add model requirements

app.py

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+import os
+os.system('pip install transformers gradio torch')
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import gradio as gr
+
+
+tokenizer = AutoTokenizer.from_pretrained("EXt1/mdeberta-v3-base-thai-fakenews")  
+model = AutoModelForSequenceClassification.from_pretrained("EXt1/mdeberta-v3-base-thai-fakenews")  
+
+def classify_fake_news(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = logits.argmax().item()
+    
+    if predicted_class == 1:
+        return "Fake News"
+    else:
+        return "Real News"
+
+# Create Gradio interface
+gr.Interface(
+    fn=classify_fake_news,
+    inputs=gr.Textbox(lines=8, placeholder="Enter text here..."), 
+    outputs="text",
+    title="Thai Fake News Classification using BERT",
+    description="Classifies Thai News as Fake or Real with 91 percent accuracy using a fine-tuned BERT model",
+    theme="compact"
+).launch()

requirements.txt

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+transformers
+gradio
+torch