Update app.py

app.py

@@ -1,15 +1,15 @@
-import gradio as gr 
-import ctranslate2 
-from transformers import AutoTokenizer 
-from huggingface_hub import snapshot_download 
-from codeexecutor import postprocess_completion, get_majority_vote  
+import gradio as gr
+import ctranslate2
+from transformers import AutoTokenizer
+from huggingface_hub import snapshot_download
+from codeexecutor import postprocess_completion,get_majority_vote
 
 # Define the model and tokenizer loading
 model_prompt = "Solve the following mathematical problem: "
 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")
-iterations = 10  
+iterations=10
 
 # Function to generate predictions using the model
 def get_prediction(question):
@@ -18,24 +18,24 @@ def get_prediction(question):
     results = generator.generate_batch([input_tokens])
     output_tokens = results[0].sequences[0]
     predicted_answer = tokenizer.convert_tokens_to_string(output_tokens)
-    return predicted_answer  
+    return predicted_answer
 
 # Function to perform majority voting across multiple predictions
 def majority_vote(question, num_iterations=10):
     all_predictions = []
-    all_answer = []
+    all_answer=[]
     for _ in range(num_iterations):
         prediction = get_prediction(question)
-        answer = postprocess_completion(prediction, True, True)
+        answer=postprocess_completion(prediction,True,True)
         all_predictions.append(prediction)
         all_answer.append(answer)
     majority_voted_pred = max(set(all_predictions), key=all_predictions.count)
-    majority_voted_ans = get_majority_vote(all_answer)
-    return majority_voted_pred, all_predictions, majority_voted_ans  
+    majority_voted_ans=get_majority_vote(all_answer)
+    return majority_voted_pred, all_predictions,majority_voted_ans
 
 # Gradio interface for user input and output
 def gradio_interface(question, correct_answer):
-    final_prediction, all_predictions, final_answer = majority_vote(question, iterations)
+    final_prediction, all_predictions,final_answer = majority_vote(question, iterations)
     return {
         "Question": question,
         "Generated Answers (10 iterations)": all_predictions,
@@ -44,61 +44,19 @@ def gradio_interface(question, correct_answer):
         "Majority answer": final_answer
     }
 
-# Custom CSS styling for a better look and feel
-custom_css = """
-    body {
-        background-color: #f7f9fc;
-        font-family: 'Arial', sans-serif;
-    }
-    .gradio-container {
-        border-radius: 10px;
-        border: 1px solid #e0e0e0;
-        box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
-        padding: 20px;
-        background-color: white;
-    }
-    h1, h2, p {
-        text-align: center;
-        color: #333;
-    }
-    input, textarea {
-        border-radius: 5px;
-        border: 1px solid #ccc;
-        padding: 10px;
-    }
-    .gr-button {
-        background-color: #4CAF50;
-        color: white;
-        border-radius: 5px;
-        padding: 10px 20px;
-        font-size: 16px;
-    }
-    .gr-button:hover {
-        background-color: #45a049;
-    }
-    .gr-output {
-        background-color: #f1f1f1;
-        border-radius: 5px;
-        padding: 20px;
-        font-size: 14px;
-    }
-"""
-
 # Gradio app setup
-demo = gr.Interface(
+interface = gr.Interface(
     fn=gradio_interface,
     inputs=[
-        gr.Textbox(label="Math Question", placeholder="Enter your math question here...", elem_id="math_question"),
-        gr.Textbox(label="Correct Answer", placeholder="Enter the correct answer here...", elem_id="correct_answer"),
+        gr.Textbox(label="Math Question"),
+        gr.Textbox(label="Correct Answer"),
     ],
     outputs=[
-        gr.JSON(label="Results", elem_id="results"),  # Display the results in a JSON format
+        gr.JSON(label="Results"),  # Display the results in a JSON format
     ],
     title="Math Question Solver",
     description="Enter a math question to get the model prediction and see all generated answers.",
-    live=True,
-    css=custom_css,  # Apply custom CSS
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()