Update pages/spam_ham_Detection.py

pages/spam_ham_Detection.py

@@ -1,59 +1,59 @@
-import pandas as pd
-import numpy as np
-import streamlit as st
-import seaborn as sns
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.model_selection import train_test_split
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
-from sklearn.svm import SVC
-from sklearn.metrics import accuracy_score
-
-st.markdown(
-    "<h1 style='text-align: center; color: #FF4B4B;'>📧 Email Spam or Ham Classification</h1>", 
-    unsafe_allow_html=True
-)
-
-df = pd.read_csv(r'E:\innomatics\ML\Running Notes\Text Data\spam.csv')
-
-# Text Vectorization
-bow = CountVectorizer(stop_words='english')
-x = df['Message']
-y = df['Category']
-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=0.25, random_state=20)
-
-# Model Selection
-model_choice = st.radio("Select a model to evaluate:", ['KNN', 'Naive Bayes', 'Decision Tree', 'Random Forest', 'SVM'])
-
-if model_choice == 'KNN':
-    model = KNeighborsClassifier()
-elif model_choice == 'Naive Bayes':
-    model = MultinomialNB()
-elif model_choice == 'Decision Tree':
-    model = DecisionTreeClassifier()
-elif model_choice == 'Random Forest':
-    model = RandomForestClassifier()
-elif model_choice == 'SVM':
-    model = SVC()
-
-# Train Model
-model.fit(x_train, y_train)
-y_pred = model.predict(x_test)
-accuracy = accuracy_score(y_test, y_pred)
-
-# Display Accuracy
-if st.button("📊 Show Model Accuracy"):
-    st.write(f"### {model_choice} Model Accuracy: {accuracy:.2f}")
-
-# Text Input for Prediction
-user_input = st.text_area("✍️ Enter text for prediction:")
-
-if st.button("🔍 Predict"):
-    data = bow.transform([user_input]).toarray()
-    prediction = model.predict(data)[0]
-    st.success(f"🔍 Prediction: {prediction}")
+import pandas as pd
+import numpy as np
+import streamlit as st
+import seaborn as sns
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score
+
+st.markdown(
+    "<h1 style='text-align: center; color: #FF4B4B;'>📧 Email Spam or Ham Classification</h1>", 
+    unsafe_allow_html=True
+)
+
+df = pd.read_csv('spam.csv')
+
+# Text Vectorization
+bow = CountVectorizer(stop_words='english')
+x = df['Message']
+y = df['Category']
+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=0.25, random_state=20)
+
+# Model Selection
+model_choice = st.radio("Select a model to evaluate:", ['KNN', 'Naive Bayes', 'Decision Tree', 'Random Forest', 'SVM'])
+
+if model_choice == 'KNN':
+    model = KNeighborsClassifier()
+elif model_choice == 'Naive Bayes':
+    model = MultinomialNB()
+elif model_choice == 'Decision Tree':
+    model = DecisionTreeClassifier()
+elif model_choice == 'Random Forest':
+    model = RandomForestClassifier()
+elif model_choice == 'SVM':
+    model = SVC()
+
+# Train Model
+model.fit(x_train, y_train)
+y_pred = model.predict(x_test)
+accuracy = accuracy_score(y_test, y_pred)
+
+# Display Accuracy
+if st.button("📊 Show Model Accuracy"):
+    st.write(f"### {model_choice} Model Accuracy: {accuracy:.2f}")
+
+# Text Input for Prediction
+user_input = st.text_area("✍️ Enter text for prediction:")
+
+if st.button("🔍 Predict"):
+    data = bow.transform([user_input]).toarray()
+    prediction = model.predict(data)[0]
+    st.success(f"🔍 Prediction: {prediction}")