Update app.py

app.py

@@ -1,75 +1,75 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-from sklearn.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-from sklearn.impute import SimpleImputer
-from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder
-from xgboost import XGBClassifier
-
-# Define columns
-numeric = ['Age', 'Tumor_Size']
-ordinal = ['Tumor_Grade', 'Symptoms_Severity', 'Alcohol_Consumption', 'Exercise_Frequency']
-nominal = ['Gender', 'Family_History', 'Smoking_History']
-
-# Define preprocessing pipelines
-numeric_preprocess = Pipeline([
-    ('Mean Imputation', SimpleImputer(strategy='mean')),
-    ('Scaling', StandardScaler())
-])
-
-ordinal_preprocess = Pipeline([
-    ('Mode Imputation', SimpleImputer(strategy='most_frequent')),
-    ('Encoding', OrdinalEncoder())
-])
-
-nominal_preprocess = Pipeline([
-    ('Mode Imputation', SimpleImputer(strategy='most_frequent')),
-    ('Encoding', OneHotEncoder(sparse_output=False))
-])
-
-preprocess = ColumnTransformer([
-    ('Numerical Transformer', numeric_preprocess, numeric),
-    ('Ordinal Transformer', ordinal_preprocess, ordinal),
-    ('Nominal Transformer', nominal_preprocess, nominal)
-], remainder='passthrough')
-
-# Load dataset and train model
-df = pd.read_csv(r"C:\Users\HP\Downloads\cancer_prediction_data (2).csv")
-x = df.drop('Cancer_Present', axis=1)
-y = df['Cancer_Present']
-xgb = Pipeline([
-    ('Data Preprocessing', preprocess),
-    ('Algorithm', XGBClassifier())
-])
-xgb.fit(x, y)
-
-# Streamlit UI
-st.set_page_config(page_title="Cancer Prediction App", page_icon="🩺", layout="centered")
-st.title("🔬 Cancer Prediction App")
-st.write("This application predicts the likelihood of having cancer based on various health parameters.")
-
-# User input fields
-age = st.number_input("📅 Age", min_value=1, max_value=120, value=30)
-gender = st.selectbox("⚧️ Gender", ["Male", "Female"])
-tumor_size = st.number_input("📏 Tumor Size (0-10)", min_value=0.0, max_value=10.0, value=5.0)
-tumor_grade = st.selectbox("🔢 Tumor Grade", ["Low", "Medium", "High"])
-symptoms_severity = st.selectbox("🤕 Symptoms Severity", ["Mild", "Moderate", "Severe"])
-family_history = st.selectbox("👨‍👩‍👧 Family History", ["Yes", "No"])
-smoking_history = st.selectbox("🚬 Smoking History", ["Non-Smoker", "Former Smoker", "Current Smoker"])
-alcohol_consumption = st.selectbox("🍷 Alcohol Consumption", ["Low", "Moderate", "High"])
-exercise_frequency = st.selectbox("🏋️ Exercise Frequency", ["Never", "Occasionally", "Rarely", "Regularly"])
-
-# Predict button
-if st.button("🔍 Predict Cancer"):
-    input_data = pd.DataFrame([[age, gender, tumor_size, tumor_grade, symptoms_severity,
-                                family_history, smoking_history, alcohol_consumption, exercise_frequency]],
-                              columns=x.columns)
-    prediction = xgb.predict(input_data)[0]
-    
-    if prediction == 0:
-        st.success("✅ Not a Cancer")
-    else:
-        st.error("⚠️ Cancer Detected! Please consult a doctor.")
-
-st.write("🔍 This model is trained using XGBoost and provides predictions based on health data.")
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder
+from xgboost import XGBClassifier
+
+# Define columns
+numeric = ['Age', 'Tumor_Size']
+ordinal = ['Tumor_Grade', 'Symptoms_Severity', 'Alcohol_Consumption', 'Exercise_Frequency']
+nominal = ['Gender', 'Family_History', 'Smoking_History']
+
+# Define preprocessing pipelines
+numeric_preprocess = Pipeline([
+    ('Mean Imputation', SimpleImputer(strategy='mean')),
+    ('Scaling', StandardScaler())
+])
+
+ordinal_preprocess = Pipeline([
+    ('Mode Imputation', SimpleImputer(strategy='most_frequent')),
+    ('Encoding', OrdinalEncoder())
+])
+
+nominal_preprocess = Pipeline([
+    ('Mode Imputation', SimpleImputer(strategy='most_frequent')),
+    ('Encoding', OneHotEncoder(sparse_output=False))
+])
+
+preprocess = ColumnTransformer([
+    ('Numerical Transformer', numeric_preprocess, numeric),
+    ('Ordinal Transformer', ordinal_preprocess, ordinal),
+    ('Nominal Transformer', nominal_preprocess, nominal)
+], remainder='passthrough')
+
+# Load dataset and train model
+df = pd.read_csv("cancer_prediction_data (2).csv")
+x = df.drop('Cancer_Present', axis=1)
+y = df['Cancer_Present']
+xgb = Pipeline([
+    ('Data Preprocessing', preprocess),
+    ('Algorithm', XGBClassifier())
+])
+xgb.fit(x, y)
+
+# Streamlit UI
+st.set_page_config(page_title="Cancer Prediction App", page_icon="🩺", layout="centered")
+st.title("🔬 Cancer Prediction App")
+st.write("This application predicts the likelihood of having cancer based on various health parameters.")
+
+# User input fields
+age = st.number_input("📅 Age", min_value=1, max_value=120, value=30)
+gender = st.selectbox("⚧️ Gender", ["Male", "Female"])
+tumor_size = st.number_input("📏 Tumor Size (0-10)", min_value=0.0, max_value=10.0, value=5.0)
+tumor_grade = st.selectbox("🔢 Tumor Grade", ["Low", "Medium", "High"])
+symptoms_severity = st.selectbox("🤕 Symptoms Severity", ["Mild", "Moderate", "Severe"])
+family_history = st.selectbox("👨‍👩‍👧 Family History", ["Yes", "No"])
+smoking_history = st.selectbox("🚬 Smoking History", ["Non-Smoker", "Former Smoker", "Current Smoker"])
+alcohol_consumption = st.selectbox("🍷 Alcohol Consumption", ["Low", "Moderate", "High"])
+exercise_frequency = st.selectbox("🏋️ Exercise Frequency", ["Never", "Occasionally", "Rarely", "Regularly"])
+
+# Predict button
+if st.button("🔍 Predict Cancer"):
+    input_data = pd.DataFrame([[age, gender, tumor_size, tumor_grade, symptoms_severity,
+                                family_history, smoking_history, alcohol_consumption, exercise_frequency]],
+                              columns=x.columns)
+    prediction = xgb.predict(input_data)[0]
+    
+    if prediction == 0:
+        st.success("✅ Not a Cancer")
+    else:
+        st.error("⚠️ Cancer Detected! Please consult a doctor.")
+
+st.write("🔍 This model is trained using XGBoost and provides predictions based on health data.")