Update app.py

app.py

@@ -1,65 +1,65 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import sklearn
-from sklearn.model_selection import train_test_split
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.linear_model import LogisticRegression
-from sklearn.svm import SVC
-from sklearn.metrics import accuracy_score
-
-# Load Dataset
-df = pd.read_csv("spam.csv")
-
-# Title
-st.title(":blue[Spam and Ham Detection]")
-
-# Preparing Data
-x = df["Message"]
-y = df["Category"]
-
-bow = CountVectorizer(stop_words="english")
-final_data = pd.DataFrame(bow.fit_transform(x).toarray(), columns=bow.get_feature_names_out())
-
-x_train, x_test, y_train, y_test = train_test_split(final_data, y, test_size=0.2, random_state=20)
-
-# Available Models
-models = {
-    "Naive Bayes": MultinomialNB(),
-    "KNN": KNeighborsClassifier(),
-    "Decision Tree": DecisionTreeClassifier(),
-    "Logistic Regression": LogisticRegression(),
-    "SVM": SVC()
-}
-
-# Select Model
-model_choice = st.radio("Choose a Classification Algorithm", list(models.keys()))
-
-# Train and Evaluate Model
-obj = models[model_choice]
-obj.fit(x_train, y_train)
-y_pred = obj.predict(x_test)
-accuracy = accuracy_score(y_test, y_pred)*100
-
-# Show Accuracy when button is clicked
-if st.button("Show Accuracy"):
-    st.write(f"Accuracy of {model_choice}: {accuracy:.4f}")
-
-# Input Field for Email
-email_input = st.text_input("enter email")
-
-# Prediction Function
-def predict_email(email):
-    data = bow.transform([email]).toarray()  # Convert sparse to dense
-    prediction = obj.predict(data)[0]
-    st.write(f"Prediction: {prediction}")
-
-# Predict Button
-if st.button("Predict Email"):
-    if email_input:
-        predict_email(email_input)
-    else:
+import streamlit as st
+import pandas as pd
+import numpy as np
+import sklearn
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score
+
+# Load Dataset
+df = pd.read_csv("spam.csv")
+
+# Title
+st.title(":blue[Spam and Ham Detection]")
+
+# Preparing Data
+x = df["Message"]
+y = df["Category"]
+
+bow = CountVectorizer(stop_words="english")
+final_data = pd.DataFrame(bow.fit_transform(x).toarray(), columns=bow.get_feature_names_out())
+
+x_train, x_test, y_train, y_test = train_test_split(final_data, y, test_size=0.2, random_state=20)
+
+# Available Models
+models = {
+    "Naive Bayes": MultinomialNB(),
+    "KNN": KNeighborsClassifier(),
+    "Decision Tree": DecisionTreeClassifier(),
+    "Logistic Regression": LogisticRegression(),
+    "SVM": SVC()
+}
+
+# Select Model
+model_choice = st.radio("Choose a Classification Algorithm", list(models.keys()))
+
+# Train and Evaluate Model
+obj = models[model_choice]
+obj.fit(x_train, y_train)
+y_pred = obj.predict(x_test)
+accuracy = accuracy_score(y_test, y_pred)*100
+
+# Show Accuracy when button is clicked
+if st.button("Show Accuracy"):
+    st.write(f"Accuracy of {model_choice}: {accuracy:.4f}")
+
+# Input Field for Email
+email_input = st.text_input("enter email")
+
+# Prediction Function
+def predict_email(email):
+    data = bow.transform([email]).toarray()  # Convert sparse to dense
+    prediction = obj.predict(data)[0]
+    st.write(f"Prediction: {prediction}")
+
+# Predict Button
+if st.button("Predict Email"):
+    if email_input:
+        predict_email(email_input)
+    else:
         st.write(":red[enter mail]")