app/models/qwen.py

@@ -37,7 +37,7 @@ class Qwen(BaseModel):
         self.max_tokens = max_tokens
         self.messages = []
 
-    def ask(self, user_message: str, clear_history: bool = True, debug: bool = False) -> Optional[str]:
+    def ask(self, user_message: str, clear_history: bool = True) -> Optional[str]:
         if clear_history:
             self.messages = []
             if self.system_prompt:
@@ -47,9 +47,6 @@ class Qwen(BaseModel):
 
         prompt_text = self._build_prompt()
 
-        if debug:
-            print(prompt_text)
-
         inputs = self.tokenizer(prompt_text, return_tensors="pt").to(self.device)
 
         with torch.no_grad():

app/utils/preprocess.py

@@ -1,27 +1,9 @@
 import pandas as pd
 
-def preprocess_test(test_solutions_path: str, test_tasks_path: str, test_tests_path: str, save_path: str) -> None:
+def preprocess_test(test_solutions_path: str, test_tasks_path: str, save_path: str) -> None:
     solutions_df = pd.read_excel(test_solutions_path)
     tasks_df = pd.read_excel(test_tasks_path)
-    tests_df = pd.read_excel(test_tests_path)
 
-    preprocessed_df = solutions_df.merge(tasks_df[['id', 'description', 'author_solution']], 
-                                         left_on='task_id', right_on='id', how='left')
+    preprocessed_df = solutions_df.merge(tasks_df[['id', 'description']], left_on='task_id', right_on='id', how='left')
 
-    preprocessed_df = preprocessed_df.merge(tests_df[['task_id', 'input', 'output']], 
-                                            left_on='task_id', right_on='task_id', how='left')
-
-    preprocessed_df['input_output'] = preprocessed_df.apply(
-        lambda row: f"{row['input']}-{row['output']}" if pd.notna(row['input']) or pd.notna(row['output']) else "", 
-        axis=1
-    )
-
-    grouped_df = preprocessed_df.groupby('id_x').agg({
-        'student_solution': 'first',
-        'description': 'first',
-        'author_solution': 'first',
-        'input_output': lambda x: '\n'.join(filter(None, x))
-    }).reset_index()
-
-    grouped_df = grouped_df.rename(columns={'id_x': 'id'})
-    grouped_df.to_excel(save_path, index=False)
+    preprocessed_df[['description', 'student_solution']].to_excel(save_path, index=False)

app/utils/submit.py

@@ -40,9 +40,9 @@ def generate_submit(tests_path: str, predict_func: Callable, save_path: str, use
         idx = tests.index[i]
         solution_row = tests.iloc[i]
 
-        input_text = f"Вводные данные:\n{solution_row['description']}\n\nКод студента:\n{solution_row['student_solution']}\n\nКод автора:\n{solution_row['author_solution']}\n\nТестовые условия:\n{solution_row['input_output']}"
+        input_text = f"{solution_row['description']}\n\n{solution_row['student_solution']}"
         text = predict_func(input_text)  
 
         embedding = embedding2string(get_sentence_embedding(text))
-        submit_df.loc[i] = [solution_row['id'], text, embedding]
+        submit_df.loc[i] = [i, text, embedding]
     submit_df.to_csv(save_path, index=False)

main.py

@@ -18,7 +18,7 @@ if __name__ == "__main__":
         system_prompt=system_prompt,
     )
     
-    preprocess_test("data/raw/test/solutions.xlsx", "data/raw/test/tasks.xlsx", "data/raw/test/tests.xlsx", "data/processed/test.xlsx")
+    preprocess_test("data/raw/test/solutions.xlsx", "data/raw/test/tasks.xlsx", "data/processed/test.xlsx")
     
     # Predict, ёмаё)
     def predict(input_text: str) -> str: