Update app.py

app.py

@@ -1,50 +1,85 @@
-import gradio as gr
-import joblib
-import numpy as np
-# Load models
-model_flavor = joblib.load("random_forest_model_flavor.pkl")
-model_topping = joblib.load("random_forest_model_topping.pkl")
-model_drink = joblib.load("random_forest_model_drink.pkl")
-
-# Load encoders
-encoder_flavor = joblib.load("encoder_flavor.pkl")
-encoder_topping = joblib.load("encoder_topping.pkl")
-encoder_drink = joblib.load("encoder_drink.pkl")
-input_encoders = joblib.load("input_encoders.pkl")
-
-# Prediction function
-def predict_merienda(mood, weather, craving_level, last_meal, budget):
-    features = [mood, weather, craving_level, last_meal, budget]
-    encoded = [input_encoders[col].transform([val])[0] for col, val in zip(input_encoders.keys(), features)]
-    encoded_np = np.array(encoded).reshape(1, -1)
-    pred_flavor = encoder_flavor.inverse_transform(model_flavor.predict(encoded_np))[0]
-    pred_topping = encoder_topping.inverse_transform(model_topping.predict(encoded_np))[0]
-    pred_drink = encoder_drink.inverse_transform(model_drink.predict(encoded_np))[0]
-    return pred_flavor, pred_topping, pred_drink
-
-# Dropdown options
-mood_list = input_encoders["mood"].classes_.tolist()
-weather_list = input_encoders["weather"].classes_.tolist()
-craving_list = input_encoders["craving_level"].classes_.tolist()
-last_meal_list = input_encoders["last_meal"].classes_.tolist()
-budget_list = input_encoders["budget"].classes_.tolist()
-# Gradio Interface
-iface = gr.Interface(
-    fn=predict_merienda,
-    inputs=[
-        gr.Dropdown(mood_list, label="Mood"),
-        gr.Dropdown(weather_list, label="Weather"),
-        gr.Dropdown(craving_list, label="Craving Level"),
-        gr.Dropdown(last_meal_list, label="Last Meal"),
-        gr.Dropdown(budget_list, label="Budget"),
-    ],
-    outputs=[
-        gr.Text(label="Recommended Flavor"),
-        gr.Text(label="Recommended Topping"),
-        gr.Text(label="Recommended Drink"),
-    ],
-    title=" Merienda Classifier",
-    description="Predicts the best pancit canton flavor, topping, and drink based on your current situation"
-    )
-
+import gradio as gr
+import joblib
+import numpy as np
+# Load models
+model_flavor = joblib.load("random_forest_model_flavor.pkl")
+model_topping = joblib.load("random_forest_model_topping.pkl")
+model_drink = joblib.load("random_forest_model_drink.pkl")
+
+# Load encoders
+encoder_flavor = joblib.load("encoder_flavor.pkl")
+encoder_topping = joblib.load("encoder_topping.pkl")
+encoder_drink = joblib.load("encoder_drink.pkl")
+input_encoders = joblib.load("input_encoders.pkl")
+
+# Prediction function
+def predict_merienda(mood, weather, craving_level, last_meal, budget):
+    features = [mood, weather, craving_level, last_meal, budget]
+    encoded = [input_encoders[col].transform([val])[0] for col, val in zip(input_encoders.keys(), features)]
+    encoded_np = np.array(encoded).reshape(1, -1)
+    pred_flavor = encoder_flavor.inverse_transform(model_flavor.predict(encoded_np))[0]
+    pred_topping = encoder_topping.inverse_transform(model_topping.predict(encoded_np))[0]
+    pred_drink = encoder_drink.inverse_transform(model_drink.predict(encoded_np))[0]
+    return pred_flavor, pred_topping, pred_drink
+
+# Dropdown options
+mood_list = input_encoders["mood"].classes_.tolist()
+weather_list = input_encoders["weather"].classes_.tolist()
+craving_list = input_encoders["craving_level"].classes_.tolist()
+last_meal_list = input_encoders["last_meal"].classes_.tolist()
+budget_list = input_encoders["budget"].classes_.tolist()
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_merienda,
+    inputs=[
+        gr.Dropdown(mood_list, label="Mood"),
+        gr.Dropdown(weather_list, label="Weather"),
+        gr.Dropdown(craving_list, label="Craving Level"),
+        gr.Dropdown(last_meal_list, label="Last Meal"),
+        gr.Dropdown(budget_list, label="Budget"),
+    ],
+    outputs=[
+        gr.Text(label="Recommended Flavor"),
+        gr.Text(label="Recommended Topping"),
+        gr.Text(label="Recommended Drink"),
+    ],
+    title=" Merienda Classifier",
+    description="Predicts the best pancit canton flavor, topping, and drink based on your current situation"
+    )
+
+import gradio as gr
+
+# Optional custom CSS for prettier background and fonts
+custom_css = """
+body {
+    background: linear-gradient(to right, #fbc2eb, #a6c1ee);
+    font-family: 'Poppins', sans-serif;
+}
+h1 {
+    text-align: center;
+    color: #6D214F;
+}
+"""
+
+# Your function to classify merienda (replace with your actual model code)
+def classify_merienda(image):
+    return "Pancit"  # <-- Replace this with your prediction logic
+
+# Build the app
+with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
+    gr.Markdown("# 🍽️ Welcome to the Merienda Classifier!")
+    gr.Markdown("Upload a picture of your favorite **merienda** and let the AI guess what it is!")
+
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Upload your Merienda")
+            classify_button = gr.Button("Classify!")
+        with gr.Column():
+            output_label = gr.Label(label="Prediction")
+
+    classify_button.click(classify_merienda, inputs=[input_image], outputs=[output_label])
+
+demo.launch()
+
+
 iface.launch()