Update app.py

app.py

@@ -1,61 +1,80 @@
-import streamlit as st
-import pandas as pd
-import pickle
-
-# Load the pre-trained model
-with open('best_model.pkl', 'rb') as model_file:
-    model = pickle.load(model_file)
-
-# Load the label encoder
-with open('label_encoder.pkl', 'rb') as label_encoder_file:
-    label_encoder = pickle.load(label_encoder_file)
-
-# Title of the app
-st.title("Coffee Type Prediction")
-
-# Sidebar inputs for user preferences
-st.sidebar.header("User Preferences")
-
-time_of_day = st.sidebar.selectbox("Time of Day", ['morning', 'afternoon', 'evening'])
-coffee_strength = st.sidebar.selectbox("Coffee Strength", ['mild', 'regular', 'strong'])
-sweetness_level = st.sidebar.selectbox("Sweetness Level", ['unsweetened', 'lightly sweetened', 'sweet'])
-milk_type = st.sidebar.selectbox("Milk Type", ['none', 'regular', 'skim', 'almond'])
-coffee_temperature = st.sidebar.selectbox("Coffee Temperature", ['hot', 'iced', 'cold brew'])
-flavored_coffee = st.sidebar.selectbox("Flavored Coffee", ['yes', 'no'])
-caffeine_tolerance = st.sidebar.selectbox("Caffeine Tolerance", ['low', 'medium', 'high'])
-coffee_bean = st.sidebar.selectbox("Coffee Bean", ['Arabica', 'Robusta', 'blend'])
-coffee_size = st.sidebar.selectbox("Coffee Size", ['small', 'medium', 'large'])
-dietary_preferences = st.sidebar.selectbox("Dietary Preferences", ['none', 'vegan', 'lactose-intolerant'])
-
-# Encoding the inputs manually (same encoding as in your training data)
-input_data = pd.DataFrame({
-    'Token_0': [time_of_day],
-    'Token_1': [coffee_strength],
-    'Token_2': [sweetness_level],
-    'Token_3': [milk_type],
-    'Token_4': [coffee_temperature],
-    'Token_5': [flavored_coffee],
-    'Token_6': [caffeine_tolerance],
-    'Token_7': [coffee_bean],
-    'Token_8': [coffee_size],
-    'Token_9': [dietary_preferences]
-})
-
-# One-hot encode the input data (ensure it matches the training data)
-input_encoded = pd.get_dummies(input_data)
-
-# Align columns with the training data (required columns)
-required_columns = [...]  # Include all columns from the original model training data
-for col in required_columns:
-    if col not in input_encoded.columns:
-        input_encoded[col] = 0
-input_encoded = input_encoded[required_columns]
-
-# Make the prediction
-prediction = model.predict(input_encoded)[0]
-
-# Reverse the label encoding (map the prediction back to the coffee type)
-coffee_type = label_encoder.inverse_transform([prediction])[0]
-
-# Display the prediction
-st.subheader(f"Recommended Coffee: {coffee_type}")
+import gradio as gr
+import pandas as pd
+import pickle
+import os
+
+# Helper function to load pickle files
+def load_pickle_file(file_name):
+    file_path = os.path.join(os.path.dirname(__file__), file_name)
+    try:
+        if os.path.exists(file_path):
+            with open(file_path, 'rb') as file:
+                return pickle.load(file)
+        else:
+            return f"File {file_name} not found."
+    except Exception as e:
+        return f"An error occurred while loading {file_name}: {e}"
+
+# Load the pre-trained model and label encoder
+model = load_pickle_file('best_model.pkl')
+label_encoder = load_pickle_file('label_encoder.pkl')
+
+# Ensure model and label encoder are loaded correctly
+if isinstance(model, str) or isinstance(label_encoder, str):
+    raise Exception(f"Error loading model or label encoder: {model} | {label_encoder}")
+
+# Define the prediction function
+def predict_coffee_type(time_of_day, coffee_strength, sweetness_level, milk_type, coffee_temperature, flavored_coffee, caffeine_tolerance, coffee_bean, coffee_size, dietary_preferences):
+    # Input Data
+    input_data = pd.DataFrame({
+        'Token_0': [time_of_day],
+        'Token_1': [coffee_strength],
+        'Token_2': [sweetness_level],
+        'Token_3': [milk_type],
+        'Token_4': [coffee_temperature],
+        'Token_5': [flavored_coffee],
+        'Token_6': [caffeine_tolerance],
+        'Token_7': [coffee_bean],
+        'Token_8': [coffee_size],
+        'Token_9': [dietary_preferences]
+    })
+
+    # One-hot encode the input data (ensure it matches the training data)
+    input_encoded = pd.get_dummies(input_data)
+    required_columns = model.feature_names_in_  # Ensure that the input has the correct columns
+    for col in required_columns:
+        if col not in input_encoded.columns:
+            input_encoded[col] = 0  # Add missing columns as 0
+    
+    input_encoded = input_encoded[required_columns]  # Ensure the order of columns matches the training data
+    
+    # Make prediction
+    prediction = model.predict(input_encoded)[0]
+    
+    # Decode the label
+    coffee_type = label_encoder.inverse_transform([prediction])[0]
+    
+    return f"Recommended Coffee: {coffee_type}"
+
+# Set up Gradio interface
+interface = gr.Interface(
+    fn=predict_coffee_type,
+    inputs=[
+        gr.inputs.Dropdown(choices=['morning', 'afternoon', 'evening'], label="Time of Day"),
+        gr.inputs.Dropdown(choices=['mild', 'regular', 'strong'], label="Coffee Strength"),
+        gr.inputs.Dropdown(choices=['unsweetened', 'lightly sweetened', 'sweet'], label="Sweetness Level"),
+        gr.inputs.Dropdown(choices=['none', 'regular', 'skim', 'almond'], label="Milk Type"),
+        gr.inputs.Dropdown(choices=['hot', 'iced', 'cold brew'], label="Coffee Temperature"),
+        gr.inputs.Dropdown(choices=['yes', 'no'], label="Flavored Coffee"),
+        gr.inputs.Dropdown(choices=['low', 'medium', 'high'], label="Caffeine Tolerance"),
+        gr.inputs.Dropdown(choices=['Arabica', 'Robusta', 'blend'], label="Coffee Bean"),
+        gr.inputs.Dropdown(choices=['small', 'medium', 'large'], label="Coffee Size"),
+        gr.inputs.Dropdown(choices=['none', 'vegan', 'lactose-intolerant'], label="Dietary Preferences")
+    ],
+    outputs="text",
+    title="Coffee Type Prediction"
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()