def runcommand(value):
    output = subprocess.run(value, shell=True, capture_output=True)
    return [output.stdout, output.stderr]

import malwi

report = malwi.MalwiReport.create(input_path="suspicious_file.py")

for obj in report.malicious_objects:
    print(f"File: {obj.file_path}")

MalwiReport.create(
    input_path,               # str or Path - file/directory to scan
    accepted_extensions=None, # List[str] - file extensions to scan (e.g., ['py', 'js'])
    silent=False,             # bool - suppress progress messages
    malicious_threshold=0.7,  # float - threshold for malicious classification (0.0-1.0)
    on_finding=None           # callable - callback when malicious objects found
) -> MalwiReport              # Returns: MalwiReport instance with scan results

import malwi

report = malwi.MalwiReport.create("suspicious_directory/")

# Properties
report.malicious              # bool: True if malicious objects detected
report.confidence             # float: Overall confidence score (0.0-1.0)
report.duration               # float: Scan duration in seconds
report.all_objects            # List[MalwiObject]: All analyzed code objects
report.malicious_objects      # List[MalwiObject]: Objects exceeding threshold
report.threshold              # float: Maliciousness threshold used (0.0-1.0)
report.all_files              # List[Path]: All files found in input path
report.skipped_files          # List[Path]: Files skipped (wrong extension)
report.processed_files        # int: Number of files successfully processed
report.activities             # List[str]: Suspicious activities detected
report.input_path             # str: Original input path scanned
report.start_time             # str: ISO 8601 timestamp when scan started
report.all_file_types         # List[str]: All file extensions found
report.version                # str: Malwi version with model hash

# Methods
report.to_demo_text()         # str: Human-readable tree summary
report.to_json()              # str: JSON formatted report
report.to_yaml()              # str: YAML formatted report
report.to_markdown()          # str: Markdown formatted report

# Pre-load models to avoid delay on first prediction
malwi.MalwiReport.load_models_into_memory()

obj = report.all_objects[0]

# Core properties
obj.name                # str: Function/class/module name
obj.file_path           # str: Path to source file
obj.language            # str: Programming language ('python'/'javascript')
obj.maliciousness       # float|None: ML confidence score (0.0-1.0)
obj.warnings            # List[str]: Compilation warnings/errors

# Source code and AST compilation
obj.file_source_code    # str: Complete content of source file
obj.source_code         # str|None: Extracted source for this specific object
obj.byte_code           # List[Instruction]|None: Compiled AST bytecode
obj.location            # Tuple[int,int]|None: Start and end line numbers
obj.embedding_count     # int: Number of DistilBERT tokens (cached)

# Analysis methods
obj.predict()           # dict: Run ML prediction and update maliciousness
obj.to_tokens()         # List[str]: Extract tokens for analysis
obj.to_token_string()   # str: Space-separated token string
obj.to_string()         # str: Bytecode as readable string
obj.to_hash()           # str: SHA256 hash of bytecode
obj.to_dict()           # dict: Serializable representation
obj.to_yaml()           # str: YAML formatted output
obj.to_json()           # str: JSON formatted output

# Class methods
MalwiObject.all_tokens(language="python")  # List[str]: All possible tokens