llm_module.py

from transformers import pipeline

# Load a lightweight T5 model for text generation
summarizer = pipeline("text2text-generation", model="t5-base")

def suggest_fixes(code: str, issues: str) -> str:
    """
    Use an LLM to suggest fixes for a Solidity contract based on identified issues.

    Args:
        code: Original Solidity code.
        issues: Vulnerability report from the analysis.

    Returns:
        Modified Solidity code with suggested fixes.
    """
    prompt = f"""
Here is a Solidity contract:
{code}

Issues found:
{issues}

Fix the issues and output the corrected code.
"""
    response = summarizer(prompt, max_length=512)[0]["generated_text"]
    return response

def generate_spec(fixed_code: str, language: str) -> str:
    """
    Use an LLM to generate a formal specification of a fixed Solidity contract.

    Args:
        fixed_code: Solidity code after fixes.
        language: Formal specification language chosen by the user.

    Returns:
        Generated specification in the selected language.
    """
    prompt = f"""
You are a formal methods expert. Generate a formal specification in {language} for the following Solidity contract:

{fixed_code}

Include:
- Preconditions
- Postconditions
- Invariants
- Assumptions
- Optional pseudocode or specific notation for {language}
"""
    response = summarizer(prompt, max_length=512)[0]["generated_text"]
    return response