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ErlangshenModel
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import os
from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
import gradio as gr
from transformers import pipeline

DATA_FILE = "stress_data.csv"

if not os.path.exists(DATA_FILE):
    df = pd.DataFrame(columns=["date","score"])
    df.to_csv(DATA_FILE,index=False)

emotion_model = pipeline(
    "sentiment-analysis",
    model="IDEA-CCNL/Erlangshen-Roberta-110M-Sentiment"
)

stress_keywords = [
"焦慮","壓力","煩","崩潰","絕望","害怕","痛苦",
"失眠","難過","憂鬱","無助","失落","疲倦"
]

crisis_keywords = [
"不想活","活不下去","想死","絕望","沒有意義"
]

violence_keywords = [
"放火","殺人","報復","傷害"
]

def stress_level(score):

    if score < 30:
        return "低壓力"
    elif score < 60:
        return "中等壓力"
    elif score < 80:
        return "高壓力"
    else:
        return "非常高壓力"

def detect_source(text):

    if any(w in text for w in ["考試","成績","作業","報告"]):
        return "學業壓力"

    if any(w in text for w in ["朋友","同學","關係"]):
        return "人際壓力"

    if any(w in text for w in ["未來","人生","迷茫"]):
        return "未來焦慮"

    if any(w in text for w in ["失眠","睡不著"]):
        return "睡眠壓力"

    return "一般壓力"

advice = {

"學業壓力":{
"relief":"使用番茄鐘學習法，每40分鐘休息10分鐘",
"food":"增加B群食物：雞蛋、全穀類",
"life":"建立讀書計畫"
},

"人際壓力":{
"relief":"與信任的人聊聊",
"food":"Omega-3食物：魚類、堅果",
"life":"安排放鬆時間"
},

"未來焦慮":{
"relief":"寫下短期目標",
"food":"富含鎂食物：香蕉、菠菜",
"life":"每天運動30分鐘"
},

"睡眠壓力":{
"relief":"睡前冥想或深呼吸",
"food":"避免咖啡因",
"life":"睡前一小時不要滑手機"
},

"一般壓力":{
"relief":"散步或慢跑",
"food":"均衡飲食",
"life":"保持規律作息"
}
}

def analyze(text):

    result = emotion_model(text)[0]

    if result["label"] == "negative":
        ai_score = result["score"] * 100
    else:
        ai_score = (1-result["score"]) * 40

    kw_score = 0

    for w in stress_keywords:
        if w in text:
            kw_score += 8

    for w in crisis_keywords:
        if w in text:
            kw_score += 40

    for w in violence_keywords:
        if w in text:
            kw_score += 30

    total_score = min(ai_score*0.7 + kw_score*0.3 ,100)

    level = stress_level(total_score)

    source = detect_source(text)

    adv = advice[source]

    df = pd.read_csv(DATA_FILE)

    new = pd.DataFrame({
        "date":[datetime.now().strftime("%Y-%m-%d %H:%M")],
        "score":[total_score]
    })

    df = pd.concat([df,new],ignore_index=True)

    df.to_csv(DATA_FILE,index=False)

    fig, ax = plt.subplots()

    df["date"] = pd.to_datetime(df["date"])

    ax.plot(df["date"],df["score"],marker="o")

    ax.set_title("Stress Trend")

    fig.autofmt_xdate()

    avg = df["score"].mean()

    dashboard = f"""
目前壓力：{total_score:.1f}
平均壓力：{avg:.1f}
"""

    result_text = f"""
壓力分數：{total_score:.1f}

壓力等級：{level}

壓力來源：{source}

減壓方式：{adv['relief']}

飲食建議：{adv['food']}

生活建議：{adv['life']}
"""

    return result_text,dashboard,fig

interface = gr.Interface(

fn=analyze,

inputs=gr.Textbox(lines=4,label="輸入你的心情"),

outputs=[
gr.Textbox(label="分析結果"),
gr.Textbox(label="壓力儀表板"),
gr.Plot(label="壓力趨勢")
],

title="AI心理壓力分析系統"

)

interface.launch()