import gradio as gr
from setfit import SetFitModel

MODEL_ID = "jjprietotorres/labse-persuasion-detection-agnostic"

# Load model
model = SetFitModel.from_pretrained(MODEL_ID)

# Define class labels
labels = ["Supply Scarcity", "Authority Endorsement", "Misrepresentation", "Neutral", "Logical Appeal"]

def classify_text(text):
    """Classify input text and show persuasion category probabilities."""
    text = text.strip()
    if not text:
        return "⚠️ Please enter some text to analyze."

    # Get probability predictions
    probas = model.predict_proba([text])

    # Zip labels with probabilities
    label_scores = list(zip(labels, probas[0].tolist()))
    label_scores = sorted(label_scores, key=lambda x: x[1], reverse=True)

    # Build Markdown report
    report_lines = [f"### 🔍 Analysis Result for:\n> {text}\n"]
    report_lines.append("\n**Predicted persuasion categories:**\n")
    for label, score in label_scores:
        bar = "█" * int(score * 20)
        report_lines.append(f"- **{label}**: {score:.2f} {bar}")
    return "\n".join(report_lines)

# Define Gradio interface
demo = gr.Interface(
    fn=classify_text,
    inputs=gr.Textbox(lines=4, placeholder="Enter text to analyze persuasion...", label="Input Text"),
    outputs=gr.Markdown(label="Classification Report"),
    title="🧠 Persuasion Detection (LaBSE + SetFit)",
    description=(
        "Detect persuasive intent in text.\n\n"
        "Model: `jjprietotorres/labse-persuasion-detection-agnostic`\n"
        "Classes: Supply Scarcity · Authority Endorsement · Misrepresentation · Logical Appeal · Neutral\n\n"
        "Part of the **Time to Trust AI** initiative."
    ),
    theme="soft",
    examples=[
        ["Experts agree this is the only viable solution."],
        ["Hurry, offer ends tonight!"],
        ["Data clearly supports our argument."],
        ["I think everyone should consider this approach carefully."],
    ],
)

if __name__ == "__main__":
    demo.launch()