Update app.py

app.py

@@ -133,16 +133,18 @@ class RecommendationSystem:
 def create_gradio_interface(recommendation_system: RecommendationSystem) -> gr.Interface:
     with gr.Blocks(theme=gr.themes.Soft()) as interface:
         gr.Markdown("""
-        # 推文推荐系统
-        调整权重以获取个性化推荐
+        # Tweet Recommendation System
+        Adjust weights to get personalized recommendations
+        
+        Note: To protect user privacy, some tweet content has been redacted or anonymized.
         """)
         
         with gr.Row():
             with gr.Column(scale=1):
-                visibility_weight = gr.Slider(0, 1, 0.5, label="可信度权重", info="调整对内容可信度的重视程度")
-                sentiment_weight = gr.Slider(0, 1, 0.3, label="情感倾向权重", info="调整对情感倾向的重视程度")
-                popularity_weight = gr.Slider(0, 1, 0.2, label="热度权重", info="调整对内容热度的重视程度")
-                submit_btn = gr.Button("获取推荐", variant="primary")
+                visibility_weight = gr.Slider(0, 1, 0.5, label="Credibility Weight", info="Adjust importance of content credibility")
+                sentiment_weight = gr.Slider(0, 1, 0.3, label="Sentiment Weight", info="Adjust importance of emotional tone")
+                popularity_weight = gr.Slider(0, 1, 0.2, label="Popularity Weight", info="Adjust importance of engagement metrics")
+                submit_btn = gr.Button("Get Recommendations", variant="primary")
 
             with gr.Column(scale=2):
                 output_html = gr.HTML()
@@ -150,39 +152,37 @@ def create_gradio_interface(recommendation_system: RecommendationSystem) -> gr.I
         def format_recommendations(raw_recommendations):
             html = '<div style="font-family: sans-serif;">'
             
-            # 添加评分说明
             html += '''
             <div style="margin-bottom: 20px; padding: 15px; background-color: #f5f5f5; border-radius: 8px;">
-                <h3 style="margin-top: 0;">评分说明</h3>
+                <h3 style="margin-top: 0;">Score Guide</h3>
                 <ul style="margin: 0;">
-                    <li><strong>可信度</strong>：内容的可信程度评估</li>
-                    <li><strong>情感倾向</strong>：文本的情感分析（积极/消极/中性）</li>
-                    <li><strong>热度</strong>：基于点赞和转发的归一化分数</li>
+                    <li><strong>Credibility</strong>: Assessment of content reliability</li>
+                    <li><strong>Sentiment</strong>: Text emotional analysis (Positive/Negative/Neutral)</li>
+                    <li><strong>Popularity</strong>: Normalized score based on likes and retweets</li>
                 </ul>
             </div>
             '''
             
-            # 显示推荐的tweets
             for i, rec in enumerate(raw_recommendations["recommendations"], 1):
                 scores = rec["scores"]
                 html += f'''
                 <div style="margin-bottom: 15px; padding: 15px; border: 1px solid #ddd; border-radius: 8px;">
                     <div style="margin-bottom: 10px; font-size: 1.1em;">{rec["text"]}</div>
-                    <div style="display: flex; flex-wrap: wrap; gap: 10px; font-size: 0.9em; color: #666;">
-                        <span style="padding: 3px 8px; background-color: #e3f2fd; border-radius: 4px;">
-                            总分: {scores["总分"]}
+                    <div style="display: flex; flex-wrap: wrap; gap: 10px; font-size: 0.9em;">
+                        <span style="padding: 3px 8px; background-color: #1976d2; color: white; border-radius: 4px;">
+                            Score: {scores["总分"]}
                         </span>
-                        <span style="padding: 3px 8px; background-color: #e8f5e9; border-radius: 4px;">
-                            可信度: {scores["可信度"]}
+                        <span style="padding: 3px 8px; background-color: #2e7d32; color: white; border-radius: 4px;">
+                            Credibility: {scores["可信度"]}
                         </span>
-                        <span style="padding: 3px 8px; background-color: #fff3e0; border-radius: 4px;">
-                            情感: {scores["情感倾向"]}
+                        <span style="padding: 3px 8px; background-color: #ed6c02; color: white; border-radius: 4px;">
+                            Sentiment: {scores["情感倾向"]}
                         </span>
-                        <span style="padding: 3px 8px; background-color: #fce4ec; border-radius: 4px;">
-                            热度: {scores["热度"]}
+                        <span style="padding: 3px 8px; background-color: #d32f2f; color: white; border-radius: 4px;">
+                            Popularity: {scores["热度"]}
                         </span>
-                        <span style="padding: 3px 8px; background-color: #f3e5f5; border-radius: 4px;">
-                            {scores["互动数"]}
+                        <span style="padding: 3px 8px; background-color: #7b1fa2; color: white; border-radius: 4px;">
+                            Engagement: {scores["互动数"]}
                         </span>
                     </div>
                 </div>
@@ -200,6 +200,44 @@ def create_gradio_interface(recommendation_system: RecommendationSystem) -> gr.I
         
     return interface
 
+
+@staticmethod
+def _get_sentiment_label(sentiment_score: float) -> str:
+    if sentiment_score > 0.3:
+        return "Positive"
+    elif sentiment_score < -0.3:
+        return "Negative"
+    return "Neutral"
+
+def _format_recommendations(self, recommendations: pd.DataFrame) -> Dict:
+    formatted_results = []
+    for _, row in recommendations.iterrows():
+        score_details = {
+            "总分": f"{row['Final_Score']:.2f}",
+            "可信度": "Reliable" if row['Credibility'] > 0 else "Uncertain",
+            "情感倾向": self._get_sentiment_label(row['Sentiment']),
+            "热度": f"{row['Popularity']:.2f}",
+            "互动数": f"Likes {row['Likes']} · Retweets {row['Retweets']}"
+        }
+        
+        formatted_results.append({
+            "text": row['Text'],
+            "scores": score_details
+        })
+        
+    return {
+        "recommendations": formatted_results,
+        "score_explanation": self._get_score_explanation()
+    }
+
+@staticmethod
+def _get_score_explanation() -> Dict[str, str]:
+    return {
+        "Credibility": "Content reliability assessment",
+        "Sentiment": "Text emotional analysis result",
+        "Popularity": "Score based on likes and retweets"
+    }
+
 def main():
     try:
         recommendation_system = RecommendationSystem(