Create app.py

app.py

@@ -0,0 +1,38 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import nltk
+from nltk.corpus import stopwords
+from nltk.stem import PorterStemmer
+import re
+
+nltk.download('stopwords')
+
+app = FastAPI()
+
+# Load the model pipeline
+pipeline = joblib.load('spam_classifier_pipeline.joblib')
+
+class EmailRequest(BaseModel):
+    subject: str
+    body: str
+
+def preprocess_text(text):
+    text = text.lower()
+    text = re.sub(r'[^a-zA-Z\s]', '', text)
+    words = text.split()
+    stop_words = set(stopwords.words('english'))
+    words = [word for word in words if word not in stop_words]
+    stemmer = PorterStemmer()
+    words = [stemmer.stem(word) for word in words]
+    return ' '.join(words)
+
+@app.post("/predict")
+async def predict(email: EmailRequest):
+    processed_text = preprocess_text(email.subject + ' ' + email.body)
+    prediction = pipeline.predict([processed_text])[0]
+    return {'prediction': ['ham', 'not_spam', 'spam'][prediction]}
+
+@app.get("/")
+async def root():
+    return {"message": "Spam Classification API"}