max_length = True,
+                    return_attention_mask = True,   # Construct attn. masks.
+                    return_tensors = 'pt',     # Return pytorch tensors.
+                    )
+    input_ids = encoded_dict['input_ids']
+    attention_masks = encoded_dict['attention_mask']
+    with torch.no_grad():  # Disable gradient calculation
+        outputs = model(input_ids, attention_masks)
+    logits = outputs.logits
+    predictions = torch.argmax(logits, dim=-1)
+    return class_labels[predictions.item()]
+
+# Set up Gradio interface
+interface = gr.Interface(fn=classify, 
+                         inputs="text", 
+                         outputs="text",
+                         title="Accident Classification",
+                         description="Enter a text to classify it.")
+
+# Launch the interface
+interface.launch()