Chemistry Molecule Visualizer

 import streamlit as st

from rdkit import Chem

from rdkit.Chem import Draw, Descriptors

import py3Dmol

def mol_to_3d_view(smiles):

    mol = Chem.MolFromSmiles(smiles)

    mb = Chem.AddHs(mol)

    Chem.EmbedMolecule(mb)

    mol_block = Chem.MolToMolBlock(mb)

    viewer = py3Dmol.view(width=400, height=400)

    viewer.addModel(mol_block, 'mol')

    viewer.setStyle({'stick': {}})

    viewer.zoomTo()

    return viewer

st.title("🧪 Chemistry Molecule Visualizer")

smiles = st.text_input("Enter SMILES string", "CC(=O)O")  # Acetic Acid

if smiles:

    mol = Chem.MolFromSmiles(smiles)

    

    if mol:

        st.subheader("📌 Molecular Structure (2D)")

        st.image(Draw.MolToImage(mol, size=(300, 300)))

        st.subheader("🔬 Properties")

        st.markdown(f"**Formula**: {Chem.rdMolDescriptors.CalcMolFormula(mol)}")

        st.markdown(f"**Molecular Weight**: {Descriptors.MolWt(mol):.2f} g/mol")

        st.subheader("🧬 3D Structure")

        viewer = mol_to_3d_view(smiles)

        viewer_html = viewer._make_html()

        st.components.v1.html(viewer_html, height=450)

    else:

        st.error("Invalid SMILES string. Try again.")

Receipt Text Extractor & Analyzer

 OCR using pytesseract

from PIL import Image

import pytesseract

def extract_text_from_image(image_path):

    image = Image.open(image_path)

    text = pytesseract.image_to_string(image)

    return text

Parse Items and Prices with re

import re

def parse_items(raw_text):

    # Match lines like: "Bread 2.50" or "Milk ....... 1.25"

    pattern = r"([A-Za-z\s]+)\s+([\d]+\.\d{2})"

    matches = re.findall(pattern, raw_text)

    

    items = [{"item": item.strip(), "price": float(price)} for item, price in matches]

    

    total = sum(i["price"] for i in items)

    avg = total / len(items) if items else 0

    

    return items, total, avg

(Optional) Step 3: Streamlit Interface

import streamlit as st

from utils.text_parser import extract_text_from_image, parse_items

import tempfile

import pandas as pd

st.title("🧾 Receipt Text Extractor & Analyzer")

uploaded_file = st.file_uploader("Upload Receipt Image", type=["jpg", "png", "jpeg"])

if uploaded_file:

    with tempfile.NamedTemporaryFile(delete=False) as tmp:

        tmp.write(uploaded_file.read())

        tmp_path = tmp.name

    raw_text = extract_text_from_image(tmp_path)

    items, total, avg = parse_items(raw_text)

    df = pd.DataFrame(items)

    st.subheader("🛒 Items Detected:")

    st.table(df)

    st.markdown(f"**Total Cost:** ₹{total:.2f}")

    st.markdown(f"**Average Item Cost:** ₹{avg:.2f}")

    # Download as CSV

    csv = df.to_csv(index=False).encode()

    st.download_button("📥 Download CSV", csv, "receipt_data.csv", "text/csv")

Music Genre Classifier

 # utils/feature_extractor.py

import librosa

import numpy as np

def extract_features(file_path):

    y, sr = librosa.load(file_path, duration=30)

    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40)

    mfccs_mean = np.mean(mfccs.T, axis=0)

    return mfccs_mean

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import train_test_split

import pandas as pd

import joblib

# Load features (extracted previously)

df = pd.read_csv("features_dataset.csv")  # Your dataset with MFCC + genre

X = df.drop('genre', axis=1)

y = df['genre']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

clf = RandomForestClassifier()

clf.fit(X_train, y_train)

# Save model

joblib.dump(clf, "model/genre_model.pkl")

# app.py

import streamlit as st

import joblib

import numpy as np

from utils.feature_extractor import extract_features

import tempfile

# Load model

model = joblib.load("model/genre_model.pkl")

st.title("🎵 Music Genre Classifier")

st.write("Upload a music file to predict its genre")

uploaded_file = st.file_uploader("Choose a file", type=["mp3", "wav"])

if uploaded_file:

    # Save temporarily

    with tempfile.NamedTemporaryFile(delete=False) as tmp:

        tmp.write(uploaded_file.read())

        tmp_path = tmp.name

    # Extract features and predict

    features = extract_features(tmp_path)

    prediction = model.predict([features])[0]

    proba = model.predict_proba([features])

    st.success(f"Predicted Genre: **{prediction}**")

    st.bar_chart(proba[0])

Custom Dictionary Builder

 db.py

import sqlite3

def init_db():

    conn = sqlite3.connect("words.db")

    cursor = conn.cursor()

    cursor.execute('''CREATE TABLE IF NOT EXISTS dictionary (

                        id INTEGER PRIMARY KEY AUTOINCREMENT,

                        word TEXT,

                        language TEXT,

                        meaning TEXT,

                        synonyms TEXT,

                        audio_file TEXT)''')

    conn.commit()

    conn.close()

def add_word(word, lang, meaning, synonyms, audio_file):

    conn = sqlite3.connect("words.db")

    cursor = conn.cursor()

    cursor.execute("INSERT INTO dictionary (word, language, meaning, synonyms, audio_file) VALUES (?, ?, ?, ?, ?)",

                   (word, lang, meaning, synonyms, audio_file))

    conn.commit()

    conn.close()

def search_word(word):

    conn = sqlite3.connect("words.db")

    cursor = conn.cursor()

    cursor.execute("SELECT * FROM dictionary WHERE word = ?", (word,))

    result = cursor.fetchone()

    conn.close()

    return result

tts.py

from gtts import gTTS

import os

def generate_audio(word, lang='en'):

    tts = gTTS(text=word, lang=lang)

    audio_file = f"audio/{word}_{lang}.mp3"

    tts.save(audio_file)

    return audio_file

app.py

import tkinter as tk

from db import init_db, add_word, search_word

from tts import generate_audio

import os

from playsound import playsound

init_db()

def submit_word():

    word = entry_word.get()

    lang = entry_lang.get()

    meaning = entry_meaning.get()

    synonyms = entry_synonyms.get()

    audio_path = generate_audio(word, lang)

    add_word(word, lang, meaning, synonyms, audio_path)

    label_status.config(text="✅ Word added!")

def play_audio():

    word = entry_word.get()

    result = search_word(word)

    if result and os.path.exists(result[5]):

        playsound(result[5])

    else:

        label_status.config(text="❌ Audio not found.")

# GUI Setup

window = tk.Tk()

window.title("📚 Custom Dictionary Builder")

window.geometry("400x400")

entry_word = tk.Entry(window)

entry_word.insert(0, "Word")

entry_word.pack(pady=5)

entry_lang = tk.Entry(window)

entry_lang.insert(0, "Language Code (e.g., en, es)")

entry_lang.pack(pady=5)

entry_meaning = tk.Entry(window)

entry_meaning.insert(0, "Meaning")

entry_meaning.pack(pady=5)

entry_synonyms = tk.Entry(window)

entry_synonyms.insert(0, "Synonyms")

entry_synonyms.pack(pady=5)

tk.Button(window, text="Add Word", command=submit_word).pack(pady=10)

tk.Button(window, text="Play Pronunciation", command=play_audio).pack(pady=5)

label_status = tk.Label(window, text="")

label_status.pack(pady=10)

window.mainloop()

Language Codes for gTTS:

• English: en

• Hindi: hi

• Spanish: es

• French: fr

• Malayalam: ml

• Tamil: ta

Mindfulness & Focus Timer App

 main.py

import tkinter as tk

from timer import start_pomodoro

from breathing import start_breathing

from prompts import get_prompt

window = tk.Tk()

window.title("🧠 Mindfulness & Focus Timer")

window.geometry("400x300")

label = tk.Label(window, text="Welcome to Focus Time!", font=("Helvetica", 16))

label.pack(pady=10)

tk.Button(window, text="Start Pomodoro", command=start_pomodoro).pack(pady=10)

tk.Button(window, text="Breathing Exercise", command=start_breathing).pack(pady=10)

tk.Button(window, text="Get Mindfulness Prompt", command=lambda: label.config(text=get_prompt())).pack(pady=10)

window.mainloop()

timer.py

import time

import pygame

import threading

def play_sound():

    pygame.init()

    pygame.mixer.init()

    pygame.mixer.music.load("sounds/ding.wav")

    pygame.mixer.music.play()

def start_pomodoro():

    def run():

        print("Focus time started!")

        for i in range(25 * 60, 0, -1):

            mins, secs = divmod(i, 60)

            print(f"{mins:02d}:{secs:02d}", end='\r')

            time.sleep(1)

        play_sound()

        print("\nTime for a break!")

    threading.Thread(target=run).start()

breathing.py

import time

import threading

def start_breathing():

    def run():

        print("Follow the breathing pattern:")

        for _ in range(4):

            print("Inhale... 🫁")

            time.sleep(4)

            print("Hold... ✋")

            time.sleep(4)

            print("Exhale... 😮‍💨")

            time.sleep(4)

    threading.Thread(target=run).start()

prompts.py

import random

prompts = [

    "Breathe in clarity, breathe out stress.",

    "Focus on one thing at a time.",

    "You are in control of your day.",

    "Be here now. 🌱",

    "Let go of what you can't control."

]

def get_prompt():

    return random.choice(prompts)