Delete train_spam_model.py

train_spam_model.py

@@ -1,66 +0,0 @@
-# -----------------------------
-# app.py pour déploiement Spam Detector
-# -----------------------------
-import gradio as gr
-import joblib
-import re
-from nltk.corpus import stopwords
-from nltk.stem import PorterStemmer
-import nltk
-nltk.download('stopwords')
-
-# -----------------------------
-# 1️⃣ Prétraitement d'un message
-# -----------------------------
-stop_words = set(stopwords.words('english'))
-stemmer = PorterStemmer()
-
-def preprocess_message(text):
-    """Prétraite un message pour qu'il corresponde au format du modèle."""
-    if not text:
-        return ""
-    text = text.lower()
-    text = re.sub(r'http\S+|www\S+', '', text)
-    text = re.sub(r'\S+@\S+', '', text)
-    text = re.sub(r'\+?\d[\d -]{8,}\d', '', text)
-    text = re.sub(r'\d+', '', text)
-    text = re.sub(r'[^a-z\s!/+>]', '', text)  # garder ponctuation utile pour spam
-    words = [stemmer.stem(word) for word in text.split() if word not in stop_words]
-    return " ".join(words)
-
-# -----------------------------
-# 2️⃣ Charger modèle et TF-IDF
-# -----------------------------
-model = joblib.load("spam_model.pkl")
-vectorizer = joblib.load("tfidf_vectorizer.pkl")
-
-# -----------------------------
-# 3️⃣ Fonction de prédiction
-# -----------------------------
-def predict_message(message):
-    cleaned = preprocess_message(message)
-    X = vectorizer.transform([cleaned])
-    prediction = model.predict(X)[0]
-    probability = model.predict_proba(X)[0][1] if hasattr(model, 'predict_proba') else None
-    return {
-        "Message": message,
-        "Prediction": prediction,
-        "Spam Probability": round(float(probability), 4) if probability is not None else None
-    }
-
-# -----------------------------
-# 4️⃣ Interface Gradio
-# -----------------------------
-iface = gr.Interface(
-    fn=predict_message,
-    inputs=gr.Textbox(lines=3, placeholder="Entrez votre message..."),
-    outputs="json",
-    title="📩 Spam Detector",
-    description="Entrez un message pour savoir s'il s'agit de spam ou non."
-)
-
-# -----------------------------
-# 5️⃣ Lancer l'application
-# -----------------------------
-if __name__ == "__main__":
-    iface.launch()