diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..2169bb7 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,25 @@ +FROM python:3.11-slim + +WORKDIR /app + +ENV LC_ALL=C.UTF-8 +ENV LANG=C.UTF-8 +ENV PYTHONUNBUFFERED=1 + +RUN sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources && \ + apt-get update && apt-get install -y --no-install-recommends \ + gcc \ + g++ \ + libgomp1 \ + && rm -rf /var/lib/apt/lists/* + + +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ + +COPY predict_with_excel.py . +COPY degree3/ ./degree3/ + +EXPOSE 8000 + +CMD ["uvicorn", "predict_with_excel:app", "--host", "0.0.0.0", "--port", "8000"] \ No newline at end of file diff --git a/degree3/CADY_仰焊.xlsx_model_degree3.pkl b/degree3/CADY_仰焊.xlsx_model_degree3.pkl new file mode 100644 index 0000000..0c96e47 Binary files /dev/null and b/degree3/CADY_仰焊.xlsx_model_degree3.pkl differ diff --git 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b/predict_with_excel.py new file mode 100644 index 0000000..7c73cc8 --- /dev/null +++ b/predict_with_excel.py @@ -0,0 +1,539 @@ +import io +import warnings +from pathlib import Path +from typing import IO, Optional, Union +from urllib.parse import quote + +import numpy as np +import pandas as pd +from fastapi import FastAPI, File, HTTPException, UploadFile +from fastapi.responses import JSONResponse, StreamingResponse +from sklearn.exceptions import DataConversionWarning + +MODEL_RANGES = { + "CAK_仰焊": "12-Infinity", + "CV17.5_平焊": "3-Infinity", + "EV15_立焊": "13-Infinity", + "SX_平焊": "14-Infinity", + "CA_平焊": "0-60", + "CA_横焊": "0-75", + "CAK_平焊": "0-85", + "CAK_立焊": "0-85", + "CAV_平焊": "0-68", + "CAY_平焊": "0-70", + "CV20_平焊": "4-80", + "EV17.5_立焊": "0-43", + "SY_平焊": "0-53", +} + +warnings.filterwarnings("ignore", message="X does not have valid feature names, but.*") +warnings.filterwarnings("ignore", category=DeprecationWarning, + message="Conversion of an array with ndim > 0 to a scalar is deprecated.*") +warnings.filterwarnings("ignore", category=DataConversionWarning) + + +def extract_headers_from_first_three_rows( + excel_source: Union[str, Path, IO[bytes]], sheet_name: Union[int, str] = 0 +): + """ + 从 Excel 前两行中提取每列的有效标题，支持合并单元格场景： + - 每列仅取一个有效标题（优先第1行，若第1行为空则用第0行） + - 自动跳过空值、'nan'、纯空格 + - 支持合并单元格导致的列数据缺失 + """ + # Step 1: 读取前两行（raw，无列名） + if hasattr(excel_source, "seek"): + excel_source.seek(0) + + df_raw = pd.read_excel( + excel_source, sheet_name=sheet_name, header=None, nrows=3 # 只读前三行 + ) + + if hasattr(excel_source, "seek"): + excel_source.seek(0) + + max_cols = df_raw.shape[1] + headers = [] + + for col_idx in range(max_cols): + val0 = df_raw.iloc[0, col_idx] if len(df_raw) > 0 else None + val1 = df_raw.iloc[1, col_idx] if len(df_raw) > 1 else None + val2 = df_raw.iloc[2, col_idx] if len(df_raw) > 2 else None + + # 定义“有效值”判断函数 + def is_valid(v): + if pd.isna(v): + return False + s = str(v).strip() + return s != "" and s.lower() not in ("nan", "none", "null") + + # 策略：优先使用第1行（第二行），因为合并单元格常导致第0行为空 + # 如果第1行有效 → 用第1行 + # 如果第1行无效但第0行有效 → 用第0行 + # 都无效 → 用默认名 + if is_valid(val1): + header = str(val1).strip() + elif is_valid(val0): + header = str(val0).strip() + elif is_valid(val2): + header = str(val2).strip() + else: + header = f"Unnamed_{col_idx}" + + headers.append(header) + + print("✅ 提取到的表头:", headers) + return headers + + +def read_excel_with_irregular_headers( + excel_source: Union[str, Path, IO[bytes]], sheet_name: Union[int, str] = 0 +): + """ + 完整读取：提取表头 + 读取剩余数据 + """ + headers = extract_headers_from_first_three_rows(excel_source, sheet_name) + + if hasattr(excel_source, "seek"): + excel_source.seek(0) + + # 读取从第4行开始的数据（跳过前3行） + df = pd.read_excel( + excel_source, + sheet_name=sheet_name, + header=None, + skiprows=3, # 跳过前三行（标题行） + names=headers, # 使用提取的标题 + ) + + # 可选：删除全空列 + df = df.dropna(how="all", axis=1) + print(f"✅ 读取数据形状: {df.shape}") + return df + + +def split_by_plus(s: str) -> list[str]: + if not isinstance(s, str): + raise TypeError("输入必须是字符串") + + # 按 '+' 分割，去除每部分首尾空格，过滤空字符串 + return [part.strip() for part in s.split("+") if part.strip()] + + +# %% +import joblib + +folder_path = Path("./degree3") + + +def load_all_models(): + """ + 加载所有模型到 model_list + """ + global model_list + # 清空列表以确保重新加载（尽管有启动时加载一次的逻辑） + # model_list = [] + # ^^^ 注释掉这行，因为 FastAPI 的 on_event("startup") 应该只执行一次。 + # 如果在开发中需要热重载模型，可以取消注释或提供专门的重载端点。 + + if model_list: # 如果已经加载过，则不再重复加载 (主要由 startup_event 控制) + print(f"[MODEL_LOADER] Models already loaded. Count: {len(model_list)}") + return + + print( + f"[MODEL_LOADER] Attempting to load models. Initial model_list length: {len(model_list)}" + ) + print( + f"[MODEL_LOADER] Target model folder (relative to WORKDIR /app): {folder_path}" + ) + resolved_folder_path = folder_path.resolve() + print(f"[MODEL_LOADER] Target model folder (absolute): {resolved_folder_path}") + + if not resolved_folder_path.exists(): + print( + f"[MODEL_LOADER] ERROR: Model directory not found at {resolved_folder_path}" + ) + return + + if not resolved_folder_path.is_dir(): + print(f"[MODEL_LOADER] ERROR: Path {resolved_folder_path} is not a directory.") + return + + print( + f"[MODEL_LOADER] Model directory found at {resolved_folder_path}. Searching for files with glob pattern: *3.pkl" + ) + + files_found = list(resolved_folder_path.glob("*3.pkl")) + print( + f"[MODEL_LOADER] Files found by glob ('{resolved_folder_path}/*3.pkl'): {[str(f.name) for f in files_found]}" + ) + + if not files_found: + print( + f"[MODEL_LOADER] WARNING: No files matched the glob pattern '*3.pkl' in {resolved_folder_path}. Listing all items in directory as a fallback check:" + ) + try: + all_items = list(resolved_folder_path.iterdir()) + print( + f"[MODEL_LOADER] All items in {resolved_folder_path}: {[str(item.name) for item in all_items]}" + ) + except Exception as e: + print( + f"[MODEL_LOADER] ERROR: Could not list items in {resolved_folder_path}: {e}" + ) + return + + temp_model_list = [] # 使用临时列表，成功加载完再赋值给全局 + for file_path_obj in files_found: + if file_path_obj.is_file(): + try: + model_name_parts = file_path_obj.name.split(".xlsx") + if len(model_name_parts) > 1: + model_name = model_name_parts[0] + else: + model_name_base = file_path_obj.stem + if model_name_base.endswith("_model_degree3"): + model_name = model_name_base[: -len("_model_degree3")] + elif model_name_base.endswith( + "3" + ): # 兼容 *3.pkl 但不含 .xlsx 的情况 + model_name = model_name_base[: -len("3")].rstrip( + "." + ) # 移除可能的尾部'.' (来自.pkl) + if model_name.endswith("_model_degree"): # 进一步处理 + model_name = model_name[: -len("_model_degree")] + else: + model_name = model_name_base + print( + f"[MODEL_LOADER] WARNING: Filename {file_path_obj.name} does not contain '.xlsx' as expected for name splitting. Using '{model_name}' as model name based on stem." + ) + + loaded_model = joblib.load(file_path_obj) + temp_model_list.append( + { + "name": model_name, + "model": loaded_model, + "filename": file_path_obj.name, + } + ) + print( + f"[MODEL_LOADER] Successfully loaded model from {file_path_obj.name} as: {model_name}" + ) + except Exception as e: + print( + f"[MODEL_LOADER] ERROR: Failed to load model {file_path_obj.name}: {e}" + ) + + model_list = temp_model_list # 赋值给全局变量 + print( + f"[MODEL_LOADER] Finished loading models. Total models loaded: {len(model_list)}" + ) + if not model_list: + print( + f"[MODEL_LOADER] WARNING: model_list is empty after attempting to load models." + ) + + +def get_welding_coefficient(row): + coefficient = 0.0 + global model_list + model_map = {model["name"]: model for model in model_list} + lenth = row.长度_m + welding_type = row.坡口代码 + welding_position = row.焊接位置 + thickness = row.板厚_mm + thickness_value = float(thickness) + thickness_array = np.array([[thickness_value]]) + + # 处理焊接位置，统一命名 + try: + if "横" in welding_position: + welding_position = "横焊" + elif "立" in welding_position: + welding_position = "立焊" + elif "仰" in welding_position: + welding_position = "仰焊" + elif "平" in welding_position: + welding_position = "平焊" + except Exception as e: + print(f"行⚠️ 处理焊接位置时出错: {welding_position}, 错误: {e}") + welding_position = welding_position # 保持原值 + + # 处理坡口代码，支持多个坡口代码相加的情况 + try: + welding_type = split_by_plus(welding_type) + except Exception as e: + print(f"⚠️ 处理坡口代码时出错: {welding_type}, 错误: {e}") + welding_type = [str(welding_type)] # 确保后续遍历 + + if isinstance(welding_type, str): + welding_type = [welding_type] + + for item in welding_type: + try: + if "min/m" in item: + # 如果已经是系数形式，直接累加 + coefficient += float(item.replace("min/m", "").strip()) + else: + model_name = f"{item}_{welding_position}" + selected_model_info = model_map.get(model_name) + if selected_model_info is None: + raise KeyError(f"模型 '{model_name}' 未加载") + model = selected_model_info["model"] + range_str = MODEL_RANGES.get(model_name, "0-Infinity") + min_val_str, max_val_str = range_str.split("-") + min_val = float(min_val_str) + max_val = None if max_val_str == "Infinity" else float(max_val_str) + if thickness_value < min_val or ( + max_val is not None and thickness_value > max_val + ): + print( + f"⚠️ 警告: 厚度 {thickness_value}mm 超出模型 '{model_name}' 的适用范围 ({range_str} mm)。" + ) + coefficient = float('nan') + break + else: + prediction = float(model.predict(thickness_array)[0]) + coefficient += prediction + except Exception as e: + coefficient = float('nan') + print( + f"⚠️ 处理坡口代码模型不存在: {item}_{welding_position}, 错误: {e}" + ) + # print(coefficient) + # print(type(coefficient)) + coefficient = float(coefficient) + print( + f"长度: {lenth}, 焊接类型: {welding_type},焊接位置: {welding_position}, 板厚: {thickness}" + ) + + return coefficient + + +def get_welding_time(row): + coefficient = row.焊接系数 + length = row.长度_m + welding_time = coefficient * length + return welding_time + + +def save_df_to_excel(df, output_file_path): + df.to_excel(output_file_path, index=False) + + +def build_prediction_dataframe(df: pd.DataFrame) -> pd.DataFrame: + """清洗原始数据并计算焊接系数、工时。""" + df = df.copy() + df["序号"] = df.reset_index(drop=True).index + 1 + df_clean = df.copy() + df_clean.columns = [ + col.replace("（", "_") + .replace("）", "_") + .replace("(", "_") + .replace(")", "_") + .strip("_") + .replace("__", "_") + for col in df.columns + ] + + df_without_last = df_clean.iloc[:-1].copy() # 去掉最后一行合计 + df_without_last["焊接系数"] = df_without_last.apply( + lambda row: get_welding_coefficient(row), axis=1 + ) + df_without_last["焊接工时"] = df_without_last.apply( + lambda row: get_welding_time(row), axis=1 + ) + + total_row = {col: np.nan for col in df_without_last.columns} + total_row["序号"] = "总计" + total_row["焊接工时"] = df_without_last["焊接工时"].sum() + + result_df = pd.concat([df_without_last, pd.DataFrame([total_row])], ignore_index=True) + return result_df + + +def _prepare_excel_source( + excel_source: Union[str, Path, IO[bytes], bytes, bytearray] +) -> tuple[Optional[Path], Optional[bytes]]: + if isinstance(excel_source, Path): + return excel_source, None + if isinstance(excel_source, str): + return Path(excel_source), None + if isinstance(excel_source, (bytes, bytearray)): + return None, bytes(excel_source) + if hasattr(excel_source, "read"): + try: + if hasattr(excel_source, "seek"): + excel_source.seek(0) + data = excel_source.read() + excel_source.seek(0) + else: + data = excel_source.read() + except Exception as exc: # pragma: no cover - 防御性 + raise ValueError("无法读取 Excel 源数据") from exc + return None, data + raise TypeError("excel_source 必须是路径、字节流或支持 read() 的对象") + + +def _make_excel_io(path_like: Optional[Path], excel_bytes: Optional[bytes]): + if path_like is not None: + return path_like + if excel_bytes is None: + raise ValueError("缺少 Excel 数据") + return io.BytesIO(excel_bytes) + + +def process_excel( + excel_source: Union[str, Path, IO[bytes], bytes, bytearray], + sheet_name: Optional[Union[int, str]] = 0, +) -> Union[pd.DataFrame, dict[str, pd.DataFrame]]: + """从 Excel 读取数据并完成完整的预测流程。 + + 当 sheet_name 为 None 时，处理工作簿内所有工作表并返回 {sheet_name: DataFrame}。 + """ + + path_like, excel_bytes = _prepare_excel_source(excel_source) + + def get_source(): + return _make_excel_io(path_like, excel_bytes) + + if sheet_name is None: + with pd.ExcelFile(get_source()) as excel_file: + sheet_names = excel_file.sheet_names + + results: dict[str, pd.DataFrame] = {} + for name in sheet_names: + df = read_excel_with_irregular_headers(get_source(), sheet_name=name) + results[name] = build_prediction_dataframe(df) + return results + + df = read_excel_with_irregular_headers(get_source(), sheet_name=sheet_name) + return build_prediction_dataframe(df) + + +model_list = [] +# source_excel_path = Path("/root/data/hanjie/1113/rawdata/场景/部件.xlsx") +# source_excel_path = Path("/root/data/hanjie/1113/rawdata/场景/搭载.xlsx") +# source_excel_path = Path("/root/data/hanjie/1113/rawdata/场景/分段.xlsx") +# source_excel_path = Path("/root/data/hanjie/1113/rawdata/场景/零件.xlsx") +# source_excel_path = Path("./all.xlsx") + +app = FastAPI(title="Welding Time Prediction API") + + +@app.on_event("startup") +def startup_event(): + load_all_models() + + +def _sanitize_sheet_name(name: Union[str, int], existing: set[str]) -> str: + base = str(name) if str(name).strip() else "Sheet" + base = base[:31] + candidate = base or "Sheet" + counter = 1 + while candidate in existing: + suffix = f"_{counter}" + candidate = (base[: 31 - len(suffix)] if base else "Sheet") + suffix + counter += 1 + return candidate + + +def _df_to_records(df: pd.DataFrame) -> list[dict[str, object]]: + cleaned = df.replace({np.inf: None, -np.inf: None}) + cleaned = cleaned.where(pd.notnull(cleaned), None) + records: list[dict[str, object]] = [] + for raw in cleaned.to_dict(orient="records"): + normalized: dict[str, object] = {} + for key, value in raw.items(): + if isinstance(value, np.generic): + value = value.item() + if isinstance(value, float) and (np.isnan(value) or np.isinf(value)): + value = None + normalized[key] = value + records.append(normalized) + return records + + +@app.post("/predict", summary="上传 Excel 并返回预测结果表") +async def predict( + file: UploadFile = File(...), + sheet_name: Optional[str] = None, + response_format: str = "json", +): + if not file.filename.lower().endswith((".xls", ".xlsx")): + raise HTTPException(status_code=400, detail="仅支持 Excel 文件 (.xls/.xlsx)") + + contents = await file.read() + if not contents: + raise HTTPException(status_code=400, detail="上传文件为空") + + try: + resolved_sheet: Optional[Union[int, str]] = None + if sheet_name is not None and sheet_name.lower() != "all": + if sheet_name.isdigit(): + resolved_sheet = int(sheet_name) + else: + resolved_sheet = sheet_name + result_obj = process_excel(contents, sheet_name=resolved_sheet) + except Exception as exc: + raise HTTPException(status_code=500, detail=f"处理 Excel 时发生错误: {exc}") from exc + + response_format = response_format.lower() + if response_format not in {"json", "excel"}: + raise HTTPException(status_code=400, detail="response_format 只支持 'json' 或 'excel'") + + if response_format == "json": + if isinstance(result_obj, dict): + payload = {str(name): _df_to_records(df) for name, df in result_obj.items()} + else: + key = str(resolved_sheet if resolved_sheet is not None else "Sheet1") + payload = {key: _df_to_records(result_obj)} + return JSONResponse(content=payload) + + output_stream = io.BytesIO() + if isinstance(result_obj, dict): + existing_names: set[str] = set() + with pd.ExcelWriter(output_stream, engine="openpyxl") as writer: + for original_name, df in result_obj.items(): + sheet_safe = _sanitize_sheet_name(original_name, existing_names) + existing_names.add(sheet_safe) + df.to_excel(writer, sheet_name=sheet_safe, index=False) + else: + result_obj.to_excel(output_stream, index=False) + + output_stream.seek(0) + + download_name = f"{Path(file.filename).stem}_预测结果.xlsx" + ascii_fallback = "prediction.xlsx" + quoted_name = quote(download_name) + content_disposition = ( + f"attachment; filename={ascii_fallback}; filename*=UTF-8''{quoted_name}" + ) + + headers = {"Content-Disposition": content_disposition} + + return StreamingResponse( + output_stream, + media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + headers=headers, + ) + + +def main(): + load_all_models() + # result_obj = process_excel(source_excel_path, sheet_name=0) + # output_file_path = source_excel_path.parent.joinpath( + # source_excel_path.stem + "_预测结果.xlsx" + # ) + # + # if isinstance(result_obj, dict): + # with pd.ExcelWriter(output_file_path, engine="openpyxl") as writer: + # for sheet_name, df in result_obj.items(): + # df.to_excel(writer, sheet_name=str(sheet_name)[:31] or "Sheet", index=False) + # else: + # print(len(result_obj)) + # save_df_to_excel(result_obj, output_file_path) + + +if __name__ == "__main__": + main() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..51e448b --- /dev/null +++ b/requirements.txt @@ -0,0 +1,8 @@ +numpy +scikit-learn +joblib +fastapi +uvicorn +pandas +openpyxl +python-multipart