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.streamlit/secrets.toml

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+EMAIL = "sauravkhengar07@gmail.com"
+APP_PASSWORD = "hayrjkxcetxoxber"

Medical_Inventory_Model/.gitattributes

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+*.joblib filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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Medical_Inventory_Model/README.md

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+---
+title: Medical Inventory Model
+emoji: 🏃
+colorFrom: yellow
+colorTo: green
+sdk: gradio
+sdk_version: 6.2.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Medical_Inventory_Software/.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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Medical_Inventory_Software/README.md

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+---
+title: Medical Inventory Software
+emoji: 🌖
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 6.2.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

csvclean.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import json\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "need = Medicine_Name\n",
+    "Category\n",
+    "Batch_No\n",
+    "Stock_Quantity\n",
+    "Monthly_Sales\n",
+    "Expiry_Date\n",
+    "Purchase_Date\n",
+    "Supplier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#I generate data using faker library "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"data/data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Medicine_Name       12\n",
+       "Category             7\n",
+       "Batch_No          9576\n",
+       "Stock_Quantity     491\n",
+       "Monthly_Sales      196\n",
+       "Expiry_Date       1347\n",
+       "Purchase_Date      731\n",
+       "Supplier             6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000, 8)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10000 entries, 0 to 9999\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column          Non-Null Count  Dtype \n",
+      "---  ------          --------------  ----- \n",
+      " 0   Medicine_Name   10000 non-null  object\n",
+      " 1   Category        10000 non-null  object\n",
+      " 2   Batch_No        10000 non-null  object\n",
+      " 3   Stock_Quantity  10000 non-null  int64 \n",
+      " 4   Monthly_Sales   10000 non-null  int64 \n",
+      " 5   Expiry_Date     10000 non-null  object\n",
+      " 6   Purchase_Date   10000 non-null  object\n",
+      " 7   Supplier        10000 non-null  object\n",
+      "dtypes: int64(2), object(6)\n",
+      "memory usage: 625.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "data[\"Expiry_Date\"] = pd.to_datetime(data[\"Expiry_Date\"])\n",
+    "data[\"Purchase_Date\"] = pd.to_datetime(data[\"Purchase_Date\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10000 entries, 0 to 9999\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column          Non-Null Count  Dtype         \n",
+      "---  ------          --------------  -----         \n",
+      " 0   Medicine_Name   10000 non-null  object        \n",
+      " 1   Category        10000 non-null  object        \n",
+      " 2   Batch_No        10000 non-null  object        \n",
+      " 3   Stock_Quantity  10000 non-null  int64         \n",
+      " 4   Monthly_Sales   10000 non-null  int64         \n",
+      " 5   Expiry_Date     10000 non-null  datetime64[ns]\n",
+      " 6   Purchase_Date   10000 non-null  datetime64[ns]\n",
+      " 7   Supplier        10000 non-null  object        \n",
+      "dtypes: datetime64[ns](2), int64(2), object(4)\n",
+      "memory usage: 625.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_col = [col for col in data.columns if data[col].dtype ==\"object\"]\n",
+    "num_col = [col for col in data.columns if data[col].dtype !=\"object\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Medicine_Name', 'Category', 'Batch_No', 'Supplier']"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cat_col"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Stock_Quantity', 'Monthly_Sales', 'Expiry_Date', 'Purchase_Date']"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_col"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10000 entries, 0 to 9999\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column          Non-Null Count  Dtype         \n",
+      "---  ------          --------------  -----         \n",
+      " 0   Medicine_Name   10000 non-null  object        \n",
+      " 1   Category        10000 non-null  object        \n",
+      " 2   Batch_No        10000 non-null  object        \n",
+      " 3   Stock_Quantity  10000 non-null  int64         \n",
+      " 4   Monthly_Sales   10000 non-null  int64         \n",
+      " 5   Expiry_Date     10000 non-null  datetime64[ns]\n",
+      " 6   Purchase_Date   10000 non-null  datetime64[ns]\n",
+      " 7   Supplier        10000 non-null  object        \n",
+      "dtypes: datetime64[ns](2), int64(2), object(4)\n",
+      "memory usage: 625.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Medicine_Name     0\n",
+       "Category          0\n",
+       "Batch_No          0\n",
+       "Stock_Quantity    0\n",
+       "Monthly_Sales     0\n",
+       "Expiry_Date       0\n",
+       "Purchase_Date     0\n",
+       "Supplier          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Timestamp('2024-06-22 00:00:00'), Timestamp('2028-06-02 00:00:00'))"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"Expiry_Date\"].min(), data[\"Expiry_Date\"].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "today = pd.Timestamp.today()\n",
+    "\n",
+    "expired = data[data[\"Expiry_Date\"] < today]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soon_expiring = data[\n",
+    "    (data[\"Expiry_Date\"] >= today) &\n",
+    "    (data[\"Expiry_Date\"] <= today + pd.Timedelta(days=30))\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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>Medicine_Name</th>\n",
+       "      <th>Category</th>\n",
+       "      <th>Batch_No</th>\n",
+       "      <th>Stock_Quantity</th>\n",
+       "      <th>Monthly_Sales</th>\n",
+       "      <th>Expiry_Date</th>\n",
+       "      <th>Purchase_Date</th>\n",
+       "      <th>Supplier</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>Cough Syrup</td>\n",
+       "      <td>Cardiac</td>\n",
+       "      <td>COU-8314</td>\n",
+       "      <td>499</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2026-01-12</td>\n",
+       "      <td>2024-07-24</td>\n",
+       "      <td>Pfizer Inc.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>Metformin</td>\n",
+       "      <td>Gastro</td>\n",
+       "      <td>MET-6823</td>\n",
+       "      <td>162</td>\n",
+       "      <td>36</td>\n",
+       "      <td>2026-01-22</td>\n",
+       "      <td>2024-08-29</td>\n",
+       "      <td>Dr. Reddy's</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>Paracetamol</td>\n",
+       "      <td>Diabetes</td>\n",
+       "      <td>PAR-5265</td>\n",
+       "      <td>296</td>\n",
+       "      <td>175</td>\n",
+       "      <td>2026-01-24</td>\n",
+       "      <td>2024-12-18</td>\n",
+       "      <td>Abbott India</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159</th>\n",
+       "      <td>Insulin</td>\n",
+       "      <td>Cardiac</td>\n",
+       "      <td>INS-6815</td>\n",
+       "      <td>159</td>\n",
+       "      <td>51</td>\n",
+       "      <td>2026-01-15</td>\n",
+       "      <td>2024-10-05</td>\n",
+       "      <td>Pfizer Inc.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>Cetirizine</td>\n",
+       "      <td>Gastro</td>\n",
+       "      <td>CET-5797</td>\n",
+       "      <td>433</td>\n",
+       "      <td>110</td>\n",
+       "      <td>2026-01-07</td>\n",
+       "      <td>2024-06-02</td>\n",
+       "      <td>Dr. Reddy's</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9909</th>\n",
+       "      <td>Ibuprofen</td>\n",
+       "      <td>Analgesic</td>\n",
+       "      <td>IBU-8010</td>\n",
+       "      <td>495</td>\n",
+       "      <td>151</td>\n",
+       "      <td>2026-01-19</td>\n",
+       "      <td>2024-11-05</td>\n",
+       "      <td>Abbott India</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9917</th>\n",
+       "      <td>Atorvastatin</td>\n",
+       "      <td>Antibiotic</td>\n",
+       "      <td>ATO-6197</td>\n",
+       "      <td>377</td>\n",
+       "      <td>90</td>\n",
+       "      <td>2026-01-14</td>\n",
+       "      <td>2025-02-01</td>\n",
+       "      <td>Abbott India</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9946</th>\n",
+       "      <td>Vitamin C</td>\n",
+       "      <td>Supplement</td>\n",
+       "      <td>VIT-9050</td>\n",
+       "      <td>352</td>\n",
+       "      <td>126</td>\n",
+       "      <td>2026-01-24</td>\n",
+       "      <td>2024-09-22</td>\n",
+       "      <td>Dr. Reddy's</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9952</th>\n",
+       "      <td>Amoxicillin</td>\n",
+       "      <td>Analgesic</td>\n",
+       "      <td>AMO-8366</td>\n",
+       "      <td>280</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2026-01-23</td>\n",
+       "      <td>2024-10-31</td>\n",
+       "      <td>Lupin Ltd.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9994</th>\n",
+       "      <td>Vitamin C</td>\n",
+       "      <td>Antihistamine</td>\n",
+       "      <td>VIT-5227</td>\n",
+       "      <td>354</td>\n",
+       "      <td>193</td>\n",
+       "      <td>2026-01-08</td>\n",
+       "      <td>2024-03-24</td>\n",
+       "      <td>Cipla Ltd.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>325 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Medicine_Name       Category  Batch_No  Stock_Quantity  Monthly_Sales  \\\n",
+       "54     Cough Syrup        Cardiac  COU-8314             499             18   \n",
+       "92       Metformin         Gastro  MET-6823             162             36   \n",
+       "105    Paracetamol       Diabetes  PAR-5265             296            175   \n",
+       "159        Insulin        Cardiac  INS-6815             159             51   \n",
+       "183     Cetirizine         Gastro  CET-5797             433            110   \n",
+       "...            ...            ...       ...             ...            ...   \n",
+       "9909     Ibuprofen      Analgesic  IBU-8010             495            151   \n",
+       "9917  Atorvastatin     Antibiotic  ATO-6197             377             90   \n",
+       "9946     Vitamin C     Supplement  VIT-9050             352            126   \n",
+       "9952   Amoxicillin      Analgesic  AMO-8366             280             14   \n",
+       "9994     Vitamin C  Antihistamine  VIT-5227             354            193   \n",
+       "\n",
+       "     Expiry_Date Purchase_Date      Supplier  \n",
+       "54    2026-01-12    2024-07-24   Pfizer Inc.  \n",
+       "92    2026-01-22    2024-08-29   Dr. Reddy's  \n",
+       "105   2026-01-24    2024-12-18  Abbott India  \n",
+       "159   2026-01-15    2024-10-05   Pfizer Inc.  \n",
+       "183   2026-01-07    2024-06-02   Dr. Reddy's  \n",
+       "...          ...           ...           ...  \n",
+       "9909  2026-01-19    2024-11-05  Abbott India  \n",
+       "9917  2026-01-14    2025-02-01  Abbott India  \n",
+       "9946  2026-01-24    2024-09-22   Dr. Reddy's  \n",
+       "9952  2026-01-23    2024-10-31    Lupin Ltd.  \n",
+       "9994  2026-01-08    2024-03-24    Cipla Ltd.  \n",
+       "\n",
+       "[325 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "soon_expiring"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Medicine_Name', 'Category', 'Batch_No', 'Stock_Quantity',\n",
+       "       'Monthly_Sales', 'Expiry_Date', 'Purchase_Date', 'Supplier'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0   2024-11-25\n",
+       "1   2023-12-27\n",
+       "2   2025-09-02\n",
+       "3   2025-06-22\n",
+       "4   2025-02-01\n",
+       "Name: Purchase_Date, dtype: datetime64[ns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.Purchase_Date.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['purchase_year'] = data.Purchase_Date.dt.year\n",
+    "data['purchase_month'] = data.Purchase_Date.dt.month\n",
+    "data['purchase_date'] = data.Purchase_Date.dt.day"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['formated_purchase_date'] = data[\"Purchase_Date\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.drop(['formated_purchase_date'],axis=1,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.rename(columns={\"Purchase_Date\":\"formated_purchase_date\"},inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10000 entries, 0 to 9999\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column                  Non-Null Count  Dtype         \n",
+      "---  ------                  --------------  -----         \n",
+      " 0   Medicine_Name           10000 non-null  object        \n",
+      " 1   Category                10000 non-null  object        \n",
+      " 2   Batch_No                10000 non-null  object        \n",
+      " 3   Stock_Quantity          10000 non-null  int64         \n",
+      " 4   Monthly_Sales           10000 non-null  int64         \n",
+      " 5   Expiry_Date             10000 non-null  datetime64[ns]\n",
+      " 6   formated_purchase_date  10000 non-null  datetime64[ns]\n",
+      " 7   Supplier                10000 non-null  object        \n",
+      " 8   purchase_year           10000 non-null  int32         \n",
+      " 9   purchase_month          10000 non-null  int32         \n",
+      " 10  purchase_date           10000 non-null  int32         \n",
+      "dtypes: datetime64[ns](2), int32(3), int64(2), object(4)\n",
+      "memory usage: 742.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

dashboard.py

@@ -0,0 +1,151 @@
+import streamlit as st
+import pandas as pd
+import urllib.parse
+
+# ---------------- Page Config ----------------
+st.set_page_config(
+    page_title="Pharma Expiry Dashboard",
+    page_icon="💊",
+    layout="wide"
+)
+
+st.title("💊 Pharma Inventory Expiry Alert System")
+
+# ---------------- File Upload ----------------
+uploaded_file = st.file_uploader(
+    "📂 Choose your Datafile",
+    type=["csv", "xlsx", "json"]
+)
+
+if uploaded_file is None:
+    st.info("⬆ Upload CSV / Excel / JSON file to continue")
+    st.stop()
+
+# ---------------- Load Data ----------------
+if uploaded_file.name.endswith(".csv"):
+    data = pd.read_csv(uploaded_file)
+elif uploaded_file.name.endswith(".xlsx"):
+    data = pd.read_excel(uploaded_file)
+else:
+    data = pd.read_json(uploaded_file)
+
+# ---------------- Safe Date Conversion ----------------
+data["Expiry_Date"] = pd.to_datetime(data["Expiry_Date"], errors="coerce")
+data["Purchase_Date"] = pd.to_datetime(data["Purchase_Date"], errors="coerce")
+
+today = pd.Timestamp.today()
+
+# ---------------- Sidebar Filters ----------------
+st.sidebar.header("🔎 Filters")
+
+category_filter = st.sidebar.multiselect(
+    "Category",
+    options=sorted(data["Category"].dropna().unique())
+)
+
+supplier_filter = st.sidebar.multiselect(
+    "Supplier",
+    options=sorted(data["Supplier"].dropna().unique())
+)
+
+expiry_filter = st.sidebar.selectbox(
+    "Expiry Status",
+    ["All", "Expired", "Expiring in 30 Days", "Safe"]
+)
+
+
+# ---------------- Apply Filters ----------------
+filtered_data = data.copy()
+
+if category_filter:
+    filtered_data = filtered_data[filtered_data["Category"].isin(category_filter)]
+
+if supplier_filter:
+    filtered_data = filtered_data[filtered_data["Supplier"].isin(supplier_filter)]
+
+if expiry_filter == "Expired":
+    filtered_data = filtered_data[filtered_data["Expiry_Date"] < today]
+
+elif expiry_filter == "Expiring in 30 Days":
+    filtered_data = filtered_data[
+        (filtered_data["Expiry_Date"] >= today) &
+        (filtered_data["Expiry_Date"] <= today + pd.Timedelta(days=30))
+    ]
+
+elif expiry_filter == "Safe":
+    filtered_data = filtered_data[filtered_data["Expiry_Date"] > today + pd.Timedelta(days=30)]
+
+# ---------------- KPI Metrics ----------------
+expired_count = data[data["Expiry_Date"] < today].shape[0]
+expiring_count = data[
+    (data["Expiry_Date"] >= today) &
+    (data["Expiry_Date"] <= today + pd.Timedelta(days=30))
+].shape[0]
+
+col1, col2, col3, col4 = st.columns(4)
+
+col1.metric("🧾 Total Medicines", data["Medicine_Name"].nunique())
+col2.metric("📦 Total Stock", data["Stock_Quantity"].sum())
+col3.metric("🚨 Expired", expired_count)
+col4.metric("⚠ Expiring (30 Days)", expiring_count)
+
+st.write(filtered_data)
+st.write(filtered_data['Category'].value_counts())
+st.write(filtered_data['Supplier'].value_counts())
+st.markdown(f"Resultant Medicine are {filtered_data['Category'].count()}")
+
+st.divider()
+st.subheader("📢 Notify Supplier")
+
+# Supplier selection
+selected_supplier = st.selectbox(
+    "Select Supplier",
+    sorted(filtered_data["Supplier"].dropna().unique())
+)
+
+# Contact inputs
+supplier_mobile = st.text_input("📱 Supplier Mobile (with country code)", placeholder="91XXXXXXXXXX")
+
+# Data to send (expired + expiring)
+alert_data = filtered_data[
+    filtered_data["Expiry_Date"] <= today + pd.Timedelta(days=30)
+]
+
+def generate_message(df, supplier):
+    message = f"Expiry Alert for Supplier: {supplier}\n\n"
+
+    # Details of each medicine
+    for _, row in df.iterrows():
+        message += (
+            f"Medicine: {row['Medicine_Name']}\n"
+            f"Batch: {row['Batch_No']}\n"
+            f"Expiry: {row['Expiry_Date'].date()}\n"
+            f"Stock: {row['Stock_Quantity']}\n"
+            "----------------------\n"
+        )
+
+    # 🔢 Totals
+    total_expiry_medicines = len(df)
+    unique_medicine_count = df['Medicine_Name'].unique()
+    category_count = df['Category'].unique()
+
+    message += (
+        "\n===== SUMMARY =====\n"
+        f"Total Expiry Medicines: {total_expiry_medicines}\n"
+        f"Total Unique Medicine Names: {unique_medicine_count}\n"
+        f"Total Categories Affected: {category_count}\n"
+    )
+
+    return message
+
+
+if st.button("📲 Submit Number"):
+    if supplier_mobile and not alert_data.empty:
+        message = generate_message(alert_data, selected_supplier)
+        encoded_msg = urllib.parse.quote(message)
+        whatsapp_url = f"https://wa.me/{supplier_mobile}?text={encoded_msg}"
+
+        # st.button(whatsapp_url)
+        st.link_button("Send Order WhatsApp", whatsapp_url)
+    else:
+        st.error("❌ Please provide mobile number & ensure data exists")

data/pharmacy_inventory_25.csv

@@ -0,0 +1,26 @@
+Medicine_Name,Category,Batch_No,Stock_Quantity,Monthly_Sales,Expiry_Date,Purchase_Date,Supplier
+Atorvastatin,Gastro,ATO-3028,333,57,2026-10-06,2025-04-30,Pfizer Inc.
+Paracetamol,Antihistamine,PAR-1696,173,141,2026-06-16,2024-12-11,Sun Pharma
+Atorvastatin,Analgesic,ATO-7955,467,21,2027-05-31,2025-04-24,Sun Pharma
+Cough Syrup,Cardiac,COU-3875,217,158,2028-01-05,2025-08-02,Cipla Ltd.
+Aspirin,Analgesic,ASP-8028,454,163,2026-05-07,2024-07-11,Pfizer Inc.
+Cetirizine,Analgesic,CET-8808,316,136,2025-12-25,2025-06-24,Sun Pharma
+Pantoprazole,Supplement,PAN-8731,42,188,2027-02-18,2025-04-25,Pfizer Inc.
+Metformin,Cardiac,MET-3112,129,152,2026-02-01,2024-01-07,Lupin Ltd.
+Paracetamol,Gastro,PAR-3249,10,178,2026-07-02,2025-09-28,Cipla Ltd.
+Paracetamol,Antihistamine,PAR-1313,151,122,2025-12-13,2024-01-30,Dr. Reddy's
+Atorvastatin,Gastro,ATO-3383,369,186,2026-04-05,2025-04-20,Pfizer Inc.
+Metformin,Antibiotic,MET-1481,189,73,2027-01-27,2025-11-03,Sun Pharma
+Aspirin,Antibiotic,ASP-8411,287,120,2026-02-27,2024-08-08,Cipla Ltd.
+Pantoprazole,Analgesic,PAN-5943,157,199,2025-10-03,2024-08-31,Dr. Reddy's
+Metformin,Analgesic,MET-9980,385,18,2026-04-08,2024-04-28,Sun Pharma
+Ibuprofen,Supplement,IBU-4706,265,81,2024-12-20,2024-01-19,Lupin Ltd.
+Pantoprazole,Antihistamine,PAN-6321,321,27,2027-09-14,2025-10-19,Cipla Ltd.
+Vitamin C,Analgesic,VIT-4816,241,128,2025-06-26,2024-09-29,Abbott India
+Metformin,Cardiac,MET-2270,350,12,2025-10-21,2024-08-25,Cipla Ltd.
+Pantoprazole,Gastro,PAN-9420,235,186,2025-11-02,2024-05-07,Pfizer Inc.
+Cetirizine,Analgesic,CET-7232,325,147,2025-02-18,2024-07-03,Dr. Reddy's
+Aspirin,Cardiac,ASP-6418,275,113,2025-11-27,2024-01-08,Pfizer Inc.
+Aspirin,Antihistamine,ASP-8208,354,200,2025-09-24,2024-08-29,Dr. Reddy's
+Cough Syrup,Supplement,COU-2116,338,102,2025-12-28,2024-12-13,Pfizer Inc.
+Ibuprofen,Antihistamine,IBU-3657,188,87,2027-05-16,2025-02-18,Abbott India

pyfile_data_gen.py

@@ -0,0 +1,66 @@
+import pandas as pd
+import random
+from faker import Faker
+from datetime import timedelta
+
+fake = Faker()
+
+# -----------------------------
+# CONFIG
+# -----------------------------
+NUM_RECORDS = 25
+
+medicine_names = [
+    "Paracetamol", "Amoxicillin", "Azithromycin", "Atorvastatin",
+    "Ibuprofen", "Metformin", "Cetirizine", "Pantoprazole",
+    "Aspirin", "Cough Syrup", "Vitamin C", "Insulin"
+]
+
+categories = [
+    "Analgesic", "Antibiotic", "Antihistamine",
+    "Cardiac", "Diabetes", "Gastro", "Supplement"
+]
+
+suppliers = [
+    "Sun Pharma", "Cipla Ltd.", "Dr. Reddy's",
+    "Pfizer Inc.", "Abbott India", "Lupin Ltd."
+]
+
+# -----------------------------
+# DATA GENERATION
+# -----------------------------
+data = []
+
+for _ in range(NUM_RECORDS):
+    medicine = random.choice(medicine_names)
+    category = random.choice(categories)
+    supplier = random.choice(suppliers)
+
+    purchase_date = fake.date_between(start_date="-2y", end_date="today")
+    expiry_date = purchase_date + timedelta(days=random.randint(180, 900))
+
+    record = {
+        "Medicine_Name": medicine,
+        "Category": category,
+        "Batch_No": f"{medicine[:3].upper()}-{random.randint(1000,9999)}",
+        "Stock_Quantity": random.randint(10, 500),
+        "Monthly_Sales": random.randint(5, 200),
+        "Expiry_Date": expiry_date,
+        "Purchase_Date": purchase_date,
+        "Supplier": supplier
+    }
+
+    data.append(record)
+
+# -----------------------------
+# CREATE DATAFRAME
+# -----------------------------
+df = pd.DataFrame(data)
+
+# -----------------------------
+# SAVE TO CSV
+# -----------------------------
+df.to_csv("pharmacy_inventory_25.csv", index=False)
+
+print("✅ 10,000 pharmacy inventory records generated successfully!")
+print(df.head())

requirements.txt

@@ -0,0 +1,7 @@
+streamlit==1.32.0
+pandas==2.2.2
+numpy==1.26.4
+matplotlib==3.8.4
+statsmodels==0.14.2
+python-dateutil==2.9.0.post0
+faker==25.2.0

utils/data_utils.py

@@ -0,0 +1,14 @@
+import pandas as pd
+
+def load_data(file):
+    if file.name.endswith(".csv"):
+        df = pd.read_csv(file)
+    elif file.name.endswith(".xlsx"):
+        df = pd.read_excel(file)
+    else:
+        df = pd.read_json(file)
+
+    df["Expiry_Date"] = pd.to_datetime(df["Expiry_Date"], errors="coerce")
+    df["Purchase_Date"] = pd.to_datetime(df["Purchase_Date"], errors="coerce")
+
+    return df

utils/message_utils.py

@@ -0,0 +1,13 @@
+def generate_message(df, supplier):
+    message = f"Expiry Alert for Supplier: {supplier}\n\n"
+    for _, row in df.iterrows():
+        message += (
+            f"Medicine: {row['Medicine_Name']}\n"
+            f"Batch: {row['Batch_No']}\n"
+            f"Expiry: {row['Expiry_Date'].date()}\n"
+            f"Stock: {row['Stock_Quantity']}\n"
+            f"Total Expiry Medicine: {row['Medicine_Name']}.value_counts()\n"
+            f"Total Expiry Category: {row['Category']}.value_counts()\n"
+            "----------------------\n"
+        )
+    return message