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import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier, HistGradientBoostingClassifier
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
from sklearn.preprocessing import LabelEncoder
import shap
# Load Dataset
data_path = 'Survey Final.csv'
df = pd.read_csv(data_path)
# Encode Target Column
le = LabelEncoder()
df['Percieved Safety'] = le.fit_transform(df['Percieved Safety'])
# Data Splitting (Global for Use in All Sections)
test_size = 0.2 # Default test size (can be changed in data splitting section)
X = df.drop(columns=['Percieved Safety'])
y = df['Percieved Safety']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
# Streamlit App
st.set_page_config(page_title="Evaluating Safety Perception on Commuting App", layout='wide')
st.title("Evaluating Safety Perception on Commuting ")
# Sidebar section
with st.sidebar:
st.image("logo.png", use_column_width=True, caption="Your Company Name")
st.markdown("---")
selected = st.selectbox(
"Navigation",
[
"📊 Data Overview",
"🔍 Exploratory Data Analysis",
"🤖 Model Training, Evaluation & Explanations",
"🔮 Predict Perceived Safety"
]
)
# Data Overview
if selected == "📊 Data Overview":
st.header("📊 Data Overview")
if st.checkbox("Show Dataset"):
st.write(df.head())
st.write(f"Dataset Shape: {df.shape}")
st.write("Data Types:")
st.write(df.dtypes)
# Exploratory Data Analysis
if selected == "🔍 Exploratory Data Analysis":
st.header("🔍 Exploratory Data Analysis")
if st.checkbox("Correlation Heatmap"):
st.write("Correlation Heatmap")
fig, ax = plt.subplots(figsize=(10, 6))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm', ax=ax)
st.pyplot(fig)
if st.checkbox("Histogram"):
st.write("Histograms of Numeric Columns")
numeric_columns = df.select_dtypes(include=['int64', 'float64']).columns.tolist()
selected_column = st.selectbox("Select Column for Histogram", numeric_columns)
fig, ax = plt.subplots()
sns.histplot(df[selected_column], kde=True, ax=ax)
st.pyplot(fig)
if st.checkbox("Boxplot for Numeric Columns"):
st.write("Boxplot of Numeric Columns")
numeric_columns = df.select_dtypes(include=['int64', 'float64']).columns.tolist()
selected_column = st.selectbox("Select Column for Boxplot", numeric_columns)
fig, ax = plt.subplots()
sns.boxplot(data=df, x=selected_column, ax=ax)
st.pyplot(fig)
if st.checkbox("Pairplot of Dataset"):
st.write("Pairplot of the Dataset")
fig = sns.pairplot(df)
st.pyplot(fig)
# Model Training, Evaluation & Explanations
if selected == "🤖 Model Training, Evaluation & Explanations":
st.header("🤖 Model Training, Evaluation & Explanations")
if st.checkbox("Train, Evaluate, and Explain Models"):
# Model Training
st.write("Training Tree-Based Models")
models = {
"Random Forest": RandomForestClassifier(random_state=42),
"Gradient Boosting": GradientBoostingClassifier(random_state=42),
"Extra Trees": ExtraTreesClassifier(random_state=42),
"Histogram Gradient Boosting": HistGradientBoostingClassifier(random_state=42)
}
model_preds = {}
model_accuracies = {}
for model_name, model in models.items():
model.fit(X_train, y_train)
preds = model.predict(X_test)
accuracy = accuracy_score(y_test, preds)
model_preds[model_name] = preds
model_accuracies[model_name] = accuracy
st.write(f"{model_name} Accuracy: {accuracy:.2f}")
# Model Evaluation
selected_model = st.selectbox("Select Model for Detailed Evaluation", list(models.keys()))
selected_model_instance = models[selected_model]
selected_preds = model_preds[selected_model]
st.write("Classification Report:")
st.text(classification_report(y_test, selected_preds))
st.write("Confusion Matrix:")
st.write(confusion_matrix(y_test, selected_preds))
# Feature Importance
if st.checkbox("Show Feature Importance"):
st.write(f"Feature Importance from {selected_model} Model")
if hasattr(selected_model_instance, 'feature_importances_'):
feature_importances = selected_model_instance.feature_importances_
importance_df = pd.DataFrame({"Feature": X_train.columns, "Importance": feature_importances})
importance_df = importance_df.sort_values(by="Importance", ascending=False)
st.bar_chart(importance_df.set_index("Feature"))
else:
st.write("The selected model does not support feature importances.")
# SHAP Explanations
if st.checkbox("Explain Predictions with SHAP"):
st.write(f"SHAP Explanation for {selected_model} Model")
explainer = shap.TreeExplainer(selected_model_instance)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test, plot_type="bar")
st.pyplot()
# Predict Percieved Safety
if selected == "🔮 Predict Percieved Safety":
st.header("🔮 Predict Percieved Safety")
st.write("Please provide the following information to predict Percieved Safety for transport:")
# User Input for Prediction
overcrowding = st.selectbox("How overcrowded do you think the transport is on a scale from 0 (Not overcrowded) to 4 (Very overcrowded)?", [0, 1, 2, 3, 4])
preference = st.selectbox("How much do you prefer this mode of transport on a scale from 0 (Not preferred) to 4 (Highly preferred)?", [0, 1, 2, 3, 4])
daytime_safety = st.selectbox("How safe do you feel using this transport during the daytime on a scale from 0 (Not safe) to 4 (Very safe)?", [0, 1, 2, 3, 4])
nighttime_safety = st.selectbox("How safe do you feel using this transport during the nighttime on a scale from 0 (Not safe) to 4 (Very safe)?", [0, 1, 2, 3, 4])
taxi_dsafety = st.selectbox("How safe do you feel using a taxi during the day on a scale from 0 (Not safe) to 4 (Very safe)?", [0, 1, 2, 3, 4])
taxi_nsafety = st.selectbox("How safe do you feel using a taxi during the night on a scale from 0 (Not safe) to 4 (Very safe)?", [0, 1, 2, 3, 4])
reporting = st.selectbox("How comfortable are you with reporting incidents related to this transport on a scale from 0 (Not comfortable) to 4 (Very comfortable)?", [0, 1, 2, 3, 4])
background_check = st.selectbox("How effective do you think background checks are for transport personnel on a scale from 0 (Not effective) to 4 (Very effective)?", [0, 1, 2, 3, 4])
user_data = np.array([[
overcrowding, preference, daytime_safety, nighttime_safety,
taxi_dsafety, taxi_nsafety, reporting, background_check
]])
if st.button("Predict Percieved Safety"):
# Train the Model (Again) and Predict
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)
prediction = model.predict(user_data)
predicted_class = le.inverse_transform(prediction)
st.write(f"Predicted Percieved Safety Class: {predicted_class[0]}")
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