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import json
import re
from transformers import AutoTokenizer, AutoModelForCausalLM
def load_model():
# Using a public model for code evaluation.
model_name = "Salesforce/codegen-350M-mono"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
return tokenizer, model
def evaluate_code(question, code):
# Refined prompt to enforce JSON-only output.
prompt = f"""You are an expert code evaluator.
Evaluate the user's solution to the following problem.
Return ONLY a JSON object with two keys:
- "stars": an integer between 0 and 5 (0 means completely incorrect, 5 means excellent).
- "feedback": a concise message.
Do not include any additional text.
Problem: "{question}"
Solution: "{code}"
"""
# Load model and tokenizer.
tokenizer, model = load_model()
# Generate a response with reduced max tokens and a lower temperature for determinism.
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=100,
temperature=0.2,
pad_token_id=tokenizer.eos_token_id
)
response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Attempt to extract the JSON object from the response.
match = re.search(r'\{.*\}', response_text)
if match:
json_text = match.group(0)
try:
result = json.loads(json_text)
except Exception as e:
result = {"stars": 0, "feedback": "Evaluation failed. Unable to parse AI response."}
else:
result = {"stars": 0, "feedback": "Evaluation failed. Unable to extract JSON from AI response."}
return result
# For direct testing from the command line.
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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