Delete app.py

app.py

@@ -1,109 +0,0 @@
-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.linear_model import LogisticRegression
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.ensemble import RandomForestClassifier
-from xgboost import XGBClassifier
-
-# Load dataset
-def load_data():
-    return pd.read_csv('cancer_prediction_data (2).csv')
-
-# 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']
-
-    preprocess = ColumnTransformer([
-        ('num', Pipeline([
-            ('imputer', SimpleImputer(strategy='mean')),
-            ('scaler', StandardScaler())
-        ]), numeric),
-        ('ord', Pipeline([
-            ('imputer', SimpleImputer(strategy='most_frequent')),
-            ('encoder', OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1))
-        ]), ordinal),
-        ('nom', Pipeline([
-            ('imputer', SimpleImputer(strategy='most_frequent')),
-            ('encoder', OneHotEncoder(sparse_output=False, handle_unknown='ignore'))
-        ]), nominal)
-    ], remainder='passthrough')
-
-    X = df.drop('Cancer_Present', axis=1)
-    y = df['Cancer_Present']
-    return train_test_split(X, y, test_size=0.2, random_state=23), preprocess
-
-# Train Model
-def train_model(X_train, y_train, preprocess, model_name):
-    models = {
-        'Decision Tree': DecisionTreeClassifier(),
-        'Logistic Regression': LogisticRegression(),
-        'KNN': KNeighborsClassifier(),
-        'Random Forest': RandomForestClassifier(),
-        'XGBoost': XGBClassifier()
-    }
-    pipeline = Pipeline([
-        ('preprocessor', preprocess),
-        ('classifier', models[model_name])
-    ])
-    pipeline.fit(X_train, y_train)
-    return pipeline
-
-# Streamlit UI
-st.set_page_config(page_title='Cancer Prediction App', layout='wide')
-
-with st.sidebar:
-    st.image('https://via.placeholder.com/300x150.png?text=Cancer+Prediction')
-    st.markdown("### Select Machine Learning Model")
-    model_name = st.radio("Choose a Model", ['Decision Tree', 'Logistic Regression', 'KNN', 'Random Forest', 'XGBoost'])
-    if st.button("Train Model"):
-        df = load_data()
-        (X_train, X_test, y_train, y_test), preprocess = preprocess_data(df)
-        model = train_model(X_train, y_train, preprocess, model_name)
-        accuracy = model.score(X_test, y_test)
-        st.session_state['trained_model'] = model
-        st.session_state['X_train'] = X_train
-        st.success(f"Model Trained Successfully! Accuracy: {accuracy:.2f}")
-
-st.title("🎗️ Cancer Prediction Using Machine Learning 🎗️")
-
-st.markdown("""<style>.big-font {font-size:20px !important;}</style>
-<p class="big-font">Provide patient details below to predict cancer presence:</p>""", unsafe_allow_html=True)
-
-# Updated controls with radio buttons or buttons
-col1, col2 = st.columns(2)
-
-with col1:
-    age = st.radio("Age", [18, 20, 30, 40, 50, 60, 70, 80, 90, 100])
-    tumor_size = st.radio("Tumor Size", [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
-    tumor_grade = st.radio("Tumor Grade", ['High', 'Low', 'Medium'])
-    symptoms_severity = st.radio("Symptoms Severity", ['Mild', 'Moderate', 'Severe'])
-
-with col2:
-    smoking_history = st.radio("Smoking History", ['Never Smoker', 'Former Smoker', 'Current Smoker'])
-    alcohol_consumption = st.radio("Alcohol Consumption", ['Low', 'Moderate', 'High'])
-    exercise_frequency = st.radio("Exercise Frequency", ['Rarely', 'Occasionally', 'Regularly', 'Never'])
-    gender = st.radio("Gender", ['Male', 'Female'])
-    family_history = st.radio("Family History", ["No", "Yes"])
-
-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']
-        X_train = st.session_state['X_train']
-        input_df = pd.DataFrame(input_data, columns=X_train.columns)
-        input_transformed = model.named_steps['preprocessor'].transform(input_df)
-        prediction = model.named_steps['classifier'].predict(input_transformed)
-        st.markdown(f"**Prediction Result: {'🟥 Positive' if prediction[0] == 1 else '🟩 Negative'}**")
-    else:
-        st.error("Please train a model first!")