import gradio as gr
from transformers import pipeline

MODEL_NAME = "cardiffnlp/twitter-roberta-base-emotion-multilabel-latest"

classifier = pipeline("text-classification", model=MODEL_NAME, top_k=None)

DISCLAIMER = """
This demo detects patterns in self-provided text that may indicate support-seeking or distress-related language.
It is for educational demonstration only and is NOT a diagnostic, disciplinary, or clinical tool.
If someone may be in immediate danger, contact emergency services or a qualified crisis resource.
"""

DISTRESS_KEYWORDS = {
    "sadness", "fear", "anger", "disgust", "negative"
}

def analyze_text(text):
    if not text or not text.strip():
        return "Please enter some text.", {}

    results = classifier(text[:512])[0]

    label_scores = {item["label"]: float(item["score"]) for item in results}

    distress_score = 0.0
    for label, score in label_scores.items():
        if label.lower() in DISTRESS_KEYWORDS:
            distress_score += score

    if distress_score >= 0.60:
        assessment = "Potential support-seeking / distress-related language detected"
    elif distress_score >= 0.30:
        assessment = "Some distress-related language detected"
    else:
        assessment = "No strong distress-related language detected"

    return assessment, label_scores

demo = gr.Interface(
    fn=analyze_text,
    inputs=gr.Textbox(
        lines=6,
        label="Student text input",
        placeholder="Enter a message, reflection, or post for analysis..."
    ),
    outputs=[
        gr.Textbox(label="Assessment"),
        gr.Label(label="Model scores")
    ],
    title="Student Support-Seeking Language Demo",
    description=DISCLAIMER,
    examples=[
        ["I feel overwhelmed and I do not know who to talk to anymore."],
        ["I am stressed about finals but I think I can manage."],
        ["I had a good day and finished my homework."],
    ],
    allow_flagging="never"
)

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