Main Streamlit File

app.py

 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.")