---

🚀 Key Highlights

• Bilingual Interface: Write code using either Hindi keywords or standard English syntax interchangeably.
• Real Python Objects: Objects remain genuine pandas.DataFrame, numpy.ndarray, torch.Tensor, sklearn models.
• Universal & Library-Specific Aliases:
  • model.sikhao(X, y) ➔ model.fit(X, y)
  • model.bhavishyavani(X) ➔ model.predict(X)
  • np.sarni([1, 2, 3]) ➔ np.array([1, 2, 3])
  • pd.csv_padho("file.csv") ➔ pd.read_csv("file.csv")
  • plt.rekha(x, y) ➔ plt.plot(x, y)
  • ganit.vargmool(16) ➔ math.sqrt(16)
• Full Keyword Argument Support: train_test_split(X, y, test_size=0.25, random_state=42).
• Multi-Variable Unpacking: maano X_train, X_test, y_train, y_test = train_test_split(...).
• Desktop GUI IDE Included: Full-featured dark-mode code editor with syntax highlighting, line numbers, and live output terminal.
• Standalone .EXE Executables: Pre-compiled binaries (IndLan_IDE.exe and IndLan.exe) for Windows.
• Missing Package Diagnostics: Actionable installation instructions recommending specific pip extras.

---

💻 IndLan 2.0 Desktop GUI IDE

IndLan 2.0 comes with a built-in Desktop GUI IDE:

indlan ide        # or simply: indlan

IDE Features:

• Real-Time Bilingual Syntax Highlighting: Color-coded syntax for Hindi keywords, English keywords, ML method aliases, strings, numbers, and comments.
• Synchronized Line Numbers Gutter: Tracks cursor and scrolling.
• Multi-Tab File Management: Open, edit, and save multiple .ind files.
• Integrated Terminal Console: View program output and errors with color tags.
• One-Click Run (F5) & Output Clearing.
• Preloaded Code Templates: Instant snippets for Data Science, Machine Learning, Plotting, and OOP.

---

📦 Installation & Extras

Core Engine

pip install indlan

Data Science Extras (Pandas, NumPy, Matplotlib, Seaborn, Openpyxl, Joblib)

pip install "indlan[data]"

Machine Learning Extras (Scikit-learn, SciPy, Statsmodels)

pip install "indlan[ml]"

AI & Deep Learning Extras (TensorFlow, PyTorch, Torchvision, Pillow)

pip install "indlan[ai]"

Full Ecosystem Extras

pip install "indlan[full]"

---

⚡ CLI Commands

indlan                             # Launch Desktop GUI IDE
indlan ide                         # Launch Desktop GUI IDE
indlan repl                        # Start Interactive REPL
indlan program.ind                 # Run an IndLan script
indlan run program.ind             # Run an IndLan script
indlan --debug program.ind         # Run with detailed Python tracebacks
indlan --version                   # Show version (2.0.1)
indlan --help                      # Show help menu

---

📖 Complete Data Science Example

🇮🇳 Hindi Syntax (program_hindi.ind)

aayat pandas ke_roop_mein pd
aayat numpy ke_roop_mein np
aayat matplotlib.pyplot ke_roop_mein plt

se sklearn.model_selection aayat train_test_split
se sklearn.ensemble aayat RandomForestClassifier
se sklearn.metrics aayat satikta_ank

chhap("=== IndLan Hindi Data Science Pipeline ===")

maano data = pd.csv_padho("students.csv")
chhap("Pehle 5 Rows:\n", data.shuru_ke(5))
chhap("Aakar (Shape):", data.shape)

maano X = np.sarni([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9]])
maano y = np.sarni([0, 0, 0, 0, 1, 1, 1, 1])

maano X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.25,
    random_state=42
)

maano model = RandomForestClassifier(random_state=42)
model.sikhao(X_train, y_train)

maano prediction = model.bhavishyavani(X_test)
chhap("Predictions:", prediction)
chhap("Accuracy Score:", satikta_ank(y_test, prediction))

plt.figure()
plt.rekha(prediction)
plt.sheershak("Prediction Chart")
plt.jaal(true)
plt.dikhao()

🇬🇧 English Syntax (program_english.ind)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

print("=== IndLan English Data Science Pipeline ===")

data = pd.read_csv("students.csv")
print("First 5 Rows:\n", data.head(5))
print("Shape:", data.shape)

X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [7, 8], [8, 9]])
y = np.array([0, 0, 0, 0, 1, 1, 1, 1])

X_train, X_test, y_train, y_test = train_test_split(
    X,
    y,
    test_size=0.25,
    random_state=42
)

model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

prediction = model.predict(X_test)
print("Predictions:", prediction)
print("Accuracy Score:", accuracy_score(y_test, prediction))

plt.figure()
plt.plot(prediction)
plt.title("Prediction Chart")
plt.grid(true)
plt.show()

---

📚 Keyword & Alias Reference

Keywords

English	Hindi Alias	Purpose
import	aayat	Import Python module
from	se	Import from package
as	ke_roop_mein	Create alias
let	maano	Variable assignment
fun	kaam	Define function
return	vapas	Return value
if	agar	If condition
elif	nahito_agar	Else if condition
else	nahito	Else branch
while	jabtak	While loop
do	karo	Do-while loop
for	pratyek	For-each loop
in	mein	In operator
switch	vibhag	Switch statement
case	sthiti	Switch case
default	anyatha	Default case
class	varg	Define class
new	naya	Instantiate class
this	yeh	Self instance
true	sahi	True boolean
false	galat	False boolean
null	khaali	Null / None
and	aur	Logical AND
or	ya	Logical OR
not	nahi	Logical NOT
break	roko	Break loop
continue	jaari	Continue loop
print	chhap	Print to stdout

Universal Method Aliases

English Method	Hindi Method Alias
fit	sikhao
predict	bhavishyavani
transform	badlo
fit_transform	sikhao_aur_badlo
compile	sankalit_karo
evaluate	mulyankan_karo
train	prashikshan_karo
test	parikshan_karo
save / dump	bachao
load	bharit_karo
read	padho
write	likho
append	jodo
drop / remove	hatao
filter	chhano
sort	kramit_karo
groupby	samuha_banao
merge	milao
reshape	aakar_badlo

Pandas Aliases

English	Hindi Alias
pd.read_csv()	pd.csv_padho()
pd.DataFrame()	pd.dataframe()
pd.Series()	pd.shrankhla()
pd.read_excel()	pd.excel_padho()
pd.read_json()	pd.json_padho()
df.head()	df.shuru_ke()
df.tail()	df.ant_ke()
df.fillna()	df.khali_bharo()
df.sort_values()	df.maan_kramit_karo()
df.describe()	df.vivaran()

NumPy Aliases

English	Hindi Alias
np.array()	np.sarni()
np.zeros()	np.shunya()
np.ones()	np.ek()
np.arange()	np.kram()
np.linspace()	np.samantar_kram()
np.mean()	np.madhya()
np.median()	np.madhyika()
np.sum()	np.yog()
np.min()	np.nyunatam()
np.max()	np.adhiktam()
np.std()	np.manak_vichalan()

Matplotlib & Seaborn Aliases

English	Hindi Alias
plt.plot()	plt.rekha()
plt.scatter()	plt.bindu()
plt.bar()	plt.stambh()
plt.hist()	plt.vitaran()
plt.pie()	plt.vritt()
plt.title()	plt.sheershak()
plt.xlabel()	plt.x_namankit()
plt.ylabel()	plt.y_namankit()
plt.grid()	plt.jaal()
plt.show()	plt.dikhao()
plt.savefig()	plt.chitra_bachao()
plt.close()	plt.band_karo()
sns.lineplot()	sns.rekha_chitra()
sns.heatmap()	sns.tapman_naksha()

Math / Ganit Aliases

English	Hindi Alias
math.sqrt()	ganit.vargmool()
math.pow()	ganit.ghat()
math.fabs()	ganit.nirapeksh()
math.floor()	ganit.neeche_purnank()
math.ceil()	ganit.upar_purnank()
math.sin()	ganit.jya()
math.cos()	ganit.kojya()
math.tan()	ganit.sparshrekha()
math.exp()	ganit.ghatank()
math.pi	ganit.pai
math.e	ganit.aadhar

---

🛠️ Native File Helpers

IndLan provides out-of-the-box native functions for quick file operations:

• csv_padho(filepath): Reads CSV files into a list of records.
• csv_likho(filepath, data): Writes lists/dicts or DataFrames to CSV.
• csv_jodo(filepath, row): Appends a row to a CSV file.
• csv_badlo(filepath, callback): Transforms CSV rows using a function.
• csv_chhano(filepath, predicate): Filters CSV rows using a condition.
• json_padho(filepath): Reads JSON into native data structures.
• json_likho(filepath, data): Writes data structures to formatted JSON.
• excel_padho(filepath): Reads Excel files (.xlsx, .xlsm).
• excel_likho(filepath, data): Writes data to Excel files.

---

⌨️ User-Input Functions

IndLan provides intuitive bilingual functions to read user input from the console, terminal, or IDE:

English Function	IndLan Function	Return Type	Description
input(prompt?)	aalao(prompt?)	string	Reads raw user input as a string
input_int(prompt?)	number_dalao(prompt?)	integer	Reads input and converts it to integer
input_float(prompt?)	decimal_dalao(prompt?)	float	Reads input and converts it to float
input_bool(prompt?)	haan_na(prompt?)	bool (true / false)	Reads input and converts to boolean (sahi / galat)

Example

maano naam = aalao("Apna naam: ")
maano umar = number_dalao("Umar: ")
maano height = decimal_dalao("Height: ")
maano pass = haan_na("Continue? (true/false): ")

chhap("Namaste", naam, "| Umar:", umar, "| Height:", height, "| Pass:", pass)

---

🚢 Publishing to PyPI

To publish a new version of IndLan to PyPI:

1. Build Distribution Packages:

   python -m build
   

2. Validate Packages with Twine:

   python -m twine check dist/*
   

3. Upload to PyPI:

   python -m twine upload dist/indlan-2.0.1*
   

   (Enter __token__ for username and your PyPI API token for password).

---

🔨 Building Standalone .EXE Binaries

To build standalone Windows executables (IndLan.exe and IndLan_IDE.exe):

python build_exe.py

The output binaries with custom icons are placed in the dist/ directory.

---

📄 License

This project is licensed under the MIT License.

Made with ❤️ by Bhavya S Solanki.#