Update inference.py

inference.py

@@ -211,10 +211,11 @@ def get_result_1(model, tokenizer):
     
 
 answers = {}
-for model_path in [
-  "/mnt/disk2/home/wujianfeng/LLaMA-Factory/all_new_1/checkpoint-600",
-  "/mnt/disk2/home/wujianfeng/LLaMA-Factory/all_new/checkpoint-600/",
 
+for model_path in [
+    "/mnt/disk2/home/wujianfeng/LLaMA-Factory/all/TACO/",
+    "/mnt/disk2/home/wujianfeng/LLaMA-Factory/all_new_2/CodeNet4Repair/",
+    "/mnt/disk2/home/wujianfeng/LLaMA-Factory/all_new_1/CodeExercise-Python-27k/",  
 ]:
     print("model_path: ", model_path)
     model = AutoModelForCausalLM.from_pretrained(
@@ -234,14 +235,21 @@ for model_path in [
     test, score = exec_code(test)
     answers[score] = test
 
-
+'''
+import os
+for path in os.listdir("./"):
+    if "home-wujianfeng" in path: 
+        with open(path, "r") as f:
+            test = json.load(f)
+        answers[float(path.split(".")[-2].split("-")[-1])] = test
+''' 
     
 answers = list(dict(sorted(answers.items())).values())
 print("answers: ", answers)
 right = 0
 jiuge_right = 0
 merge = []
-for i in range(len(answers)):
+for i in range(len(answers[0])):
 #for i in range(2):
     flag = 0
     for answer in answers:
@@ -257,7 +265,7 @@ for i in range(len(answers)):
          
 
 
-print(right / len(answers), jiuge_right / len(answers))
+print(right / len(answers[0]), jiuge_right / len(answers[0]))
 with open("wjf_jiuge.jsonl", "w") as f:
     for item in merge:
         item.pop("result")