import gradio as gr

def predict(text):
    # Tokenize the input text
    inputs = tokenizer(text, return_tensors="pt")
    # Get the model outputs
    outputs = model(**inputs)
    # Apply softmax to the logits to get probabilities
    scores = torch.nn.functional.softmax(outputs.logits, dim=1)
    # Get the predicted label
    predicted_label_idx = scores.argmax(dim=1).item()
    labels = ["negative", "neutral", "positive"]
    predicted_label = labels[predicted_label_idx]
    confidence_score = scores[0, predicted_label_idx].item()
    # Create a dictionary with the prediction and scores
    result = {
        "text": text,
        "label": predicted_label,
        "score": confidence_score,
        "scores": {
            "positive": scores[0, 2].item(),
            "neutral": scores[0, 1].item(),
            "negative": scores[0, 0].item()
        }
    }
    return result

iface = gr.Interface(
    fn=predict,
    inputs="text",
    outputs=[
        gr.Textbox(label="Prediction"),
        gr.Label(label="Label Confidence")
    ],
    title="Hellenic Sentiment AI",
    description=None,
    article=None,
    theme="default",
    flagging_dir=None,
    share=True,
    favicon_path=None,
    css=None,
    analytics_script=None,
    allow_flagging="never",
    allow_screenshot=True,
    enable_queue=True,
    show_input=True,
    show_output=True,
    footer="Development by Geo Sar"
)

iface.launch()