from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import torch
import streamlit as st
from PIL import Image
import pytesseract
import pandas as pd
import plotly.express as px

# ✅ Step 1: Emoji 翻译模型（你自己训练的模型）
emoji_model_id = "JenniferHJF/qwen1.5-emoji-finetuned"
emoji_tokenizer = AutoTokenizer.from_pretrained(emoji_model_id, trust_remote_code=True)
emoji_model = AutoModelForCausalLM.from_pretrained(
    emoji_model_id,
    trust_remote_code=True,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
).to("cuda" if torch.cuda.is_available() else "cpu")
emoji_model.eval()

# ✅ Step 2: 可选择的冒犯性文本识别模型
model_options = {
    "Toxic-BERT": "unitary/toxic-bert",
    "Roberta Offensive": "cardiffnlp/twitter-roberta-base-offensive",
    "BERT Emotion": "bhadresh-savani/bert-base-go-emotion"
}

# ✅ 页面配置
st.set_page_config(page_title="Emoji Offensive Text Detector", page_icon="🚨", layout="wide")

# ✅ 侧边栏: 选择模型
with st.sidebar:
    st.header("🧠 Settings")
    selected_model = st.selectbox("Choose classification model", list(model_options.keys()))
    selected_model_id = model_options[selected_model]
    classifier = pipeline("text-classification", model=selected_model_id,
                          device=0 if torch.cuda.is_available() else -1)

# 初始化历史记录
if "history" not in st.session_state:
    st.session_state.history = []

# 核心函数: 翻译并分类
def classify_emoji_text(text: str):
    prompt = f"输入：{text}\n输出："
    input_ids = emoji_tokenizer(prompt, return_tensors="pt").to(emoji_model.device)
    with torch.no_grad():
        output_ids = emoji_model.generate(**input_ids, max_new_tokens=64, do_sample=False)
    decoded = emoji_tokenizer.decode(output_ids[0], skip_special_tokens=True)
    translated_text = decoded.split("输出：")[-1].strip() if "输出：" in decoded else decoded.strip()

    result = classifier(translated_text)[0]
    label = result["label"]
    score = result["score"]
    reasoning = (
        f"The sentence was flagged as '{label}' due to potentially offensive phrases. "
        "Consider replacing emotionally charged, ambiguous, or abusive terms."
    )

    st.session_state.history.append({
        "text": text,
        "translated": translated_text,
        "label": label,
        "score": score,
        "reason": reasoning
    })
    return translated_text, label, score, reasoning

# 页面主体
st.title("🚨 Emoji Offensive Text Detector & Analysis")

# 输入区域
st.markdown("### ✍️ Input your sentence or upload screenshot:")
col1, col2 = st.columns(2)
with col1:
    default_text = "你是🐷"
    text = st.text_area("Enter sentence with emojis:", value=default_text, height=150)
    if st.button("🚦 Analyze Text"):
        with st.spinner("🔍 Processing..."):
            try:
                translated, label, score, reason = classify_emoji_text(text)
                st.markdown("#### 🔄 Translated sentence:")
                st.code(translated, language="text")

                st.markdown(f"#### 🎯 Prediction: {label}")
                st.markdown(f"#### 📊 Confidence Score: {score:.2%}")
                st.markdown("#### 🧠 Model Explanation:")
                st.info(reason)
            except Exception as e:
                st.error(f"❌ An error occurred during processing:\n\n{e}")

with col2:
    uploaded_file = st.file_uploader("Upload an image (JPG/PNG)", type=["jpg", "jpeg", "png"])
    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded Screenshot", use_column_width=True)
        if st.button("🛠️ OCR & Analyze Image"):
            with st.spinner("🧠 Extracting text via OCR..."):
                ocr_text = pytesseract.image_to_string(image, lang="chi_sim+eng").strip()
                st.markdown("#### 📋 Extracted Text:")
                st.code(ocr_text)
                classify_emoji_text(ocr_text)

# 分析仪表盘
st.markdown("---")
st.title("📊 Violation Analysis Dashboard")

if st.session_state.history:
    st.markdown("### 🧾 Offensive Terms & Suggestions")
    for item in st.session_state.history:
        st.markdown(f"- 🔹 **Input:** {item['text']}")
        st.markdown(f"   - ✨ **Translated:** {item['translated']}")
        st.markdown(f"   - ❗ **Label:** {item['label']} with **{item['score']:.2%}** confidence")
        st.markdown(f"   - 🔧 **Suggestion:** {item['reason']}")

    # 雷达图演示示例（可替换为动态数据）
    radar_df = pd.DataFrame({
        "Category": ["Insult", "Abuse", "Discrimination", "Hate Speech", "Vulgarity"],
        "Score": [0.7, 0.4, 0.3, 0.5, 0.6]
    })
    radar_fig = px.line_polar(
        radar_df,
        r='Score',
        theta='Category',
        line_close=True,
        title="⚠️ Risk Radar by Category",
        color_discrete_sequence=['black'],
        template='simple_white'
    )
    radar_fig.update_layout(
        polar=dict(
            gridshape='circular',
            bgcolor='white',
            radialaxis=dict(
                showticklabels=False,
                ticks='',
                showgrid=True,
                gridcolor='lightgrey',
                gridwidth=1,
                linecolor='black',
                linewidth=2
            ),
            angularaxis=dict(
                showticklabels=False,
                ticks='',
                showline=True,
                linecolor='black',
                linewidth=2
            )
        ),
        paper_bgcolor='white',
        plot_bgcolor='white'
    )
    st.plotly_chart(radar_fig)
else:
    st.info("⚠️ No classification data available yet.")