Create app.py

app.py

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+# Import necessary libraries
+import numpy as np
+import pandas as pd
+import streamlit as st
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from lime.lime_tabular import LimeTabularExplainer
+
+# Load dataset
+data = load_iris()
+X = data.data
+y = data.target
+
+# Split dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# Train a Random Forest classifier
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Create an explainer using LIME
+explainer = LimeTabularExplainer(X_train, mode='classification', training_labels=y_train, feature_names=data.feature_names, class_names=data.target_names, discretize_continuous=True)
+
+# Streamlit UI
+st.title("Explainable AI with LIME")
+st.write("This application demonstrates how to make AI models more interpretable using LIME.")
+
+# User input for test instance index
+idx = st.number_input("Select a test instance index to explain", min_value=0, max_value=len(X_test)-1, value=0)
+
+# Choose a test instance to explain
+instance = X_test[idx].reshape(1, -1)
+
+# Get the explanation for the chosen instance
+exp = explainer.explain_instance(instance[0], model.predict_proba)
+
+# Display the explanation
+st.write(f"Explanation for instance {idx}:")
+st.write(exp.as_list())