Update app.py

app.py

@@ -10,21 +10,28 @@ from sklearn.tree import DecisionTreeClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.svm import SVC
 from sklearn.metrics import accuracy_score
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
+import threading
 
+# Read dataset
 df = pd.read_csv(r"spam.csv")
 
-
+# Initialize Streamlit app
 st.title("Identifying Spam and Ham Emails")
 
+# Define feature and target variables
 x = df["Message"]
 y = df["Category"]
 
+# Create a Bag of Words (BoW) model
 bow = CountVectorizer(stop_words="english")
 final_data = pd.DataFrame(bow.fit_transform(x).toarray(), columns=bow.get_feature_names_out())
 
+# Train-test split
 x_train, x_test, y_train, y_test = train_test_split(final_data, y, test_size=0.2, random_state=20)
 
-
+# Initialize models
 models = {
     "Naive Bayes": MultinomialNB(),
     "KNN": KNeighborsClassifier(),
@@ -33,26 +40,36 @@ models = {
     "SVM": SVC()
 }
 
+# Model selection
 model_choice = st.selectbox("Choose a Classification Algorithm", list(models.keys()))
 
-
+# Train the selected model
 obj = models[model_choice]
 obj.fit(x_train, y_train)
 y_pred = obj.predict(x_test)
 accuracy = accuracy_score(y_test, y_pred)
 
-
+# Display accuracy
 if st.button("Show Accuracy"):
     st.write(f"Accuracy of {model_choice}: {accuracy:.4f}")
 
+# Email input and prediction function
 email_input = st.text_input("Enter an Email for Prediction")
 
-
 def predict_email(email):
     data = bow.transform([email]).toarray() 
     prediction = obj.predict(data)[0]
     st.write(f"Prediction: {prediction}")
 
+if st.button("Predict Email"):
+    if email_input:
+        predict_email(email_input)
+    else:
+        st.write(":red[Please enter an email to classify]")
+
+# FastAPI app to handle GET requests
+app = FastAPI()
+
 @app.get("/predict/")
 def predict_spam(email: str):
     """
@@ -61,11 +78,16 @@ def predict_spam(email: str):
     """
     data = bow.transform([email]).toarray()
     prediction = obj.predict(data)[0]
-    return JSONResponse(content={"prediction": prediction})    
+    return JSONResponse(content={"prediction": prediction})
 
+# Running FastAPI in a separate thread to work alongside Streamlit
+def run_api():
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)
 
-if st.button("Predict Email"):
-    if email_input:
-        predict_email(email_input)
-    else:
-        st.write(":red[Please enter an email to classify]")
+# Start FastAPI in a separate thread
+api_thread = threading.Thread(target=run_api, daemon=True)
+api_thread.start()
+
+# You can also check API response using the link below:
+# http://localhost:8000/predict/?email=Your_email_text_here