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app.py

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+
+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
+
+# Function to generate predictions using the model
+def get_prediction(question):
+    input_text = model_prompt + question
+    input_tokens = tokenizer.tokenize(input_text)
+    results = generator.generate_batch([input_tokens])
+    output_tokens = results[0].sequences[0]
+    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):
+    all_predictions = []
+    all_answer=[]
+    for _ in range(num_iterations):
+        prediction = get_prediction(question)
+        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
+
+# Gradio interface for user input and output
+def gradio_interface(question, correct_answer):
+    final_prediction, all_predictions,final_answer = majority_vote(question, iterations)
+    return {
+        "Question": question,
+        "Generated Answers (10 iterations)": all_predictions,
+        "Majority-Voted Prediction": final_prediction,
+        "Correct solution": correct_answer,
+        "Majority answer": final_answer
+    }
+
+# Gradio app setup
+interface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Textbox(label="Math Question"),
+        gr.Textbox(label="Correct Answer"),
+    ],
+    outputs=[
+        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.",
+)
+
+if __name__ == "__main__":
+
+