Update app.py

app majority n steps

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import ctranslate2
 from transformers import AutoTokenizer
 from huggingface_hub import snapshot_download
-from codeexecutor import postprocess_completion, get_majority_vote, get_solution_steps
+from codeexecutor import postprocess_completion, get_majority_vote
 
 # Define the model and tokenizer loading
 model_prompt = "Solve the following mathematical problem: "
@@ -20,38 +20,39 @@ def get_prediction(question):
     predicted_answer = tokenizer.convert_tokens_to_string(output_tokens)
     return predicted_answer
 
-# Function to perform majority voting across multiple predictions
-def majority_vote(question, num_iterations=10):
+# Function to perform majority voting and solve the problem with steps
+def majority_vote_with_steps(question, num_iterations=10):
     all_predictions = []
     all_answer = []
+    steps_to_solve = []
+    
     for _ in range(num_iterations):
         prediction = get_prediction(question)
-        answer = postprocess_completion(prediction, True, True)
+        # Process prediction to get steps and answer
+        answer, success = postprocess_completion(prediction, True, True)
         all_predictions.append(prediction)
         all_answer.append(answer)
-    majority_voted_pred = max(set(all_predictions), key=all_predictions.count)
+        if success:
+            steps_to_solve.append(answer)  # Add the steps if code executes successfully
+    
     majority_voted_ans = get_majority_vote(all_answer)
-    return majority_voted_pred, majority_voted_ans
-
-# Function to get steps for solving the problem
-def get_solution_steps(question):
-    # Assuming 'get_solution_steps' is a function that provides steps for solving the problem
-    steps = get_solution_steps(question)  # You need to implement this function based on how steps are generated
-    return steps
+    
+    # If steps to solve exist, return them, else fallback to "No steps found"
+    steps_solution = steps_to_solve[0] if steps_to_solve else "No steps found"
+    
+    return majority_voted_ans, steps_solution
 
 # Gradio interface for user input and output
 def gradio_interface(question, correct_answer):
-    final_prediction, final_answer = majority_vote(question, iterations)
-    solution_steps = get_solution_steps(question)  # Fetch the steps to solve the problem
+    final_answer, steps_solution = majority_vote_with_steps(question, iterations)
     return {
         "Question": question,
-        "Majority-Voted Prediction": final_prediction,
-        "Correct Solution": correct_answer,
-        "Majority Answer": final_answer,
-        "Solution Steps": solution_steps
+        "Majority-Voted Answer": final_answer,
+        "Steps to Solve": steps_solution,
+        "Correct Solution": correct_answer
     }
 
-# Custom CSS for enhanced design (unchanged)
+# Custom CSS for enhanced design
 custom_css = """
     body {
         background-color: #fafafa;
@@ -93,7 +94,7 @@ custom_css = """
     #math_question, #correct_answer {
         font-size: 20px;
         font-family: 'Poppins', sans-serif;
-        font-weight: 500px;
+        font-weight: 500px;  /* Apply bold */
         color: #007acc;
         margin-bottom: 5px;
         display: inline-block;
@@ -151,7 +152,7 @@ interface = gr.Interface(
         gr.JSON(label="📊 Results"),  # Display the results in a JSON format
     ],
     title="🔢 Math Question Solver",
-    description="Enter a math question to get the majority-voted prediction and the steps to solve it.",
+    description="Enter a math question to get the model's majority-voted answer and steps to solve the problem.",
     css=custom_css  # Apply custom CSS
 )