import gradio as gr
import joblib
import re
import contractions

# Load model and encoder
model = joblib.load("tfidf_baseline.pkl")
le    = joblib.load("label_encoder.pkl")

# Signal categories
signal_categories = {
    "Side Effects":     ["side effect", "reaction", "rash", "nausea", "vomit",
                         "dizzy", "dizziness", "headache", "itching", "swelling"],
    "Weight Changes":   ["weight gain", "weight loss", "gained weight", "bloating"],
    "Mental Health":    ["depression", "anxiety", "mood", "suicidal", "panic",
                         "mental", "emotional", "crying", "mood swing"],
    "Sleep Issues":     ["insomnia", "sleep", "tired", "fatigue", "exhausted",
                         "drowsy", "can not sleep"],
    "Pain":             ["pain", "cramps", "cramping", "ache", "burning", "soreness"],
    "Ineffectiveness":  ["not work", "didn t work", "no effect", "useless",
                         "ineffective", "did nothing"],
    "Withdrawal":       ["withdrawal", "stopped", "quit", "discontinue",
                         "coming off", "weaning"],
    "Hormonal Effects": ["period", "bleeding", "spotting", "hormonal",
                         "menstrual", "libido", "sex drive"],
    "Digestive Issues": ["stomach", "diarrhea", "constipation", "nausea",
                         "bowel", "gut", "acid", "heartburn"],
    "Access & Cost":    ["expensive", "cost", "afford", "insurance", "price"]
}

def clean_text(text):
    if not isinstance(text, str):
        return ""
    text = contractions.fix(text)
    text = text.replace("'", "'").replace("&", "and")
    text = text.lower()
    text = re.sub(r"http\S+|www\S+", "", text)
    text = re.sub(r"[^a-z\s]", "", text)
    text = re.sub(r"\s+", " ", text).strip()
    return text

def extract_signals(text):
    found = []
    for category, keywords in signal_categories.items():
        if any(kw in text for kw in keywords):
            found.append(category)
    return found

def analyze_review(review_text, condition):
    if not review_text.strip():
        return "Please enter a review.", ""

    condition = condition.strip().lower() if condition.strip() else "unknown"
    clean     = clean_text(review_text)
    combined  = condition + " " + clean

    pred_label = model.predict([combined])[0]
    sentiment  = le.inverse_transform([pred_label])[0]
    proba      = model.predict_proba([combined])[0]
    confidence = round(float(max(proba)) * 100, 1)

    emoji_map = {"Positive": "🟢", "Neutral": "🟡", "Negative": "🔴"}
    sentiment_output = f"{emoji_map[sentiment]} {sentiment}  ({confidence}% confidence)"

    if sentiment == "Negative":
        signals = extract_signals(clean)
        if signals:
            signals_output = "⚠️ Detected Adverse Signals:\n" + "\n".join(f"  • {s}" for s in signals)
        else:
            signals_output = "⚠️ Negative review — no specific signal category detected."
    else:
        signals_output = "No adverse signals flagged for non-negative reviews."

    return sentiment_output, signals_output

# Gradio UI
with gr.Blocks(title="MedReview Intelligence") as demo:
    gr.Markdown("""
    # 🏥 MedReview Intelligence
    ### Clinical Feedback Analyzer — Adverse Signal Detection from Patient Drug Reviews
    *Built by Samuel Yaula Dutse*
    """)

    with gr.Row():
        with gr.Column():
            review_input = gr.Textbox(
                label="Patient Review",
                placeholder="Enter a patient drug review here...",
                lines=5
            )
            condition_input = gr.Textbox(
                label="Medical Condition (optional)",
                placeholder="e.g. Depression, Birth Control, Diabetes..."
            )
            analyze_btn = gr.Button("Analyze Review", variant="primary")

        with gr.Column():
            sentiment_output = gr.Textbox(label="Sentiment", interactive=False)
            signals_output   = gr.Textbox(label="Adverse Signals Detected", lines=6, interactive=False)

    gr.Examples(
        examples=[
            ["This medication has been a lifesaver. No side effects and my condition improved within weeks.", "Depression"],
            ["I gained 15 pounds in 2 months and the mood swings are unbearable. I had to stop taking it.", "Birth Control"],
            ["It works okay I guess. Not great but not terrible either. Still adjusting.", "Anxiety"],
        ],
        inputs=[review_input, condition_input]
    )

    analyze_btn.click(
        fn=analyze_review,
        inputs=[review_input, condition_input],
        outputs=[sentiment_output, signals_output]
    )

demo.launch()