add app.py and requirements.txt

app.py

@@ -42,7 +42,7 @@ def update_knowledge_graph(entities, relations):
 def visualize_kg_text():
     nodes = [f"{ent[0]} ({ent[1]})" for ent in knowledge_graph["entities"]]
     edges = [f"{h} --[{r}]-> {t}" for h, t, r in knowledge_graph["relations"]]
-    return "📌 实体:\n" + "\n".join(nodes) + "\n\n📎 关系:\n" + "\n".join(edges)
+    return "\n".join(["📌 实体:"] + nodes + ["", "📎 关系:"] + edges)
 
 # ======================== 实体识别（NER） ========================
 def ner(text, model_type="bert"):
@@ -113,7 +113,8 @@ def process_file(file, model_type="bert"):
 
 # ======================== 模型评估与自动标注 ========================
 def convert_telegram_json_to_eval_format(path):
-    data = json.load(open(path, encoding="utf-8"))
+    with open(path, encoding="utf-8") as f:
+        data = json.load(f)
     result = []
     for m in data.get("messages", []):
         if isinstance(m.get("text"), str):
@@ -132,7 +133,7 @@ def evaluate_ner_model(data, model_type):
         for ent in gold.union(pred):
             y_true.append(1 if ent in gold else 0)
             y_pred.append(1 if ent in pred else 0)
-    return f"Precision: {precision_score(y_true,y_pred):.2f}\nRecall: {recall_score(y_true,y_pred):.2f}\nF1: {f1_score(y_true,y_pred):.2f}"
+    return f"Precision: {precision_score(y_true, y_pred):.2f}\nRecall: {recall_score(y_true, y_pred):.2f}\nF1: {f1_score(y_true, y_pred):.2f}"
 
 def auto_annotate(file, model_type):
     data = convert_telegram_json_to_eval_format(file.name)