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import json
import re
from transformers import AutoTokenizer, AutoModelForCausalLM
# Global variables for caching the model and tokenizer
tokenizer, model = None, None
def load_model():
global tokenizer, model
if tokenizer is None or model is None:
# Use the DeepSeek instruct model for code evaluation.
model_name = "deepseek-ai/deepseek-coder-1.3b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
return tokenizer, model
def extract_json(response_text):
# Attempt to extract all JSON blocks (non-greedy, with DOTALL)
matches = re.findall(r'\{.*?\}', response_text, re.DOTALL)
for m in reversed(matches):
try:
temp = json.loads(m)
if isinstance(temp, dict) and "stars" in temp and "feedback" in temp:
return temp
except Exception:
continue
return None
def evaluate_code(question, code):
# Revised prompt that explicitly states the expected arithmetic operation for square.
prompt = f"""You are an expert code evaluator.
Evaluate the following solution for the given problem.
The problem asks for a function that returns the square of a number.
A correct solution must multiply the number by itself (using x*x or x**2).
If the solution uses any other operation (such as addition), it is completely incorrect.
Rate the solution as follows:
- 5 stars: Perfect solution; correct, efficient, and follows best practices.
- 4 stars: Correct solution with minor issues.
- 3 stars: Partially correct solution with noticeable issues.
- 2 stars: Incorrect solution with some correct elements.
- 1 star: Mostly incorrect solution.
- 0 stars: Completely incorrect solution.
Respond with exactly one JSON object (with no extra text) that has exactly two keys:
"stars": an integer between 0 and 5,
"feedback": a concise string message explaining your rating.
The JSON must start with '{{' and end with '}}'.
Do not output any additional text.
Question: "{question}"
Solution: "{code}"
Your response:"""
tokenizer, model = load_model()
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=120,
temperature=0.2,
pad_token_id=tokenizer.eos_token_id,
do_sample=True
)
response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Raw model response:", response_text) # Debug output
result = extract_json(response_text)
if result is None:
result = {"stars": 0, "feedback": "Evaluation failed. Unable to extract valid JSON from AI response."}
return result
# For direct command-line testing.
if __name__ == "__main__":
import sys
if len(sys.argv) < 3:
print(json.dumps({"error": "Please provide a question and code as arguments"}))
sys.exit(1)
question = sys.argv[1]
code = sys.argv[2]
result = evaluate_code(question, code)
print(json.dumps(result))
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