Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.datasets import load_breast_cancer
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Load the breast cancer dataset
+data = load_breast_cancer()
+
+# Select only the 5 features for simplicity
+X = data.data[:, [0, 1, 2, 3, 4]]  # mean_radius, mean_texture, mean_perimeter, mean_area, mean_smoothness
+y = data.target
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train a RandomForestClassifier
+model = RandomForestClassifier(random_state=42)
+model.fit(X_train, y_train)
+
+# Get accuracy on test data
+y_pred = model.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+
+# Streamlit app interface
+st.title("Breast Cancer Detection App")
+
+st.write("This app predicts whether the tumor is benign or malignant based on user input.")
+
+# Function to get user input
+def get_user_input():
+    mean_radius = st.slider('Mean Radius', float(X[:, 0].min()), float(X[:, 0].max()), float(X[:, 0].mean()))
+    mean_texture = st.slider('Mean Texture', float(X[:, 1].min()), float(X[:, 1].max()), float(X[:, 1].mean()))
+    mean_perimeter = st.slider('Mean Perimeter', float(X[:, 2].min()), float(X[:, 2].max()), float(X[:, 2].mean()))
+    mean_area = st.slider('Mean Area', float(X[:, 3].min()), float(X[:, 3].max()), float(X[:, 3].mean()))
+    mean_smoothness = st.slider('Mean Smoothness', float(X[:, 4].min()), float(X[:, 4].max()), float(X[:, 4].mean()))
+
+    # Create a dictionary for user inputs
+    user_data = {
+        'mean_radius': mean_radius,
+        'mean_texture': mean_texture,
+        'mean_perimeter': mean_perimeter,
+        'mean_area': mean_area,
+        'mean_smoothness': mean_smoothness
+    }
+    
+    # Convert the dictionary to a dataframe
+    features = pd.DataFrame(user_data, index=[0])
+    return features
+
+# Get the user input
+user_input = get_user_input()
+
+# Display the user input
+st.subheader("User Input:")
+st.write(user_input)
+
+# Make prediction
+prediction = model.predict(user_input)
+
+# Output the result
+if prediction[0] == 0:
+    st.write("### The prediction is: **Benign**")
+else:
+    st.write("### The prediction is: **Malignant**")
+
+# Show the accuracy of the model
+st.write(f"Model Accuracy: {accuracy * 100:.2f}%")