Create app.py

app.py

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+import re
+import string
+import gradio as gr
+
+import nltk
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+
+# Ensure NLTK resources are available at runtime
+nltk.download("punkt", quiet=True)
+nltk.download("stopwords", quiet=True)
+nltk.download("wordnet", quiet=True)
+nltk.download("omw-1.4", quiet=True)
+
+# ---------- Normalization helpers ----------
+_wordnet_lemmatizer = WordNetLemmatizer()
+_stop_words = set(stopwords.words("english"))
+_punct_table = str.maketrans("", "", string.punctuation)
+
+def word_tokenize(text: str):
+    # Simple word tokenizer that keeps apostrophes inside words
+    return nltk.word_tokenize(text)
+
+def remove_non_ascii(words):
+    return [w.encode("ascii", "ignore").decode("ascii") for w in words]
+
+def to_lowercase(words):
+    return [w.lower() for w in words]
+
+def remove_punctuation(words):
+    return [w.translate(_punct_table) for w in words if w.translate(_punct_table) != ""]
+
+def remove_stopwords(words):
+    return [w for w in words if w not in _stop_words]
+
+def lemmatize_list(words):
+    # Lemmatize as nouns first, then verbs if noun same as original
+    out = []
+    for w in words:
+        n = _wordnet_lemmatizer.lemmatize(w, pos="n")
+        v = _wordnet_lemmatizer.lemmatize(n, pos="v")
+        out.append(v)
+    return out
+
+def normalize_pipeline(text: str):
+    """
+    Runs the full preprocessing pipeline while returning step-by-step outputs.
+    Returns a dict mapping step name to value (list of tokens or final string).
+    """
+    steps = {}
+    steps["original"] = text
+
+    tokens = word_tokenize(text)
+    steps["1) tokenize"] = tokens
+
+    words = remove_non_ascii(tokens)
+    steps["2) remove_non_ascii"] = words
+
+    words = to_lowercase(words)
+    steps["3) to_lowercase"] = words
+
+    words = remove_punctuation(words)
+    steps["4) remove_punctuation"] = words
+
+    words = remove_stopwords(words)
+    steps["5) remove_stopwords"] = words
+
+    words = lemmatize_list(words)
+    steps["6) lemmatize"] = words
+
+    final_text = " ".join(words)
+    steps["7) join"] = final_text
+    return steps, final_text
+
+# ---------- Gradio UI ----------
+EXAMPLES = [
+    "Habitat's 20 by 28 campaign is inspiring—let's build more homes in Jackson!",
+    "NLTK makes text preprocessing EASY: Tokenize, lowercase, remove punctuation & stopwords, then lemmatize.",
+    "Cats were running, jumped over fences; the dogs' tails were wagging! 🐶🐱",
+    "Email me at Example@Domain.com!!! This, perhaps, isn't AS easy as it looks...",
+]
+
+def run_pipeline(text):
+    steps, final_text = normalize_pipeline(text)
+    # Provide a human-friendly multiline trace
+    trace_lines = []
+    for k, v in steps.items():
+        if isinstance(v, list):
+            display = ", ".join(v[:40]) + (" ..." if len(v) > 40 else "")
+        else:
+            display = v
+        trace_lines.append(f"{k}:\n{display}\n")
+    trace = "\n".join(trace_lines)
+    return steps, final_text, trace
+
+with gr.Blocks(title="Text Normalization Demo") as demo:
+    gr.Markdown("# Text Normalization (Step-by-Step)")
+    gr.Markdown(
+        "Enter text on the left or choose an example. The pipeline shows each step: "
+        "**tokenize → remove_non_ascii → lowercase → remove_punctuation → remove_stopwords → lemmatize → join**."
+    )
+
+    with gr.Row():
+        with gr.Column():
+            text_in = gr.Textbox(label="Input text", lines=6, placeholder="Type or pick an example below...")
+            examples = gr.Examples(
+                examples=[[e] for e in EXAMPLES],
+                inputs=[text_in],
+                label="Try these examples",
+            )
+            run_btn = gr.Button("Run normalization")
+        with gr.Column():
+            final_out = gr.Textbox(label="Final normalized text", lines=2)
+            trace_out = gr.Code(label="Step-by-step trace (human readable)", language="markdown")
+
+    # Collapsible JSON view for each step (for clarity & grading)
+    steps_json = gr.JSON(label="Detailed steps (JSON)")
+
+    run_btn.click(fn=run_pipeline, inputs=text_in, outputs=[steps_json, final_out, trace_out])
+
+if __name__ == "__main__":
+    demo.launch()