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Update app.py fithly
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app.py
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@@ -28,10 +28,10 @@ model.eval()
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class GenerateRequest(BaseModel):
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prompt: str # mô tả cần sinh code
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language: str | None = "Python"
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max_new_tokens: int = 128
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num_beams: int = 1 # ít beam
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temperature: float = 0.3 # giảm randomness
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@@ -45,11 +45,11 @@ class FixRequest(BaseModel):
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class CompleteRequest(BaseModel):
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prefix: str # code phía trước con trỏ
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suffix: str = "" # code phía sau con trỏ (
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language: str | None = "Python"
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max_new_tokens: int = 64 # completion thường ngắn
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num_beams: int = 1
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temperature: float = 0.3
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class CodeResponse(BaseModel):
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@@ -72,71 +72,27 @@ def run_model(prompt: str,
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num_beams=num_beams,
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temperature=temperature,
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early_stopping=True,
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repetition_penalty=1.05,
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)
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text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return text.strip()
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def clean_code(raw: str, lang: str) -> str:
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"""
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Dọn mấy dòng rác đầu output (vd: ':', 'program:', ...) cho ra code “sạch” hơn.
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Không đụng gì phần giữa & cuối.
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"""
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lines = raw.splitlines()
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if not lines:
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return raw.strip()
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lang_low = (lang or "").lower()
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def looks_like_code(s: str) -> bool:
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s = s.strip()
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if not s:
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return False
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if lang_low == "python":
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# thường bắt đầu bằng import/def/class/# comment
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prefixes = ("def ", "class ", "import ", "from ", "#", "@")
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return s.startswith(prefixes)
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elif lang_low in ("c", "c++", "cpp"):
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prefixes = ("#include", "int ", "void ", "char ", "float ",
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"double ", "struct ", "typedef ")
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return s.startswith(prefixes)
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else:
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# fallback cho ngôn ngữ khác
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return any(ch in s for ch in (";", "{", "}", "=", "function ", "public ", "private "))
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start = 0
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for i, line in enumerate(lines):
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if looks_like_code(line):
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start = i
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break
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cleaned = "\n".join(lines[start:]).strip()
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return cleaned if cleaned else raw.strip()
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# ==== ENDPOINT 1: TẠO CODE TỪ PROMPT ====
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@app.post("/generate-code", response_model=CodeResponse)
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def generate_code(req: GenerateRequest):
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lang = req.language or "Python"
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Generate ONLY valid {lang} source code for the task below.
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Do NOT add any explanations, comments in natural language, or markdown.
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Do NOT repeat the task description.
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Return only raw {lang} code that can be run.
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Task:
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{req.prompt}
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Begin {lang} code now:
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""".strip()
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output = run_model(
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prompt,
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temperature=req.temperature,
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)
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output = clean_code(output, lang)
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return CodeResponse(output=output)
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@@ -155,19 +109,17 @@ Begin {lang} code now:
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@app.post("/fix-code", response_model=CodeResponse)
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def fix_code(req: FixRequest):
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lang = req.language or "Python"
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prompt
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Fix
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Buggy {lang} code:
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{req.code}
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Corrected {lang} code:
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""".strip()
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output = run_model(
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prompt,
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@@ -176,41 +128,24 @@ Corrected {lang} code:
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temperature=req.temperature,
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)
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output = clean_code(output, lang)
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return CodeResponse(output=output)
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# ==== ENDPOINT 3: GỢI Ý CODE KIỂU CURSOR
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@app.post("/complete-code", response_model=CodeResponse)
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def complete_code(req: CompleteRequest):
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lang = req.language or "Python"
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You will be given the prefix and suffix of a {lang} file.
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Your task is to generate ONLY the missing {lang} code between them.
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Rules:
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- DO NOT repeat the prefix.
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- DO NOT repeat the suffix.
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- DO NOT add any explanations, natural language text, or markdown.
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- DO NOT add imports/includes if they already appear in the prefix.
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- Return ONLY raw {lang} code that can be directly inserted at the cursor.
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Prefix:
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{req.prefix}
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<CURSOR HERE>
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Suffix:
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{req.suffix}
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Missing {lang} code:
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""".strip()
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output = run_model(
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prompt,
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temperature=req.temperature,
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)
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return CodeResponse(output=output.strip())
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# ==== HEALTHCHECK ====
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class GenerateRequest(BaseModel):
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prompt: str # mô tả cần sinh code (nên gửi tiếng Anh)
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language: str | None = "Python"
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max_new_tokens: int = 128
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num_beams: int = 1 # ít beam cho ổn định
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temperature: float = 0.3 # giảm randomness
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class CompleteRequest(BaseModel):
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prefix: str # code phía trước con trỏ
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suffix: str = "" # code phía sau con trỏ (chưa dùng nhiều, vì Codet5 không phải infill)
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language: str | None = "Python"
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max_new_tokens: int = 64 # completion thường ngắn
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num_beams: int = 1
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temperature: float = 0.3
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class CodeResponse(BaseModel):
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num_beams=num_beams,
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temperature=temperature,
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early_stopping=True,
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repetition_penalty=1.05, # nhẹ để giảm lặp
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)
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text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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return text.strip()
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# ==== ENDPOINT 1: TẠO CODE TỪ PROMPT ====
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@app.post("/generate-code", response_model=CodeResponse)
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def generate_code(req: GenerateRequest):
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"""
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Sinh code từ mô tả.
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Lưu ý: Codet5+ “thích” prompt ngắn, dạng pattern.
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"""
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lang = req.language or "Python"
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# Prompt cực ngắn, đúng style CodeT5 (tránh essay dài)
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# Ví dụ: "Python code:\n# Task: Create a function that prints numbers from 1 to 10.\n"
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prompt = f"{lang} code:\n# Task: {req.prompt}\n"
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output = run_model(
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prompt,
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temperature=req.temperature,
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)
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return CodeResponse(output=output)
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@app.post("/fix-code", response_model=CodeResponse)
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def fix_code(req: FixRequest):
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"""
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Sửa lỗi code: input là code sai, output là code đúng.
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"""
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lang = req.language or "Python"
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# Cũng giữ prompt thật đơn giản
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prompt = (
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f"Fix the following {lang} code:\n"
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f"{req.code}\n\n"
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f"Fixed {lang} code:\n"
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)
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output = run_model(
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prompt,
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temperature=req.temperature,
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)
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return CodeResponse(output=output)
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# ==== ENDPOINT 3: GỢI Ý CODE (KIỂU CURSOR – DÙ CHỈ DÙNG PREFIX) ====
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@app.post("/complete-code", response_model=CodeResponse)
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def complete_code(req: CompleteRequest):
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"""
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Gợi ý code tiếp theo dựa trên prefix.
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Lưu ý: Codet5p-770m không phải model infill thực sự,
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nên suffix ít tác dụng. Ở đây ta dùng chủ yếu prefix.
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"""
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lang = req.language or "Python"
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# Dùng prefix làm context, để model tiếp tục code.
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# Suffix có thể dùng để hiển thị phía client, còn model chủ yếu nhìn prefix.
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prompt = f"{lang} code:\n{req.prefix}"
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output = run_model(
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prompt,
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temperature=req.temperature,
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)
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return CodeResponse(output=output)
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# ==== HEALTHCHECK ====
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