Update app.py

app.py

@@ -1,6 +1,10 @@
 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"
@@ -22,26 +26,20 @@ model_options = {
 # ✅ 页面配置
 st.set_page_config(page_title="Emoji Offensive Text Detector", page_icon="🚨", layout="wide")
 
-# ✅ 页面标题
-st.title("🧠 Emoji-based Offensive Language Classifier")
+# ✅ 侧边栏: 选择模型
+with st.sidebar:
+    st.header("🧠 Settings")
+    moderation_type = st.selectbox("Select Task Type", ["Normal Text", "Bullet Screen Text"])
+    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)
 
-st.markdown("""
-This application translates emojis in a sentence and classifies whether the final sentence is offensive or not using two AI models.
-- The **first model** translates emoji or symbolic phrases into standard Chinese text.
-- The **second model** performs offensive language detection.
-""")
+# 初始化历史记录
+if "history" not in st.session_state:
+    st.session_state.history = []
 
-# Streamlit 侧边栏模型选择
-selected_model = st.sidebar.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)
+# 核心函数: 翻译并分类
 
-# ✅ 输入区域
-st.markdown("### ✍️ Input your sentence:")
-default_text = "你是🐷"
-text = st.text_area("Enter sentence with emojis:", value=default_text, height=150)
-
-# ✅ 主逻辑封装函数
 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)
@@ -53,21 +51,70 @@ def classify_emoji_text(text: str):
     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(f"#### 🧠 Model Explanation:")
+                st.info(reason)
+            except Exception as e:
+                st.error(f"❌ An error occurred during processing:\n\n{e}")
 
-    return translated_text, label, score
+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)
 
-# ✅ 触发按钮
-if st.button("🚦 Analyze"):
-    with st.spinner("🔍 Processing..."):
-        try:
-            translated, label, score = classify_emoji_text(text)
-            st.markdown("### 🔄 Translated sentence:")
-            st.code(translated, language="text")
+# 分析仪表盘
+st.markdown("---")
+st.title("📊 Violation Analysis Dashboard")
+if st.session_state.history:
+    df = pd.DataFrame(st.session_state.history)
+    # 饼图
+    label_counts = df["label"].value_counts().reset_index()
+    label_counts.columns = ["Category", "Count"]
+    fig = px.pie(label_counts, names="Category", values="Count", title="Offensive Category Distribution")
+    st.plotly_chart(fig)
 
-            st.markdown(f"### 🎯 Prediction: `{label}`")
-            st.markdown(f"### 📊 Confidence Score: `{score:.2%}`")
+    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']}")
 
-        except Exception as e:
-            st.error(f"❌ An error occurred during processing:\n\n{e}")
+    # 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")
+    st.plotly_chart(radar_fig)
 else:
-    st.info("👈 Please input text and click the button to classify.")
+    st.info("⚠️ No classification data available yet.")