feat: 新增目标房型节点

Author: JayLyu

__init__.py

@@ -4,8 +4,9 @@
 
 from .node_mappings import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
 
-WEB_DIRECTORY = "./web"
+# WEB_DIRECTORY = "./web"
 
 print(f"{blue}ComfyUI Baikong Buying: {green}Loaded nodes successfully.{color_end}")
 
-__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
+# __all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

node_mappings.py

@@ -7,8 +7,8 @@
 
 node_module_mappings = {
     'node_housing_decision': 'BK_HousingDecision',
-    'node_table_preview': 'BK_Table_Preview',
-    'node_district_info': 'BK_District_Info'
+    'node_housing_calc': 'BK_HousingCalc',
+    'node_housing_info': 'BK_HousingInfo'
 }
 
 imported_classes = {}
@@ -29,6 +29,6 @@
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "BK_HousingDecision": "BK Housing Decision",
-    "BK_Table_Preview": "BK Table Preview",
-    "BK_District_Info": "BK District Info"
+    "BK_HousingCalc": "BK Housing Calc",
+    "BK_HousingInfo": "BK Housing Info"
 }

nodes/node_housing_calc.py

@@ -0,0 +1,160 @@
+# -*- coding: utf-8 -*-
+
+import torch
+import pandas as pd
+import numpy as np
+from PIL import Image
+import json
+from io import StringIO
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+from matplotlib.font_manager import FontProperties
+import os
+
+# Tensor to PIL
+
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+# Convert PIL to Tensor
+
+
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+
+class BK_HousingCalc:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "csv": ("CSV",),
+            },
+            "optional": {
+                "sort_by": ("STRING", {
+                    "default": "房屋总价,得房率",
+                    "multiline": False,
+                }),
+                "sort_direction": (["正序", "倒序"], {"default": "正序"}),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING", "IMAGE")
+    RETURN_NAMES = ("JSON", "CSV", "IMAGE")
+    FUNCTION = "exec"
+    OUTPUT_NODE = True
+    CATEGORY = "⭐️ Baikong"
+    DESCRIPTION = "使用 Table 预览数据，并输出 csv、json 两种格式的结果"                                                                                         Excel"
+
+    def json_to_table(self, json_data):
+
+        # 将 JSON 数据转换为 DataFrame
+        df = pd.json_normalize(json_data)
+
+        # 填充缺失值为 "-"
+        df.fillna("-", inplace=True)
+
+        return df
+
+    def create_table_image(self, df):
+        # 设置更高的 DPI 值来提高清晰度
+        plt.rcParams['figure.dpi'] = 200
+
+        # 调整高度计算方式，减小系数
+        rows = len(df) + 1  # +1 是为了包含表头
+        fig, ax = plt.subplots(
+            figsize=(15, max(4, rows * 0.25)), dpi=200)
+        ax.axis('tight')
+        ax.axis('off')
+
+        # 获取字体文件路径
+        font_dir = os.path.join(os.path.dirname(
+            os.path.dirname(os.path.realpath(__file__))), "fonts")
+        font_path = os.path.join(font_dir, "AlibabaPuHuiTi-3-45-Light.ttf")
+        font_prop = FontProperties(fname=font_path)
+
+        # 创建表格时应用字体
+        table = ax.table(cellText=df.values,
+                         colLabels=df.columns,
+                         cellLoc='center',
+                         loc='center')
+
+        # 调整表格样式并应用字体
+        table.auto_set_font_size(False)
+        table.set_fontsize(9)
+
+        # 为每个单元格设置字体
+        for cell in table._cells.values():
+            cell.set_text_props(fontproperties=font_prop)
+            cell.set_edgecolor('black')
+            cell.set_linewidth(0.5)
+
+        # 自动调整布局以确保表格完全显示
+        plt.tight_layout()
+
+        # 将图表转换为图片时保持高 DPI
+        canvas = FigureCanvas(fig)
+        canvas.draw()
+
+        # 获取更高分辨率的图像数据
+        s, (width, height) = canvas.print_to_buffer()
+        image = Image.frombytes("RGBA", (width, height), s)
+        image = image.convert('RGB')
+        plt.close(fig)
+
+        return image
+
+    def exec(self, csv, sort_by="", sort_direction="正序", unique_id=None, extra_pnginfo=None):
+        if unique_id is not None and extra_pnginfo is not None:
+            df = pd.read_csv(StringIO(csv))
+
+            # 填充缺失值为 "-"
+            df.fillna("-", inplace=True)
+
+            # 处理排序
+            if sort_by.strip():
+                # 将排序字段拆分为列表
+                sort_columns = [col.strip() for col in sort_by.split(',')]
+                # 验证所有排序列是否存在
+                valid_columns = [
+                    col for col in sort_columns if col in df.columns]
+
+                if valid_columns:
+                    # 根据选择的方向确定升序还是降序
+                    ascending = True if sort_direction == "正序" else False
+                    # 如果是多列排序，创建相同长度的 ascending 列表
+                    if len(valid_columns) > 1:
+                        ascending = [ascending] * len(valid_columns)
+
+                    df = df.sort_values(by=valid_columns, ascending=ascending)
+                    print(f"[BK_Table_Preview] ○ Sorted by {
+                          valid_columns} in {sort_direction} order")
+                else:
+                    print(
+                        f"[BK_Table_Preview] ⚠️ Warning: No valid column names found in {sort_by}")
+
+            # 将表格转换为 JSON 格式
+            json_output = df.to_json(
+                orient='records', force_ascii=False)
+
+            # 将 DataFrame 转换为 CSV 字符串
+            csv_output = df.to_csv(index=False)
+
+            # 创建表格图片
+            table_image = self.create_table_image(df)
+            # 转换为 tensor
+            table_tensor = pil2tensor(table_image)
+
+            # print(f"[BK_Table_Preview] ○ INPUT {df}")
+
+        return (json.dumps(json.loads(json_output),
+                           ensure_ascii=False,
+                           indent=2),
+                csv_output,
+                table_tensor)

nodes/node_district_info.py renamed to nodes/node_housing_info.py

@@ -2,44 +2,50 @@
 
 import pandas as pd
 import json
+from io import StringIO
 
-class BK_District_Info:
+class BK_HousingInfo:
 
     @classmethod
     def INPUT_TYPES(s):
         return {
             "required": {},
             "optional": {
+                "pipeline": ("CSV",), 
                 "小区名称": ("STRING",{"default": "滨运锦绣里",}),
-                "房屋总价": ("INT", {"default": 4000000, "min": 0, "max": 99999999999}),
+                "房屋总价": ("INT", {"default": 4200000, "min": 0, "max": 99999999999}),
                 "室": ("INT", {"default": 3, "min": 0, "max": 20}),
                 "厅": ("INT", {"default": 2, "min": 0, "max": 10}),
                 "卫": ("INT", {"default": 2, "min": 0, "max": 10}),
-                "建筑面积": ("FLOAT", {"default": 100.0, "min": 0, "max": 1000, "step": 0.01}),
-                "得房率": ("INT", {"default": 80, "min": 0, "max": 200, "step": 1}),
-                "当前楼层": ("INT", {"default": 4, "min": 0, "max": 100}),
-                "总楼层": ("INT", {"default": 8, "min": 0, "max": 100}),
+                "建筑面积": ("FLOAT", {"default": 110.0, "min": 0, "max": 1000, "step": 0.01}),
+                "得房率": ("INT", {"default": 91, "min": 0, "max": 200, "step": 1}),
+                "当前楼层": ("INT", {"default": 16, "min": 0, "max": 100}),
+                "总楼层": ("INT", {"default": 17, "min": 0, "max": 100}),
+                "装修标准": ("INT", {"default": 4000, "min": 0, "max": 99999999999}),
                 "房屋权属": (["商品房","公房","经济适用房","其他",],{"default": "商品房"}),
                 "楼房类型": (["塔楼", "板楼", "高层", "小高层", "超高层"],{"default": "塔楼"}),
                 "房屋用途": (["普通住宅","商业类","别墅","四合院","车位","其他",],{"default": "普通住宅"}),
                 "房屋类型": (["新房","二手房"],{"default": "新房"}),
                 "装修": (["精装修","无"],{"default": "精装修"}),
                 "交房时间": ("STRING",{"default": "2024-11",}),
-                "物业费": ("FLOAT", {"default": 3.0, "min": 0, "max": 100, "step": 0.01}),
+                "物业费": ("FLOAT", {"default": 5.5, "min": 0, "max": 100, "step": 0.01}),
+                "容积率": ("FLOAT", {"default": 2.2, "min": 0, "max": 100, "step": 0.01}),
+                "总户数": ("INT", {"default": 1500, "min": 0, "max": 5000}),
             }
         }
+
     RETURN_TYPES = ("CSV", )
     RETURN_NAMES = ("CSV", )
     FUNCTION = "exec"
     OUTPUT_NODE = True
     CATEGORY = "⭐️ Baikong"
-    DESCRIPTION = "预览 Excel"
+    DESCRIPTION = "目标房产的基础信息"
 
-    def exec(self, 房屋总价, 室, 厅, 卫, 建筑面积, 小区名称=None, 得房率=None, 
-             当前楼层=None, 总楼层=None, 房屋用途=None, 房屋权属=None, 楼房类型=None, 
-             房屋类型=None, 装修=None, 交房时间=None, 物业费=None):
+    def exec(self, pipeline=None, 房屋总价=None, 室=None, 厅=None, 卫=None, 建筑面积=None, 
+             小区名称=None, 得房率=None, 当前楼层=None, 总楼层=None, 装修标准=None, 房屋用途=None, 
+             房屋权属=None, 楼房类型=None, 房屋类型=None, 装修=None, 交房时间=None, 物业费=None,
+             容积率=None, 总户数=None):
 
-        # 创建一个字典来存储输入数据
         data = {
             "小区名称": 小区名称 or "-",
             "房屋总价": f"{房屋总价/10000:.2f}万元",
@@ -48,21 +54,28 @@ def exec(self, 房屋总价, 室, 厅, 卫, 建筑面积, 小区名称=None, 得
             "得房率": f"{得房率}%" or "-",
             "套内面积": f"{建筑面积*得房率/100:.2f}m²" or "-",
             "楼层": f"{当前楼层}/{总楼层}" or "-",
+            "装修标准": f"{装修标准}元/m²" or "-",
             "交房时间": 交房时间 or "-",
             "物业费(月)": f"{物业费}元/m²" or "-",
             "物业费(年)": f"{物业费*建筑面积*12:.2f}元" or "-",
             "房屋用途": 房屋用途 or "-",
             "楼房类型": 楼房类型 or "-",
             "房屋类型": 房屋类型 or "-",
-            "装修": 装修 or "-"
+            "装修": 装修 or "-",
+            "容积率": 容积率 or "-",
+            "总户数": 总户数 or "-"
         }
 
-        # 将数据转换为 DataFrame
-        df = pd.DataFrame([data])
+        current_df = pd.DataFrame([data])
+        
+        if pipeline is not None:
+            pipeline_df = pd.read_csv(StringIO(pipeline))
+            combined_df = pd.concat([pipeline_df, current_df], ignore_index=True)
+        else:
+            combined_df = current_df
 
-        result =  df.to_csv(index=False, encoding='utf-8')
+        result = combined_df.to_csv(index=False, encoding='utf-8')
 
-        print(f"[BK_District_Info] ○ INPUT {result}")
+        print(f"[BK_District_Info] ○ INPUT {小区名称}-{室}室{厅}厅{卫}卫")
 
-        # 返回 DataFrame 作为 CSV 格式
-        return (result,)
+        return (result,)