Update app.py

app.py

@@ -1,3 +1,62 @@
 import gradio as gr
+import torch
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-gr.load("models/VietTung04/videberta-base-topic-classification").launch()
+tokenizer = AutoTokenizer.from_pretrained("VietTung04/videberta-base-topic-classification")
+model = AutoModelForSequenceClassification.from_pretrained("VietTung04/videberta-base-topic-classification")
+
+def preprocess_fn(text):
+    stopword_path = 'vietnamese-stopwords.txt'
+    
+    with open(stopword_path, 'r', encoding='utf-8') as file:
+        stopwords = file.read().splitlines()
+    
+    def remove_stopwords(tokens):
+        return [word for word in tokens if word not in stopwords]
+    text = re.sub(r'http\S+', ' ', text)  # Remove URLs
+    text = re.sub(r'#\w+', ' ', text)  # Remove hashtags
+    text = re.sub(r'@\w+', ' ', text)  # Remove mentions
+    text = re.sub(r'\d+', ' ', text)  # Remove numbers
+    text = re.sub(r'[^\w\sđĐàÀảẢãÃáÁạẠăĂằẰẳẲẵẴắẮặẶâÂầẦẩẨẫẪấẤậẬèÈẻẺẽẼéÉẹẸêÊềỀểỂễỄếẾệỆìÌỉỈĩĨíÍịỊòÒỏỎõÕóÓọỌôÔồỒổỔỗỖốỐộỘơƠờỜởỞỡỠớỚợỢùÙủỦũŨúÚụỤưƯừỪửỬữỮứỨựỰỳỲỷỶỹỸýÝỵỴ]', ' ', text)  # Remove special characters
+    # Tokenize Vietnamese text
+    tokens = word_tokenize(' '.join(text.split()).lower())
+    
+    # Remove stop words
+    tokens = remove_stopwords(tokens)
+
+    return ' '.join(tokens)
+
+def predict_topic(text):
+    inputs = tokenizer(
+        preprocess_fn(text),
+        truncation=True,
+        padding='max_length',
+        max_length=512,
+        add_special_tokens=True,
+        return_tensors='pt'
+    )
+    inputs = {key: value.to(device) for key, value in inputs.items()}
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=1).cpu().numpy()[0]
+
+    # Get the top 3 classes
+    top3_indices = probabilities.argsort()[-3:][::-1]
+    top3_probabilities = probabilities[top3_indices]
+    top3_classes = [model.config.id2label[idx] for idx in top3_indices]  # Assuming your model has this attribute
+
+    return {top3_classes[i]: float(top3_probabilities[i]) for i in range(3)}
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_topic,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your text here..."),
+    outputs=gr.Label(num_top_classes=3),
+    title="Text Classification",
+    description="Enter text to classify it into different categories and get the probability for each class."
+)
+
+# Launch the interface
+iface.launch()