1

pages/8_Chat.py

@@ -2,7 +2,7 @@ import os, re, sys, time, math, shutil, urllib, string, random, pickle, zipfile,
 import streamlit as st, pandas as pd, numpy as np
 import my_static_methods as my_stm
 from faker import Faker
-st.markdown(my_stm.STYLE_CORRECTION, unsafe_allow_html=True)
+st.html(my_stm.STYLE_CORRECTION)
 st.sidebar.markdown("# Переговоры 💬")
 
 fake = Faker()
@@ -14,5 +14,5 @@ if prompt := st.chat_input("Спрашивайте тут : "):
     answer = prompt[::-1]
     strEnFakeText = fake.paragraph(nb_sentences=4, variable_nb_sentences=False)
     strRuFakeText = fakeRU.paragraph(nb_sentences=4, variable_nb_sentences=False)
-    answer = f"{datetime.datetime.now():%d.%m.%Y %H:%M:%S}\n {strEnFakeText}\n {strRuFakeText}"
-    messages.chat_message("boss").write(f"{answer}") # assistant
+    answer = f"{datetime.datetime.now():%d.%m.%Y %H:%M:%S} \n {strEnFakeText} \n {strRuFakeText}"
+    messages.chat_message("ai").write(f"{answer}") # assistant

static/test.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,9 +16,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:1: SyntaxWarning: invalid escape sequence '\\M'\n",
+      "<>:1: SyntaxWarning: invalid escape sequence '\\M'\n",
+      "C:\\Users\\f64\\AppData\\Local\\Temp\\ipykernel_6328\\1255343956.py:1: SyntaxWarning: invalid escape sequence '\\M'\n",
+      "  path1 = \"N:\\Makarov\\Development\\Python\\Jupiter Notebooks\\Gaziev CSV\\TestData_1504_AB_soloV_gaziev.zip\"\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -218,7 +228,7 @@
        "[12010 rows x 10 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -230,7 +240,7 @@
     "    df = pd.read_csv(path1, sep=';',compression=\"zip\")\n",
     "if not df is None:\n",
     "    df0 = df.copy()\n",
-    "    df0[\"Vis\"] = df0.V.map(lambda v: 0 if v is np.NaN else 1).astype(int)\n",
+    "    df0[\"Vis\"] = df0.V.map(lambda v: 0 if str(v)==\"nan\" else 1).astype(int)\n",
     "    df0[\"Vfloat\"] = df0.V.map(lambda v: 0 if str(v)==\"nan\" else str(v).replace(',', '.')).astype(float)\n",
     "    df0[\"Vsign\"] = df0.Vfloat.map(lambda v: -1 if v<0 else 1 if v>0 else 0).astype(int)\n",
     "    df0[\"Vposneg\"] = df0.Vfloat.map(lambda v: \"n\" if v<0 else \"p\" if v>0 else \"o\").astype(str)\n",
@@ -239,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -257,6 +267,281 @@
     "    fileXYZ = f\"{colnames}_{colcounts}.CSV\"\n",
     "    write(fileXYZ)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Y</th>\n",
+       "      <th>Z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AAA011111</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>702.0</td>\n",
+       "      <td>536.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AAA011111</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>711.0</td>\n",
+       "      <td>556.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AAA011111</td>\n",
+       "      <td>-221.0</td>\n",
+       "      <td>703.0</td>\n",
+       "      <td>505.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AAA011111</td>\n",
+       "      <td>-202.0</td>\n",
+       "      <td>660.0</td>\n",
+       "      <td>382.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AAA011111</td>\n",
+       "      <td>-22.0</td>\n",
+       "      <td>714.0</td>\n",
+       "      <td>277.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>AAA011111</td>\n",
+       "      <td>211.0</td>\n",
+       "      <td>746.0</td>\n",
+       "      <td>312.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>AAA011111</td>\n",
+       "      <td>-200.0</td>\n",
+       "      <td>732.0</td>\n",
+       "      <td>257.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>AAA011112</td>\n",
+       "      <td>201.0</td>\n",
+       "      <td>584.0</td>\n",
+       "      <td>-36.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>AAA011112</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>572.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>AAA011112</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>557.0</td>\n",
+       "      <td>58.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>AAA011112</td>\n",
+       "      <td>-102.0</td>\n",
+       "      <td>616.0</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>AAA011112</td>\n",
+       "      <td>-222.0</td>\n",
+       "      <td>525.0</td>\n",
+       "      <td>-178.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>AAA011112</td>\n",
+       "      <td>-320.0</td>\n",
+       "      <td>452.0</td>\n",
+       "      <td>-505.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>AAA011112</td>\n",
+       "      <td>202.0</td>\n",
+       "      <td>486.0</td>\n",
+       "      <td>-547.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           ID      X      Y      Z\n",
+       "0   AAA011111  111.0  702.0  536.0\n",
+       "1   AAA011111  200.0  711.0  556.0\n",
+       "2   AAA011111 -221.0  703.0  505.0\n",
+       "3   AAA011111 -202.0  660.0  382.0\n",
+       "4   AAA011111  -22.0  714.0  277.0\n",
+       "5   AAA011111  211.0  746.0  312.0\n",
+       "6   AAA011111 -200.0  732.0  257.0\n",
+       "7         NaN    NaN    NaN    NaN\n",
+       "8   AAA011112  201.0  584.0  -36.0\n",
+       "9   AAA011112  200.0  572.0   50.0\n",
+       "10  AAA011112   -2.0  557.0   58.0\n",
+       "11  AAA011112 -102.0  616.0   22.0\n",
+       "12  AAA011112 -222.0  525.0 -178.0\n",
+       "13  AAA011112 -320.0  452.0 -505.0\n",
+       "14  AAA011112  202.0  486.0 -547.0\n",
+       "15        NaN    NaN    NaN    NaN"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path2 = r\"J:\\tmp\\Makarov\\Pack_01.csv\"\n",
+    "df2 = None\n",
+    "if(os.path.exists(path2)):\n",
+    "    df2 = pd.read_csv(path2, sep=';', header=None)\n",
+    "    df2.columns = [\"ID\",\"X\",\"Y\",\"Z\"]\n",
+    "#df2.groupby(\"id\").index\n",
+    "df2.head(16)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "set(df2.groupby(\"ID\").apply(len))={14, 7}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\f64\\AppData\\Local\\Temp\\ipykernel_6328\\1271425466.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  print(f\"{set(df2.groupby(\"ID\").apply(len))=}\")\n",
+      "C:\\Users\\f64\\AppData\\Local\\Temp\\ipykernel_6328\\1271425466.py:2: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  df2.groupby(\"ID\").apply(len)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ID\n",
+       "AAA011111     7\n",
+       "AAA011112     7\n",
+       "AAA011113     7\n",
+       "AAA011114     7\n",
+       "AAA011115     7\n",
+       "AAA011116     7\n",
+       "AAA011117     7\n",
+       "AAA011118     7\n",
+       "BBB011111     7\n",
+       "BBB011112    14\n",
+       "BBB011113     7\n",
+       "BBB011114     7\n",
+       "BBB011115     7\n",
+       "BBB011116     7\n",
+       "CCC011111     7\n",
+       "CCC011112     7\n",
+       "DDD011111     7\n",
+       "DDD011112     7\n",
+       "DDD011113     7\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(f\"{set(df2.groupby(\"ID\").apply(len))=}\")\n",
+    "df2.groupby(\"ID\").apply(len)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\f64\\AppData\\Local\\Temp\\ipykernel_6328\\3316428820.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "  set(df2.groupby(\"ID\").apply(len))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{7, 14}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#df2.groupby(\"ID\").apply(lambda df: df.tail(1))\n",
+    "#df2.groupby(\"ID\").apply(lambda df: type(df))\n",
+    "#set(df2.groupby(\"ID\").apply(lambda df: len(df)).values)\n",
+    "\n",
+    "#df2.groupby(\"ID\").apply(lambda df: list(df.columns))\n",
+    "#df2.groupby(\"ID\").apply(lambda df: list(df.columns))"
+   ]
   }
  ],
  "metadata": {
@@ -275,7 +560,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,