Update app.py

app.py

@@ -1,41 +1,83 @@
 from flask import Flask, request, jsonify
 from sentence_transformers import SentenceTransformer, util
+import logging
 
 app = Flask(__name__)
 
-# 预加载模型（线上部署关键）
-model = SentenceTransformer("flax-sentence-embeddings/st-codesearch-distilroberta-base")
-code_snippets = [...]  # 你的代码片段
-code_embeddings = model.encode(code_snippets, convert_to_tensor=True)
+# 配置日志
+logging.basicConfig(level=logging.INFO)
+app.logger = logging.getLogger("CodeSearchAPI")
 
-@app.route('/v1/search', methods=['POST'])
-def api_handler():
-    """生产级API端点"""
+# 预定义代码片段（已验证数据）
+CODE_SNIPPETS = [
+    """def sort_list(x): return sorted(x)""",
+    """def count_above_threshold(elements, threshold=0):
+    return sum(1 for e in elements if e > threshold)""",
+    """def find_min_max(elements):
+    return min(elements), max(elements)"""
+]
+
+# 输入数据验证
+def validate_snippets(snippets):
+    cleaned = []
+    for idx, s in enumerate(snippets):
+        if not isinstance(s, str):
+            app.logger.warning(f"索引 {idx} 类型错误，已转换为字符串")
+            s = str(s)
+        cleaned.append(s.replace("...", "").strip())
+    return [s for s in cleaned if len(s) > 0]
+
+# 初始化模型和编码
+try:
+    model = SentenceTransformer("flax-sentence-embeddings/st-codesearch-distilroberta-base")
+    valid_snippets = validate_snippets(CODE_SNIPPETS)
+    code_emb = model.encode(valid_snippets, convert_to_tensor=True)
+    app.logger.info(f"成功加载模型，编码 {len(valid_snippets)} 个有效代码片段")
+except Exception as e:
+    app.logger.error(f"初始化失败: {str(e)}")
+    raise
+
+@app.route('/search', methods=['POST'])
+def handle_search():
+    """API 处理端点"""
     try:
         # 请求验证
         if not request.is_json:
-            return jsonify({"error": "Invalid Content-Type"}), 415
+            app.logger.warning("无效的 Content-Type")
+            return jsonify({"error": "需要 application/json"}), 415
             
         data = request.get_json()
-        if 'query' not in data:
-            return jsonify({"error": "Missing query parameter"}), 400
-            
-        # 执行语义搜索
-        query = data['query']
-        query_emb = model.encode(query, convert_to_tensor=True)
-        results = util.semantic_search(query_emb, code_embeddings, top_k=1)[0]
+        query = data.get('query', '').strip()
         
-        # 构建响应
-        return jsonify({
-            "data": {
-                "best_match": code_snippets[results[0]['corpus_id']],
-                "similarity": float(results[0]['score'])
+        if not query:
+            app.logger.warning("收到空查询")
+            return jsonify({"error": "查询不能为空"}), 400
+            
+        # 编码查询
+        try:
+            query_emb = model.encode(query, convert_to_tensor=True)
+        except Exception as e:
+            app.logger.error(f"编码失败: {str(e)}")
+            return jsonify({"error": "查询处理失败"}), 500
+            
+        # 语义搜索
+        try:
+            hits = util.semantic_search(query_emb, code_emb, top_k=1)[0]
+            best = hits[0]
+            result = {
+                "code": valid_snippets[best['corpus_id']],
+                "score": round(float(best['score']), 4)
             }
-        })
-        
+            app.logger.info(f"成功处理查询: '{query}'")
+            return jsonify(result)
+            
+        except IndexError:
+            app.logger.error("无匹配结果")
+            return jsonify({"error": "无可用匹配"}), 404
+            
     except Exception as e:
-        app.logger.error(f"API Error: {str(e)}")
-        return jsonify({"error": "Internal Server Error"}), 500
+        app.logger.error(f"未知错误: {str(e)}", exc_info=True)
+        return jsonify({"error": "服务器内部错误"}), 500
 
 if __name__ == "__main__":
     app.run(host='0.0.0.0', port=8080)