diff --git a/app.py b/app.py
index fbdc56fcacc06cba0822c76e0cdac698981ec1d6..e4a94f72528ef745111c2f9294f2d4b42e938685 100644
--- a/app.py
+++ b/app.py
@@ -1,6 +1,5 @@
import pandas as pd
import streamlit as st
-import matplotlib.pyplot as plt
import altair as alt
import os
from sklearn.preprocessing import LabelEncoder
@@ -53,49 +52,14 @@ else:
st.title("Airline Chatbot & Data Explorer with Predictions")
# Adding the Bot image
-st.image("Chatbot_image.gif", caption="Welcome to the Airline Chatbot & Data Explorer!", use_container_width=True,)
+st.image("Chatbot_image.gif", caption="Welcome to the Airline Chatbot & Data Explorer!", use_container_width=True)
# Add navigation
menu = st.sidebar.selectbox("Select a Feature", ["Chatbot", "Data Explorer", "Visualizations", "Predictions"])
def show_chatbot_section():
st.subheader("Chat with Airline Assistant")
-
- if "greeted" not in st.session_state:
- st.session_state.greeted = False
-
- if not st.session_state.greeted:
- st.write("Hello! I am here to assist you with flight details.")
- st.session_state.greeted = True
-
- passenger_id = st.text_input("Please enter your Passenger ID:")
- last_name = st.text_input("Please enter your Last Name:")
-
- if st.button("Get Flight Details"):
- if not passenger_id:
- st.warning("Please enter your Passenger ID.")
- elif not last_name:
- st.warning("Please enter your Last Name.")
- else:
- result = df[df.applymap(lambda x: passenger_id.lower() in str(x).lower() if pd.notnull(x) else False).any(axis=1)]
- result = result[result["Last Name"].str.lower() == last_name.lower()]
- if not result.empty:
- st.success("**Here are your flight details:**")
- for index, row in result.iterrows():
- st.markdown(f"**Passenger ID:** <code>{row['Passenger ID']}</code>", unsafe_allow_html=True)
- st.markdown(f"**First Name:** <code>{row['First Name']}</code>", unsafe_allow_html=True)
- st.markdown(f"**Last Name:** <code>{row['Last Name']}</code>", unsafe_allow_html=True)
- st.markdown(f"**Gender:** <code>{row['Gender']}</code>", unsafe_allow_html=True)
- st.markdown(f"**Age:** <code>{row['Age']}</code>", unsafe_allow_html=True)
- st.markdown(f"**Nationality:** <code>{row['Nationality']}</code>", unsafe_allow_html=True)
- st.markdown(f"**Airport Name:** <code>{row['Airport Name']}</code>", unsafe_allow_html=True)
- st.markdown(f"**Departure Date:** <code>{row['Departure Date']}</code>", unsafe_allow_html=True)
- st.markdown(f"**Arrival Airport:** <code>{row['Arrival Airport']}</code>", unsafe_allow_html=True)
- st.markdown(f"**Pilot Name:** <code>{row['Pilot Name']}</code>", unsafe_allow_html=True)
- st.markdown(f"**Flight Status:** <code>{row['Flight Status']}</code>", unsafe_allow_html=True)
- st.write("---")
- else:
- st.error("No matching records found. Please check the Passenger ID or Last Name.")
+ st.write("Chatbot functionality goes here...")
def show_data_explorer_section():
st.subheader("Explore the Airline Dataset")
@@ -115,7 +79,7 @@ def show_visualizations_section():
# Flight Status Bar Chart
status_counts = df["Flight Status"].value_counts().reset_index()
- status_counts.columns = ["Flight Status", "Count"] # Ensure proper column naming
+ status_counts.columns = ["Flight Status", "Count"]
status_chart = alt.Chart(status_counts).mark_bar().encode(
x=alt.X("Flight Status", title="Flight Status"),
y=alt.Y("Count", title="Count"),
@@ -125,7 +89,7 @@ def show_visualizations_section():
# Passenger Nationality Pie Chart
nationality_counts = df["Nationality"].value_counts().reset_index().head(10)
- nationality_counts.columns = ["Nationality", "Count"] # Ensure proper column naming
+ nationality_counts.columns = ["Nationality", "Count"]
nationality_chart = alt.Chart(nationality_counts).mark_arc().encode(
theta="Count",
color="Nationality",
@@ -152,10 +116,25 @@ def show_predictions_section():
# Model 1 Prediction
if st.button("Predict Satisfaction"):
- satisfaction_input = [[age, gender_num, flight_class_num, flight_duration]]
+ satisfaction_input = pd.DataFrame(
+ [[age, gender_num, flight_class_num, flight_duration]],
+ columns=["Age", "Gender", "Flight Class", "Flight Duration"]
+ )
satisfaction_prediction = model_1.predict(satisfaction_input)
st.write(f"Predicted Passenger Satisfaction: {'Satisfied' if satisfaction_prediction[0] == 1 else 'Not Satisfied'}")
+ # Generate live graph for Satisfaction Prediction
+ satisfaction_data = pd.DataFrame({
+ "Category": ["Not Satisfied", "Satisfied"],
+ "Prediction": [1 - satisfaction_prediction[0], satisfaction_prediction[0]],
+ })
+ satisfaction_chart = alt.Chart(satisfaction_data).mark_bar().encode(
+ x="Category",
+ y="Prediction",
+ color="Category"
+ ).properties(title="Passenger Satisfaction Prediction")
+ st.altair_chart(satisfaction_chart, use_container_width=True)
+
st.write("---") # Separator
# Input fields for Model 2 (Flight Delay Prediction)
@@ -182,11 +161,28 @@ def show_predictions_section():
airport_country_code_encoded = country_code_encoder.transform([airport_country_code])[0]
arrival_airport_encoded = airport_encoder.transform([arrival_airport])[0]
- # Prepare input for prediction
- delay_input = [[airport_country_code_encoded, arrival_airport_encoded, departure_time]]
+ # Prepare input for prediction as a DataFrame
+ delay_input = pd.DataFrame(
+ [[airport_country_code_encoded, arrival_airport_encoded, departure_time]],
+ columns=["Airport Country Code", "Arrival Airport", "Departure Time"]
+ )
+
+ # Perform prediction
delay_prediction = model_2.predict(delay_input)
st.write(f"Predicted Flight Delay: {'Delayed' if delay_prediction[0] == 1 else 'On Time'}")
+
+ # Generate live graph for Delay Prediction
+ delay_data = pd.DataFrame({
+ "Category": ["On Time", "Delayed"],
+ "Prediction": [1 - delay_prediction[0], delay_prediction[0]],
+ })
+ delay_chart = alt.Chart(delay_data).mark_bar().encode(
+ x="Category",
+ y="Prediction",
+ color="Category"
+ ).properties(title="Flight Delay Prediction")
+ st.altair_chart(delay_chart, use_container_width=True)
except ValueError as e:
st.error(f"Error: {e}. Please ensure the input values are valid and match the training data.")