Upload app.py with huggingface_hub

app.py

@@ -1,59 +1,109 @@
 
-import streamlit as st 
-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 get_majority_vote
+import re
 
 # Define the model and tokenizer loading
-model_prompt = "Solve the following mathematical problem: "
+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  
+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])
+    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  
+    return predicted_answer
 
-# Function to perform majority voting across multiple predictions
-def majority_vote(question, num_iterations=10):
+# 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_answer=[]
+    all_answers = []
+    steps_list = []
+
     for _ in range(num_iterations):
         prediction = get_prediction(question)
-        answer=postprocess_completion(prediction,True,True)
+        answer, steps = parse_prediction(prediction)
         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  
-
-# Streamlit app UI
-st.title("Math Question Solver")  
-st.write("Enter a math question to get the model prediction and see all generated answers.")  
-
-# Input field for math question
-question = st.text_input("Math Question", placeholder="Enter your math question here...")  
-
-# Input field for correct answer
-correct_answer = st.text_input("Correct Answer", placeholder="Enter the correct answer here...")  
-
-# Button to trigger prediction
-if st.button("Get Prediction"):
-    if question and correct_answer:
-        final_prediction, all_predictions,final_answer = majority_vote(question, iterations)
-        st.write("Question: ", question)
-        st.write("Generated Answers (10 iterations): ", all_predictions)
-        st.write("Majority-Voted Prediction: ", final_prediction)
-        st.write("Correct solution: ", correct_answer)
-        st.write("Majority answer: ", final_answer)
+        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:
-        st.error("Please enter both math question and correct answer")
+        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()
+