Update app.py

app.py

@@ -2,14 +2,14 @@ import gradio as gr
 import ctranslate2
 from transformers import AutoTokenizer
 from huggingface_hub import snapshot_download
-from codeexecutor import postprocess_completion,get_majority_vote
+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):
@@ -23,19 +23,19 @@ def get_prediction(question):
 # 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,
@@ -44,19 +44,64 @@ def gradio_interface(question, correct_answer):
         "Majority answer": final_answer
     }
 
+# Custom CSS for styling
+custom_css = """
+    body {
+        background-color: #f4f7fb;
+        font-family: 'Roboto', sans-serif;
+    }
+    .gradio-container {
+        border-radius: 15px;
+        border: 2px solid #e0e4e7;
+        padding: 20px;
+        box-shadow: 0 10px 15px rgba(0, 0, 0, 0.1);
+        background-color: white;
+        max-width: 700px;
+        margin: 0 auto;
+    }
+    h1, h2, p {
+        text-align: center;
+        color: #333;
+    }
+    input, textarea {
+        border-radius: 8px;
+        border: 1px solid #ccc;
+        padding: 10px;
+        font-size: 16px;
+    }
+    .gr-button {
+        background-color: #4caf50;
+        color: white;
+        border-radius: 8px;
+        padding: 10px 20px;
+        font-size: 16px;
+        transition: background-color 0.3s;
+    }
+    .gr-button:hover {
+        background-color: #45a049;
+    }
+    .gr-output {
+        background-color: #f1f1f1;
+        border-radius: 8px;
+        padding: 15px;
+        font-size: 14px;
+    }
+"""
+
 # Gradio app setup
 interface = gr.Interface(
     fn=gradio_interface,
     inputs=[
-        gr.Textbox(label="Math Question"),
-        gr.Textbox(label="Correct Answer"),
+        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"),
     ],
     outputs=[
-        gr.JSON(label="Results"),  # Display the results in a JSON format
+        gr.JSON(label="Results", elem_id="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.",
+    css=custom_css  # Apply custom CSS
 )
 
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch()