Delete app.py

app.py

@@ -1,259 +0,0 @@
-import gradio as gr
-import pandas as pd
-from faker import Faker
-import os
-import re
-
-# ── Example CSV (created at startup, before Blocks definition) ────────────────
-def _create_example():
-    if os.path.exists("example.csv"):
-        return
-    sample = pd.DataFrame({
-        "id":         range(1, 6),
-        "first_name": ["Mario", "Lucia", "Giovanni", "Sofia", "Marco"],
-        "last_name":  ["Rossi", "Bianchi", "Ferrari", "Esposito", "Romano"],
-        "email":      ["mario.rossi@gmail.com", "lucia.b@yahoo.it",
-                       "g.ferrari@outlook.com", "sofia.e@libero.it",
-                       "m.romano@hotmail.com"],
-        "phone":      ["+39 333 1234567", "02 9876543", "+39 347 9988776",
-                       "080 5551234", "+39 320 4567890"],
-        "city":       ["Roma", "Milano", "Napoli", "Bari", "Torino"],
-        "notes":      ["premium customer", "newsletter yes", "B2B",
-                       "trial", "enterprise"],
-    })
-    sample.to_csv("example.csv", index=False)
-
-_create_example()
-
-# ── Available anonymization types ─────────────────────────────────────────────
-ANON_TYPES = {
-    "email":          "📧 Email",
-    "first_name":     "👤 First Name",
-    "last_name":      "👤 Last Name",
-    "full_name":      "👤 Full Name",
-    "phone":          "📱 Phone",
-    "address":        "🏠 Address",
-    "city":           "🏙️ City",
-    "postal_code":    "📮 Postal Code",
-    "tax_id":         "🪪 Tax ID",
-    "date_of_birth":  "📅 Date of Birth",
-    "generic":        "🔒 Generic",
-}
-TYPE_LABELS = {v: k for k, v in ANON_TYPES.items()}
-TYPE_CHOICES = list(ANON_TYPES.values())
-
-# ── Automatic type detection ──────────────────────────────────────────────────
-def _detect_type(series):
-    name = (series.name or "").lower()
-    sample = series.dropna().astype(str).head(200)
-
-    if any(k in name for k in ("email", "mail", "e-mail")):
-        return "email"
-    if any(k in name for k in ("telefon", "phone", "cell", "mobile", "tel")):
-        return "phone"
-    if any(k in name for k in ("full_name", "fullname", "nome_completo")):
-        return "full_name"
-    if any(k in name for k in ("nome", "first", "given")):
-        return "first_name"
-    if any(k in name for k in ("cognome", "surname", "last")):
-        return "last_name"
-    if any(k in name for k in ("indirizzo", "address", "via", "street")):
-        return "address"
-    if any(k in name for k in ("città", "city", "comune", "citta")):
-        return "city"
-    if any(k in name for k in ("cap", "postal", "zip")):
-        return "postal_code"
-    if any(k in name for k in ("cf", "codice_fiscale", "fiscal", "tax")):
-        return "tax_id"
-    if any(k in name for k in ("nascita", "birth", "dob", "birthday")):
-        return "date_of_birth"
-
-    if len(sample) > 0:
-        if sample.str.match(r"^[A-Za-z0-9._%+\-]+@[A-Za-z0-9.\-]+\.[A-Za-z]{2,}$").mean() > 0.5:
-            return "email"
-        if sample.str.match(r"^[\+\d\s\-\(\)]{7,15}$").mean() > 0.5:
-            return "phone"
-
-    return "generic"
-
-# ── Fake-data generators (deterministic per value) ────────────────────────────
-def _make_fake(value, anon_type, cache):
-    if value in cache:
-        return cache[value]
-
-    seed = abs(hash(value)) % (2**32)
-    fake_local = Faker(["it_IT", "en_US"])
-    fake_local.seed_instance(seed)
-
-    generators = {
-        "email":         lambda: fake_local.email(),
-        "first_name":    lambda: fake_local.first_name(),
-        "last_name":     lambda: fake_local.last_name(),
-        "full_name":     lambda: fake_local.name(),
-        "phone":         lambda: fake_local.phone_number(),
-        "address":       lambda: fake_local.street_address(),
-        "city":          lambda: fake_local.city(),
-        "postal_code":   lambda: fake_local.postcode(),
-        "tax_id":        lambda: _fake_tax_id(fake_local),
-        "date_of_birth": lambda: fake_local.date_of_birth(minimum_age=18, maximum_age=80).strftime("%d/%m/%Y"),
-        "generic":       lambda: f"ANONYMIZED_{abs(hash(value)) % 100000:05d}",
-    }
-
-    result = generators.get(anon_type, generators["generic"])()
-    cache[value] = result
-    return result
-
-def _fake_tax_id(f):
-    letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
-    digits = "0123456789"
-    return (
-        "".join(f.random_choices(letters, length=3))
-        + "".join(f.random_choices(letters, length=3))
-        + "".join(f.random_choices(digits, length=2))
-        + f.random_choices(letters, length=1)[0]
-        + "".join(f.random_choices(digits, length=2))
-        + f.random_choices(letters, length=1)[0]
-        + "".join(f.random_choices(digits, length=3))
-        + f.random_choices(letters, length=1)[0]
-    )
-
-# ── Main logic ────────────────────────────────────────────────────────────────
-EMPTY_CONFIG = [["", TYPE_CHOICES[-1], False]]
-
-def reset_state():
-    """Reset state when the user clears the uploaded file."""
-    return None, EMPTY_CONFIG, "Upload a CSV file to get started."
-
-def load_csv(file_path):
-    """Load CSV → return (DataFrame state, column-config list-of-lists, status)."""
-    if not file_path:
-        return None, EMPTY_CONFIG, "Upload a CSV file to get started."
-
-    try:
-        df = pd.read_csv(file_path, encoding="utf-8", on_bad_lines="skip")
-    except UnicodeDecodeError:
-        df = pd.read_csv(file_path, encoding="latin-1", on_bad_lines="skip")
-    except Exception as e:
-        return None, EMPTY_CONFIG, f"❌ Error reading the file: {e}"
-
-    if df.empty or len(df.columns) == 0:
-        return None, EMPTY_CONFIG, "❌ The CSV file looks empty or malformed."
-
-    col_config = [
-        [str(c), ANON_TYPES[_detect_type(df[c])], True]
-        for c in df.columns
-    ]
-    msg = (
-        f"✅ Loaded: **{len(df)} rows × {len(df.columns)} columns**. "
-        f"Review the detected types below and click **Anonymize**."
-    )
-    return df, col_config, msg
-
-
-def anonymize(df, col_config):
-    """Anonymize df according to col_config (a list-of-lists or DataFrame)."""
-    if df is None or not isinstance(df, pd.DataFrame) or df.empty:
-        return pd.DataFrame(), None, "❌ Please upload a CSV first."
-
-    # Normalize col_config to list of [col, type_label, anonymize_bool]
-    if isinstance(col_config, pd.DataFrame):
-        rows = col_config.values.tolist()
-    else:
-        rows = list(col_config) if col_config else []
-
-    if not rows:
-        return pd.DataFrame(), None, "❌ Column configuration is empty."
-
-    result = df.copy()
-    processed = 0
-
-    for row in rows:
-        if len(row) < 3:
-            continue
-        col_name, type_label, do_anon = row[0], row[1], row[2]
-        if not do_anon or not col_name or col_name not in result.columns:
-            continue
-
-        anon_type = TYPE_LABELS.get(type_label, "generic")
-        cache = {}
-        result[col_name] = result[col_name].apply(
-            lambda v: _make_fake(str(v), anon_type, cache)
-            if pd.notna(v) and str(v).strip() != "" else v
-        )
-        processed += 1
-
-    csv_path = "/tmp/anonymized.csv"
-    result.to_csv(csv_path, index=False, encoding="utf-8")
-
-    msg = f"✅ Anonymized **{processed} columns** out of {len(df.columns)} total. Download below."
-    return result.head(10), csv_path, msg
-
-# ── UI ────────────────────────────────────────────────────────────────────────
-DESCRIPTION = """
-# 🔒 CSV Data Anonymizer — GDPR Ready
-
-Upload a CSV, review the columns detected as sensitive, and download the anonymized version.
-The mapping is **deterministic**: the same value always produces the same fake data, preserving dataset consistency.
-"""
-
-with gr.Blocks(theme=gr.themes.Soft(), title="CSV Data Anonymizer") as demo:
-    df_state = gr.State()
-
-    gr.Markdown(DESCRIPTION)
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            file_input = gr.File(
-                label="📂 Upload CSV",
-                file_types=[".csv"],
-                type="filepath",
-            )
-            status_box = gr.Markdown("Upload a CSV file to get started.")
-
-        with gr.Column(scale=2):
-            col_editor = gr.Dataframe(
-                label="⚙️ Column configuration",
-                headers=["Column", "Type", "Anonymize"],
-                datatype=["str", "str", "bool"],
-                value=EMPTY_CONFIG,
-                col_count=(3, "fixed"),
-                interactive=True,
-                wrap=True,
-            )
-
-    anon_btn = gr.Button("🔒 Anonymize", variant="primary", size="lg")
-
-    gr.Markdown("### Result preview (first 10 rows)")
-    preview_out = gr.Dataframe(label="Anonymized preview", interactive=False, wrap=True)
-
-    download_out = gr.File(label="⬇️ Download anonymized CSV")
-    result_status = gr.Markdown("")
-
-    file_input.upload(
-        fn=load_csv,
-        inputs=[file_input],
-        outputs=[df_state, col_editor, status_box],
-        api_name="load_csv",
-    )
-
-    file_input.clear(
-        fn=reset_state,
-        inputs=[],
-        outputs=[df_state, col_editor, status_box],
-        api_name="clear",
-    )
-
-    anon_btn.click(
-        fn=anonymize,
-        inputs=[df_state, col_editor],
-        outputs=[preview_out, download_out, result_status],
-        api_name="anonymize",
-    )
-
-    gr.Markdown(
-        "---\n*The file is processed entirely in memory — "
-        "no data is saved to disk or transmitted to third parties.*"
-    )
-
-if __name__ == "__main__":
-    demo.launch()