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app.py

@@ -10,9 +10,6 @@ tokenizer = AutoTokenizer.from_pretrained("AI-MO/NuminaMath-7B-TIR")
 model_path = snapshot_download(repo_id="Makima57/deepseek-math-Numina")
 generator = ctranslate2.Generator(model_path, device="cpu", compute_type="int8")
 
-with open("app.py", "w") as file:
-    file.write(app_code)
-
 # Function to generate predictions using the model
 def get_prediction(question):
     input_text = model_prompt + question
@@ -32,13 +29,13 @@ def majority_vote(question, num_iterations=10):
     return majority_voted_pred, all_predictions
 
 # Gradio interface for user input and output
-def gradio_interface(question, _):
+def gradio_interface(question, correct_answer):
     final_prediction, all_predictions = majority_vote(question, num_iterations=10)
     return {
         "Question": question,
         "Generated Answers (10 iterations)": all_predictions,
         "Majority-Voted Prediction": final_prediction,
-        "Correct Answer": final_prediction  # Display the most voted answer as the correct answer
+        "Correct Answer": correct_answer
     }
 
 # Gradio app setup