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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 get_majority_vote
+import re
+
+# Define the model and tokenizer loading
+model_prompt = "Explain and solve the following mathematical problem step by step, showing all work: "
+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],
+        max_length=512,
+        sampling_temperature=0.7,
+        sampling_topk=40,
+    )
+    output_tokens = results[0].sequences[0]
+    predicted_answer = tokenizer.convert_tokens_to_string(output_tokens)
+    return predicted_answer
+
+# Function to parse the prediction to extract the answer and steps
+def parse_prediction(prediction):
+    lines = prediction.strip().split('
+')
+    answer = None
+    steps = []
+    for line in lines:
+        # Check for "Answer:" or "answer:"
+        match = re.match(r'^\s*(?:Answer|answer)\s*[:=]\s*(.*)', line)
+        if match:
+            answer = match.group(1).strip()
+        else:
+            steps.append(line)
+    if answer is None:
+        # If no "Answer:" found, assume last line is the answer
+        answer = lines[-1].strip()
+        steps = lines[:-1]
+    steps_text = '
+'.join(steps).strip()
+    return answer, steps_text
+
+# Function to perform majority voting and get steps
+def majority_vote_with_steps(question, num_iterations=10):
+    all_predictions = []
+    all_answers = []
+    steps_list = []
+
+    for _ in range(num_iterations):
+        prediction = get_prediction(question)
+        answer, steps = parse_prediction(prediction)
+        all_predictions.append(prediction)
+        all_answers.append(answer)
+        steps_list.append(steps)
+
+    # Get the majority voted answer
+    majority_voted_ans = get_majority_vote(all_answers)
+
+    # Find the steps corresponding to the majority voted answer
+    for i, ans in enumerate(all_answers):
+        if ans == majority_voted_ans:
+            steps_solution = steps_list[i]
+            break
+    else:
+        steps_solution = "No steps found"
+
+    return majority_voted_ans, steps_solution
+
+# Gradio interface for user input and output
+def gradio_interface(question, correct_answer):
+    final_answer, steps_solution = majority_vote_with_steps(question, iterations)
+    return {
+        "Question": question,
+        "Majority-Voted Answer": final_answer,
+        "Steps to Solve": steps_solution,
+        "Correct Solution": correct_answer
+    }
+
+# Custom CSS for enhanced design (unchanged)
+
+
+# Gradio app setup
+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"),
+    ],
+    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's majority-voted answer and steps to solve the problem.",
+  
+)
+
+if __name__ == "__main__":
+    interface.launch()
+
+
+