Update app.py

app.py

@@ -1,108 +1,110 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder
-from sklearn.impute import SimpleImputer
-from sklearn.compose import ColumnTransformer
-from sklearn.model_selection import train_test_split
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.svm import SVC
-from sklearn.linear_model import LogisticRegression
-from sklearn.neighbors import KNeighborsClassifier
-
-# Load dataset
-def load_data():
-    df = pd.read_csv('cancer_prediction_data (2).csv')
-    return df
-
-# Data Preprocessing
-def preprocess_data(df):
-    numeric = ['Age', 'Tumor_Size']
-    ordinal = ['Tumor_Grade', 'Symptoms_Severity', 'Alcohol_Consumption', 'Exercise_Frequency']
-    nominal = ['Gender', 'Family_History', 'Smoking_History']
-    
-    # Pipelines
-    numeric_preprocess = Pipeline([
-        ('imputer', SimpleImputer(strategy='mean')),
-        ('scaler', StandardScaler())
-    ])
-    ordinal_preprocess = Pipeline([
-        ('imputer', SimpleImputer(strategy='most_frequent')),
-        ('encoder', OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1))
-    ])
-    nominal_preprocess = Pipeline([
-        ('imputer', SimpleImputer(strategy='most_frequent')),
-        ('encoder', OneHotEncoder(sparse_output=False, handle_unknown='ignore'))
-    ])
-    
-    # Column Transformer
-    preprocess = ColumnTransformer([
-        ('num', numeric_preprocess, numeric),
-        ('ord', ordinal_preprocess, ordinal),
-        ('nom', nominal_preprocess, nominal)
-    ], remainder='passthrough')
-    
-    X = df.drop('Cancer_Present', axis=1)
-    y = df['Cancer_Present']
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=23)
-    return X_train, X_test, y_train, y_test, preprocess
-
-# Train Models
-def train_model(X_train, y_train, preprocess, model_name):
-    models = {
-        'Decision Tree': DecisionTreeClassifier(),
-        'SVM': SVC(),
-        'Logistic Regression': LogisticRegression(),
-        'KNN': KNeighborsClassifier()
-    }
-    
-    model = models[model_name]
-    pipeline = Pipeline([
-        ('preprocessor', preprocess),
-        ('classifier', model)
-    ])
-    pipeline.fit(X_train, y_train)
-    return pipeline
-
-# Streamlit UI
-st.title("Cancer Prediction Using Machine Learning")
-df = load_data()
-X_train, X_test, y_train, y_test, preprocess = preprocess_data(df)
-
-model_name = st.selectbox("Select Model", ['Decision Tree', 'SVM', 'Logistic Regression', 'KNN'])
-
-if st.button("Train Model"):
-    model = train_model(X_train, y_train, preprocess, model_name)
-    accuracy = model.score(X_test, y_test)
-    st.write(f"Model Accuracy: {accuracy:.2f}")
-    st.session_state['trained_model'] = model
-    st.success("Model trained successfully!")
-
-# Prediction Section
-st.header("Make a Prediction")
-age = st.number_input("Age", min_value=18, max_value=100, value=30)
-tumor_size = st.number_input("Tumor Size", min_value=1.0, max_value=10.0, value=5.0)
-tumor_grade = st.selectbox("Tumor Grade", [1, 2, 3])
-symptoms_severity = st.selectbox("Symptoms Severity", [1, 2, 3])
-smoking_history = st.selectbox("Smoking History", [0, 1, 2])
-alcohol_consumption = st.selectbox("Alcohol Consumption", [0, 1, 2, 3])
-exercise_frequency = st.selectbox("Exercise Frequency", [0, 1, 2, 3])
-gender = st.selectbox("Gender", [0, 1])
-family_history = st.selectbox("Family History", [0, 1])
-
-input_data = [[age, tumor_size, tumor_grade, symptoms_severity, smoking_history, 
-               alcohol_consumption, exercise_frequency, gender, family_history]]
-
-if st.button("Predict Cancer Presence"):
-    if 'trained_model' in st.session_state:
-        model = st.session_state['trained_model']
-        input_df = pd.DataFrame(input_data, columns=X_train.columns)
-        
-        # Transform input data using the same preprocessor
-        input_transformed = model.named_steps['preprocessor'].transform(input_df)
-        prediction = model.named_steps['classifier'].predict(input_transformed)
-
-        st.write("Cancer Prediction:", "Positive" if prediction[0] == 1 else "Negative")
-    else:
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder
+from sklearn.impute import SimpleImputer
+from sklearn.compose import ColumnTransformer
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.neighbors import KNeighborsClassifier
+
+# Load dataset
+def load_data():
+    df = pd.read_csv('cancer_prediction_data (2).csv')
+    return df
+
+# Data Preprocessing
+def preprocess_data(df):
+    numeric = ['Age', 'Tumor_Size']
+    ordinal = ['Tumor_Grade', 'Symptoms_Severity', 'Alcohol_Consumption', 'Exercise_Frequency']
+    nominal = ['Gender', 'Family_History', 'Smoking_History']
+    
+    # Pipelines
+    numeric_preprocess = Pipeline([
+        ('imputer', SimpleImputer(strategy='mean')),
+        ('scaler', StandardScaler())
+    ])
+    ordinal_preprocess = Pipeline([
+        ('imputer', SimpleImputer(strategy='most_frequent')),
+        ('encoder', OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1))
+    ])
+    nominal_preprocess = Pipeline([
+        ('imputer', SimpleImputer(strategy='most_frequent')),
+        ('encoder', OneHotEncoder(sparse_output=False, handle_unknown='ignore'))
+    ])
+    
+    # Column Transformer
+    preprocess = ColumnTransformer([
+        ('num', numeric_preprocess, numeric),
+        ('ord', ordinal_preprocess, ordinal),
+        ('nom', nominal_preprocess, nominal)
+    ], remainder='passthrough')
+    
+    X = df.drop('Cancer_Present', axis=1)
+    y = df['Cancer_Present']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=23)
+    return X_train, X_test, y_train, y_test, preprocess
+
+# Train Models
+def train_model(X_train, y_train, preprocess, model_name):
+    models = {
+        'Decision Tree': DecisionTreeClassifier(),
+        'SVM': SVC(),
+        'Logistic Regression': LogisticRegression(),
+        'KNN': KNeighborsClassifier()
+    }
+    
+    model = models[model_name]
+    pipeline = Pipeline([
+        ('preprocessor', preprocess),
+        ('classifier', model)
+    ])
+    pipeline.fit(X_train, y_train)
+    return pipeline
+
+# Streamlit UI
+st.markdown("<h1 style='text-align: center; color: #1976D2;'>🎗️ Cancer Prediction Using ML 🎗️</h1>", unsafe_allow_html=True)
+
+df = load_data()
+X_train, X_test, y_train, y_test, preprocess = preprocess_data(df)
+
+model_name = st.selectbox("Select Model", ['Decision Tree', 'SVM', 'Logistic Regression', 'KNN'])
+
+if st.button("Train Model"):
+    model = train_model(X_train, y_train, preprocess, model_name)
+    accuracy = model.score(X_test, y_test)
+    st.write(f"Model Accuracy: {accuracy:.2f}")
+    st.session_state['trained_model'] = model
+    st.success("Model trained successfully!")
+
+# Prediction Section
+st.markdown("<h2 style='color: #D32F2F;'>🔍 Make a Prediction</h2>", unsafe_allow_html=True)
+
+age = st.number_input("Age", min_value=18, max_value=100, value=30)
+tumor_size = st.number_input("Tumor Size", min_value=1.0, max_value=10.0, value=5.0)
+tumor_grade = st.selectbox("Tumor Grade", [1, 2, 3])
+symptoms_severity = st.selectbox("Symptoms Severity", [1, 2, 3])
+smoking_history = st.selectbox("Smoking History", [0, 1, 2])
+alcohol_consumption = st.selectbox("Alcohol Consumption", [0, 1, 2, 3])
+exercise_frequency = st.selectbox("Exercise Frequency", [0, 1, 2, 3])
+gender = st.selectbox("Gender", [0, 1])
+family_history = st.selectbox("Family History", [0, 1])
+
+input_data = [[age, tumor_size, tumor_grade, symptoms_severity, smoking_history, 
+               alcohol_consumption, exercise_frequency, gender, family_history]]
+
+if st.button("Predict Cancer Presence"):
+    if 'trained_model' in st.session_state:
+        model = st.session_state['trained_model']
+        input_df = pd.DataFrame(input_data, columns=X_train.columns)
+        
+        # Transform input data using the same preprocessor
+        input_transformed = model.named_steps['preprocessor'].transform(input_df)
+        prediction = model.named_steps['classifier'].predict(input_transformed)
+
+        st.write("Cancer Prediction:", "Positive" if prediction[0] == 1 else "Negative")
+    else:
         st.error("Please train a model first!")