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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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import textwrap |
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from viz_generator import code_to_mermaid |
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DEFAULT_MODEL = "Salesforce/codet5-small" |
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class CodeExplainViz: |
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def __init__(self, model_name_or_path=DEFAULT_MODEL): |
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self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) |
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self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path) |
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def explain(self, code: str, max_length: int = 256) -> dict: |
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prompt = "explain: " + code |
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inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512) |
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outputs = self.model.generate(**inputs, max_length=max_length, num_beams=4, early_stopping=True) |
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text = self.tokenizer.decode(outputs[0], skip_special_tokens=True) |
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lines = [l.strip() for l in text.splitlines() if l.strip()] |
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short = lines[0] if lines else textwrap.shorten(text, width=120) |
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detailed = "\n".join(lines[1:]) if len(lines) > 1 else text |
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mermaid = code_to_mermaid(code) |
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unit_tests = self._make_unit_test_template(code) |
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return {"short": short, "detailed": detailed, "mermaid": mermaid, "unit_tests": unit_tests} |
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def _make_unit_test_template(self, code: str) -> str: |
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import re |
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m = re.search(r"def\s+([A-Za-z0-9_]+)\s*\((.*?)\):", code) |
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fn = m.group(1) if m else "function_under_test" |
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params = m.group(2) if m else "" |
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param_count = len([p for p in params.split(',') if p.strip()]) if params.strip() else 0 |
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args = ", ".join(["0"] * param_count) |
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template = f"""import pytest |
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from your_module import {fn} |
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def test_{fn}_basic(): |
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# TODO: replace with real inputs and expected outputs |
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assert {fn}({args}) == ... |
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def test_{fn}_edge_cases(): |
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# Example edge-case tests |
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with pytest.raises(Exception): |
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{fn}(...)""" |
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return template |
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