Word Cloud Generator using Python and Streamlit

Introduction

A word cloud is the fastest way to answer *"what is this text actually about?"* — frequencies become font sizes and the dominant topics jump out instantly. This app takes pasted text or an uploaded file and renders a customizable word cloud: your colors, your stopwords, your shape. It is also a sneaky-good text-analysis primer, sitting between the raw reading of the sentiment analyzer and the charting of the Pandas dashboard.

The wordcloud library handles layout (fitting words without overlap) in C-speed; your job is feeding it clean text and good parameters.

Features

Two inputs — paste text or upload a .txt/.md file.

Stopword control — remove filler words, optionally add your own.

Custom look — background color, colormap, and max words.

Frequency table — top 15 words with counts beside the cloud.

PNG download — export at full resolution.

Prerequisites

Python 3.8+ — from python.org.

Dependencies — install with pip:

code

pip install streamlit wordcloud matplotlib

Step 1: Create the Script

Save as wordcloud_app.py:

code

import streamlit as st
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
from collections import Counter
import re

st.set_page_config(page_title="Word Cloud Generator", page_icon="☁️")
st.title("☁️ Word Cloud Generator")

tab_text, tab_file = st.tabs(["Paste text", "Upload file"])
text = ""
with tab_text:
    text = st.text_area("Your text", height=200, placeholder="Paste an article, essay, or transcript...")
with tab_file:
    f = st.file_uploader("Upload .txt or .md", type=["txt", "md"])
    if f:
        text = f.getvalue().decode("utf-8", errors="ignore")

col1, col2, col3 = st.columns(3)
colormap = col1.selectbox("Colors", ["viridis", "plasma", "Blues", "Reds", "darkgreen"])
bg = col2.selectbox("Background", ["white", "black", "#0e1117"])
max_words = col3.slider("Max words", 30, 300, 120)

extra_stop = st.text_input("Extra stopwords (comma-separated)")

if st.button("☁️ Generate", type="primary") and text.strip():
    stops = set(STOPWORDS) | {w.strip().lower() for w in extra_stop.split(",") if w.strip()}
    words = re.findall(r"[a-zA-Z']{3,}", text.lower())
    filtered = [w for w in words if w not in stops]

    if filtered:
        wc = WordCloud(
            width=900, height=500, background_color=bg,
            colormap=colormap, max_words=max_words, stopwords=stops,
            collocations=False,
        ).generate(" ".join(filtered))

        left, right = st.columns([0.65, 0.35])
        with left:
            fig, ax = plt.subplots(figsize=(9, 5))
            ax.imshow(wc, interpolation="bilinear")
            ax.axis("off")
            st.pyplot(fig)

            buf = io.BytesIO()
            wc.to_image().save(buf, format="PNG")
            st.download_button("⬇️ Download PNG", buf.getvalue(), "wordcloud.png", "image/png")
        with right:
            st.markdown("**Top 15 words**")
            for word, count in Counter(filtered).most_common(15):
                st.markdown(f"`{word}` — {count}")
    else:
        st.warning("No words left after stopword filtering!")

Step 2: Run the App

code

streamlit run wordcloud_app.py

Paste a few paragraphs (try your own blog posts!), generate, and tweak colors and stopwords until it looks right.

How It Works

The pipeline is text → tokens → frequencies → layout. A regex keeps alphabetic words of 3+ characters, stopwords get filtered, and WordCloud.generate() counts what remains and packs words largest-first into the canvas, rotating some for density. Setting collocations=False stops the library from gluing bigrams like "new york" into single entries — which keeps the frequency table and the cloud consistent.

The top-15 sidebar is deliberately built with collections.Counter rather than the library's internal counts, so the numbers you see are the words you filtered — a small transparency win when you are tuning stopwords.

The download uses wc.to_image(), which hands back a PIL image you can save to a BytesIO buffer — the same export path used in the QR generator.

Common Errors & Fixes

`WordCloud` receives an empty list / blank image — every token was a stopword (common with short text); the filtered guard catches this with a friendly warning.

Font error on some systems — the library bundles a default font, but custom font_path= values must point to a real .ttf file.

"the", "and" still appearing — STOPWORDS covers common English only; add domain words via the extra-stopwords input (try "said, like, just").

Matplotlib figure has white margins — that is figure padding; pass bbox_inches="tight", pad_inches=0 to plt.savefig if you export via matplotlib instead of to_image().

Key Concepts

Tokenize → filter → count — the core of nearly all text analysis.

Stopwords — filler words excluded so signal words dominate.

`collocations=False` — treat words individually, not as phrase pairs.

PIL export — any matplotlib/wordcloud figure becomes a downloadable image.

What to Try Next

Render the cloud inside a shape by passing a mask array (a heart, a logo silhouette).

Compare two texts side by side with st.columns — two clouds, one insight.

Feed it the OCR app's output for an image-to-cloud pipeline.

Add bigram mode (collocations=True + include_numbers=False) and see how phrases change the story.

FAQ

Why do word sizes look random?

They are proportional to frequency, but layout packing adds rotation and position variance for readability. The *relative* sizes are exact; positions are aesthetic.

Can I use non-English text?

Yes, but pass a font_path to a font covering your script (Devanagari, Arabic, CJK) and adjust the tokenizing regex, since [a-zA-Z] excludes non-Latin letters.

Is a word cloud actually useful for analysis?

As a first glance, yes — dominant terms in seconds. Pair it with the frequency table (like this app does) and the sentiment analyzer for real insight.