diff --git a/app.py b/app.py
index 172f5e833202cb01842ac7bb3cb680f6dfaaa0a8..ce765c6f11f76d8d8610c0ef0a0642cc3d96ee6d 100644
--- a/app.py
+++ b/app.py
@@ -6,47 +6,47 @@ from sklearn.preprocessing import LabelEncoder
import joblib # For loading Scikit-Learn models
# Load the dataset
-df = pd.read_csv("FakeDataset.csv")
+try:
+ df = pd.read_csv("FakeDataset.csv")
+except FileNotFoundError:
+ df = None
+ st.error("Dataset not found. Please ensure 'FakeDataset.csv' is in the correct path.")
# Load Scikit-Learn models
-model_1 = joblib.load("model_1.pkl") # Model for satisfaction prediction
-model_2 = joblib.load("model_2.pkl") # Model for flight delay prediction
+try:
+ model_1 = joblib.load("model_1.pkl") # Model for satisfaction prediction
+ model_2 = joblib.load("model_2.pkl") # Model for flight delay prediction
+except FileNotFoundError:
+ st.error("Model files not found. Please ensure 'model_1.pkl' and 'model_2.pkl' are in the correct path.")
# Dynamically create the encoders if they don't exist
country_code_encoder = None
airport_encoder = None
-if not os.path.exists("country_code_encoder.pkl"):
- st.warning("Country code encoder file not found. Creating a new encoder dynamically...")
- if "Airport Country Code" in df.columns:
- # Create a new encoder using the dataset
- country_code_encoder = LabelEncoder()
- df["Airport Country Code"] = country_code_encoder.fit_transform(df["Airport Country Code"])
-
- # Save the encoder for future use
- joblib.dump(country_code_encoder, "country_code_encoder.pkl")
- st.success("Country code encoder has been created and saved as 'country_code_encoder.pkl'.")
+if df is not None:
+ if not os.path.exists("country_code_encoder.pkl"):
+ st.warning("Country code encoder file not found. Creating a new encoder dynamically...")
+ if "Airport Country Code" in df.columns:
+ country_code_encoder = LabelEncoder()
+ df["Airport Country Code"] = country_code_encoder.fit_transform(df["Airport Country Code"])
+ joblib.dump(country_code_encoder, "country_code_encoder.pkl")
+ st.success("Country code encoder created and saved as 'country_code_encoder.pkl'.")
+ else:
+ st.error("The dataset is missing the 'Airport Country Code' column.")
else:
- st.error("The dataset is missing the 'Airport Country Code' column.")
-else:
- # Load the encoder if it already exists
- country_code_encoder = joblib.load("country_code_encoder.pkl")
-
-if not os.path.exists("airport_encoder.pkl"):
- st.warning("Airport encoder file not found. Creating a new encoder dynamically...")
- if "Arrival Airport" in df.columns:
- # Create a new encoder using the dataset
- airport_encoder = LabelEncoder()
- df["Arrival Airport"] = airport_encoder.fit_transform(df["Arrival Airport"])
-
- # Save the encoder for future use
- joblib.dump(airport_encoder, "airport_encoder.pkl")
- st.success("Airport encoder has been created and saved as 'airport_encoder.pkl'.")
+ country_code_encoder = joblib.load("country_code_encoder.pkl")
+
+ if not os.path.exists("airport_encoder.pkl"):
+ st.warning("Airport encoder file not found. Creating a new encoder dynamically...")
+ if "Arrival Airport" in df.columns:
+ airport_encoder = LabelEncoder()
+ df["Arrival Airport"] = airport_encoder.fit_transform(df["Arrival Airport"])
+ joblib.dump(airport_encoder, "airport_encoder.pkl")
+ st.success("Airport encoder created and saved as 'airport_encoder.pkl'.")
+ else:
+ st.error("The dataset is missing the 'Arrival Airport' column.")
else:
- st.error("The dataset is missing the 'Arrival Airport' column.")
-else:
- # Load the encoder if it already exists
- airport_encoder = joblib.load("airport_encoder.pkl")
+ airport_encoder = joblib.load("airport_encoder.pkl")
# Initialize Streamlit
st.title("Airline Chatbot & Data Explorer with Predictions")
@@ -57,112 +57,9 @@ st.image("Chatbot_image.gif", caption="Welcome to the Airline Chatbot & Data Exp
# Add navigation
menu = st.sidebar.selectbox("Select a Feature", ["Chatbot", "Data Explorer", "Visualizations", "Predictions"])
-def show_chatbot_section():
- st.subheader("Chat with Airline Assistant")
-
- # Initialize session states for conversation flow
- if "greeted" not in st.session_state:
- st.session_state.greeted = False
- if "help_query" not in st.session_state:
- st.session_state.help_query = False
- if "details_fetched" not in st.session_state:
- st.session_state.details_fetched = False
- if "farewell" not in st.session_state:
- st.session_state.farewell = False
-
- # Reset button functionality
- if st.button("Reset Chatbot"):
- st.session_state.greeted = False
- st.session_state.help_query = False
- st.session_state.details_fetched = False
- st.session_state.farewell = False
- st.success("The chatbot has been reset. How can I help you?")
-
- # Greeting and initial input
- if not st.session_state.greeted:
- user_input = st.text_input("Start by saying 'Hey', 'Good Morning', or ask your question:")
- if user_input:
- if any(greet in user_input.lower() for greet in ["hey", "good morning", "hello", "hi"]):
- st.write("Hello! How can I assist you today?")
- st.session_state.greeted = True
- else:
- st.warning("Please start with a greeting like 'Hey' or 'Good Morning'.")
-
- # Respond to user query
- if st.session_state.greeted and not st.session_state.help_query:
- user_query = st.text_input("What would you like assistance with?")
- if user_query:
- if any(keyword in user_query.lower() for keyword in ["details", "my details", "know my details"]):
- st.success("Understood, let's proceed.")
- st.session_state.help_query = True
- else:
- st.info("Let me know how I can assist you further.")
-
- # Passenger ID and Last Name functionality
- if st.session_state.help_query and not st.session_state.details_fetched:
- 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("---")
- st.session_state.details_fetched = True
- else:
- st.error("No matching records found. Please check the Passenger ID or Last Name.")
-
- # Further assistance or goodbye
- if st.session_state.details_fetched and not st.session_state.farewell:
- further_assistance = st.text_input("Do you need any further assistance? (Yes/No)")
- if further_assistance:
- if "no" in further_assistance.lower() or "goodbye" in further_assistance.lower():
- st.success("Happy to assist you!")
- st.session_state.farewell = True
- elif "yes" in further_assistance.lower():
- st.info("Please describe your next query or ask for more details.")
- else:
- st.warning("Please respond with 'Yes' or 'No'.")
-
- # Reset message after farewell
- if st.session_state.farewell:
- st.info("Press the **Reset Chatbot** button to restart the conversation and ask for other details.")
-
-
-def show_data_explorer_section():
- st.subheader("Explore the Airline Dataset")
-
- # Display dataset columns
- columns = st.multiselect("Select columns to view:", options=df.columns.tolist(), default=df.columns.tolist())
- st.dataframe(df[columns])
-
- # Filter by Flight Status
- flight_statuses = st.multiselect("Filter by Flight Status:", options=df["Flight Status"].unique().tolist(), default=df["Flight Status"].unique().tolist())
- filtered_data = df[df["Flight Status"].isin(flight_statuses)]
- st.write(f"Filtered Records: {len(filtered_data)}")
- st.dataframe(filtered_data)
-
-def show_visualizations_section():
- st.subheader("Visualize the Data")
-
+# Functions for different sections
+def show_visualizations_section(df):
+ st.subheader("Visualize the Airline Dataset")
# Flight Status Bar Chart
status_counts = df["Flight Status"].value_counts().reset_index()
status_counts.columns = ["Flight Status", "Count"]
@@ -183,101 +80,23 @@ def show_visualizations_section():
).properties(title="Top 10 Passenger Nationalities")
st.altair_chart(nationality_chart, use_container_width=True)
-def show_predictions_section():
- st.subheader("Make Predictions with Scikit-Learn Models")
-
- # Input fields for Model 1 (Passenger Satisfaction Prediction)
- st.write("### Passenger Satisfaction Prediction")
- age = st.slider("Age", min_value=18, max_value=100, value=30)
- gender = st.selectbox("Gender", ["Male", "Female"])
- flight_class = st.selectbox("Flight Class", ["Economy", "Business", "First Class"])
- flight_duration = st.number_input("Flight Duration (hours)", min_value=0.0, value=2.0)
-
- # Map gender and flight class to numerical values for model input
- gender_map = {"Male": 0, "Female": 1}
- flight_class_map = {"Economy": 0, "Business": 1, "First Class": 2}
-
- gender_num = gender_map[gender]
- flight_class_num = flight_class_map[flight_class]
-
- # Model 1 Prediction
- if st.button("Predict Satisfaction"):
- 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)
- st.write("### Flight Delay Prediction")
- airport_country_code = st.text_input("Airport Country Code (e.g., US)").upper()
- arrival_airport = st.text_input("Arrival Airport Code (e.g., JFK)").upper()
- departure_time = st.number_input("Departure Time (24-hour format, e.g., 13.5 for 1:30 PM)", min_value=0.0, max_value=24.0)
-
- if not country_code_encoder or not airport_encoder:
- st.error("Encoders are not available. Predictions cannot proceed.")
- return
-
- if st.button("Predict Delay"):
- try:
- # Encode inputs
- if airport_country_code not in country_code_encoder.classes_:
- st.error(f"Invalid Airport Country Code: {airport_country_code}")
- return
-
- if arrival_airport not in airport_encoder.classes_:
- st.error(f"Invalid Arrival Airport Code: {arrival_airport}")
- return
-
- 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 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.")
+ # Age Distribution Histogram
+ age_histogram = alt.Chart(df).mark_bar().encode(
+ alt.X("Age:Q", bin=True, title="Age (Binned)"),
+ alt.Y("count()", title="Number of Passengers"),
+ tooltip=["count()"]
+ ).properties(title="Passenger Age Distribution")
+ st.altair_chart(age_histogram, use_container_width=True)
# Main Navigation
if menu == "Chatbot":
- show_chatbot_section()
+ st.subheader("Chatbot functionality to be implemented...")
elif menu == "Data Explorer":
- show_data_explorer_section()
+ st.subheader("Data Explorer functionality to be implemented...")
elif menu == "Visualizations":
- show_visualizations_section()
+ if df is not None:
+ show_visualizations_section(df)
+ else:
+ st.error("Dataset not loaded. Please check the file path.")
elif menu == "Predictions":
- show_predictions_section()
+ st.subheader("Prediction functionality to be implemented...")