Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the trained model and tokenizer
+model_path = 'viv/AIKIA'
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Preprocessing function for Greek text
+def preprocessing_greek(text):
+    # Your preprocessing steps
+    text = text.lower()  # Example step
+    return text
+
+# Prediction function
+def predict(sentence):
+    model.eval()
+    preprocessed_sentence = preprocessing_greek(sentence)
+    inputs = tokenizer(preprocessed_sentence, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.nn.functional.softmax(logits, dim=1)
+    predicted_label = torch.argmax(probabilities, dim=1).item()
+    labels_map = {0: 'NOT', 1: 'OFFENSIVE'}
+    return labels_map[predicted_label], probabilities.tolist()
+
+# Gradio Interface
+iface = gr.Interface(fn=predict, inputs="text", outputs=["text", "json"])
+iface.launch()