Merge branch 'swebench_di' of github.com:stellaHSR/MetaGPT into swebe…

…nch_di

data/inference/const.py

@@ -45,20 +45,6 @@ def read_sub_set_instance(path=SUBSET_DATASET, tag="scikit-learn"):
         fail_filters = df["instance_id_fail"].tolist()
         pass_filters = [s for s in pass_filters if tag in s]
         fail_filters = [s for s in fail_filters if tag in s]
-        print(pass_filters)
-        print(fail_filters)
-        # Filter for instances containing the tag in either column
-        # pass_filter = df["instance_id_pass"].str.contains(tag, na=False)
-        # fail_filter = df["instance_id_fail"].str.contains(tag, na=False)
-
-        # Combine the filters using | (OR operator) for efficiency
-        # combined_filter = pass_filters | fail_filters
-
-        # print(df[combined_filter])
-        # Apply combined filter and select the specific columns
-        # filtered_df = df[combined_filter][["instance_id_pass", "instance_id_fail"]]
-
-        # Flatten the DataFrame into a list and remove NaN values
         subset_instance = pass_filters + fail_filters
 
         return subset_instance

swe_bench/inference/run_api.py

@@ -56,7 +56,6 @@ async def openai_inference(
             logger.info(f"{repo_prefix}_{version}")
             data.append(f"{repo_prefix}_{version}")
 
-            # import pdb;pdb.set_trace()
             response = await run_instance(instance=datum)
             if response is None:
                 continue
@@ -65,7 +64,6 @@ async def openai_inference(
             output_dict["full_output"] = response
             output_dict["model_patch"] = extract_diff(response)
             print(json.dumps(output_dict), file=f, flush=True)
-    # print(data)
 
 
 async def main(