Upload app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+
+
+model = joblib.load("model.joblib")
+tfidf_vectorizer = joblib.load("tfidf_vectorizer.joblib")
+
+
+class TextInput(BaseModel):
+    text: str
+
+
+app = FastAPI()
+
+
+@app.post("/predict")
+def predict(input: TextInput):
+   
+    processed_text = preprocess_text(input.text)
+    
+    
+    text_tfidf = tfidf_vectorizer.transform([processed_text])
+   
+    prediction = model.predict(text_tfidf)
+    
+    return {"prediction": "Spam" if int(prediction[0]) == 0 else "Ham"}
+
+def preprocess_text(text):
+    import re
+    from nltk.stem import WordNetLemmatizer
+    from nltk.corpus import stopwords
+    lemmatizer = WordNetLemmatizer()
+    stop_words = set(stopwords.words('english'))
+    
+    text = re.sub('[^a-zA-Z]', ' ', text)
+    text = text.lower()
+    words = text.split()
+    words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]
+    return ' '.join(words)