Update ui.py

ui.py

@@ -2,59 +2,34 @@ import gradio as gr
 import matplotlib.pyplot as plt
 from sentiment import analyze_sentiment
 
-# 📌 定義可選擇的模型（顯示中文名稱）
-MODEL_OPTIONS = {
-    "多語言推特情緒分析 (XLM-RoBERTa)": "cardiffnlp/twitter-xlm-roberta-base-sentiment",
-    "多語言情緒分析 (BERT)": "nlptown/bert-base-multilingual-uncased-sentiment",
-    "英語情緒分析 (DistilBERT)": "distilbert-base-uncased-finetuned-sst-2-english"
-}
-
-# 📌 生成信心度視覺化圖表
+# 📌 生成簡單的信心度條狀圖
 def plot_confidence(score):
-    fig, ax = plt.subplots()
-    categories = ["低", "中等", "高", "極高"]
-    levels = [0.3, 0.5, 0.75, 0.9]
-    
-    confidence_category = next((cat for cat, lvl in zip(categories, levels) if score < lvl), "極高")
-    
-    ax.bar(categories, levels, color=["red", "orange", "yellow", "green"])
-    ax.set_ylim([0, 1])
-    ax.set_title(f"信心度分析 ({confidence_category})")
+    fig, ax = plt.subplots(figsize=(4, 1))  # 調整圖表尺寸
+    ax.barh(["信心度"], [score], color="blue")  # 橫向條狀圖
+    ax.set_xlim([0, 1])  # 設定範圍為 0-1
+    ax.set_xticks([0, 0.25, 0.5, 0.75, 1])  # 設定刻度
+    ax.set_xlabel("信心度（百分比）")  # 標題
     return fig
 
-# 📌 產生可下載的分析報告
-def generate_report(text, result, model_name):
-    report = f"🔍 **分析報告**\n\n📝 **輸入內容**: {text}\n\n📊 **分析結果**:\n{result}\n\n🤖 **使用模型**: {model_name}"
-    return report
-
 # 📌 建立 Gradio 介面
 def create_ui():
     with gr.Blocks(theme=gr.themes.Soft()) as iface:
-        gr.Markdown("# 🎯 多語言情緒分析 AI\n請輸入一段文字，選擇模型，AI 會分析其情緒。")
+        gr.Markdown("# 🎯 多語言情緒分析 AI")
+        gr.Markdown("請輸入一段文字，AI 會分析其情緒，並提供信心度。")
         
-        with gr.Row():
-            text_input = gr.Textbox(lines=3, placeholder="請輸入文本（支援多語言）...", label="輸入文本")
-        
-        with gr.Row():
-            model_selector = gr.Dropdown(choices=list(MODEL_OPTIONS.keys()), value="多語言推特情緒分析 (XLM-RoBERTa)", label="選擇 AI 模型")
+        text_input = gr.Textbox(lines=3, placeholder="請輸入文本...", label="輸入文本")
 
         analyze_button = gr.Button("分析情緒")
 
-        with gr.Row():
-            result_output = gr.Markdown(label="分析結果")
-            plot_output = gr.Plot(label="信心度圖表")
-        
-        report_output = gr.File(label="下載報告")
+        result_output = gr.Markdown(label="分析結果")
+        plot_output = gr.Plot(label="信心度")
 
         # 📌 綁定按鈕功能
-        def process_analysis(text, model_name):
-            model_id = MODEL_OPTIONS[model_name]  # 轉換中文名稱為模型 ID
-            result = analyze_sentiment(text, model_id)  # 調用 API 進行分析
-            confidence_score = float(result.split("信心度為**: ")[1].split("%")[0]) / 100
-            plot = plot_confidence(confidence_score)
-            report = generate_report(text, result, model_name)
-            return result, plot, report
+        def process_analysis(text):
+            result, confidence_score = analyze_sentiment(text)  # 調用 API 進行分析
+            plot = plot_confidence(confidence_score)  # 生成條狀圖
+            return result, plot
 
-        analyze_button.click(process_analysis, inputs=[text_input, model_selector], outputs=[result_output, plot_output, report_output])
+        analyze_button.click(process_analysis, inputs=text_input, outputs=[result_output, plot_output])
 
     return iface