Update app.py

app.py

@@ -1,21 +1,30 @@
 import pandas as pd
-   from sklearn.preprocessing import LabelEncoder
-   from sklearn.ensemble import RandomForestClassifier
-   from sklearn.model_selection import train_test_split
-   import gradio as gr
-
-   # Function to load/generate data (replace with your data loading logic)
-   def load_data():
-       data = {
-           'face_shape': ['round', 'oval', 'square', 'heart', 'oval', 'round', 'square', 'heart', 'oval', 'square'],
-           'skin_tone': ['fair', 'medium', 'deep', 'cool', 'warm', 'fair', 'medium', 'deep', 'cool', 'warm'],
-           'face_size': ['small', 'medium', 'large', 'medium', 'small', 'large', 'small', 'large', 'medium', 'small'],
-           'mask_style': ['glitter_cat', 'gold_venetian', 'black_minimal', 'floral_masquerade', 'gold_venetian',
-                         'glitter_cat', 'black_minimal', 'floral_masquerade', 'gold_venetian', 'black_minimal']
-       }
-       return pd.DataFrame(data)
-
-   # Rest of your code (preprocessing, model training, recommend_mask function)
+from sklearn.preprocessing import LabelEncoder
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+import gradio as gr
+
+# Function to load/generate data
+def load_data():
+    data = {
+        'face_shape': ['round', 'oval', 'square', 'heart', 'oval', 'round', 'square', 'heart', 'oval', 'square'],
+        'skin_tone': ['fair', 'medium', 'deep', 'cool', 'warm', 'fair', 'medium', 'deep', 'cool', 'warm'],
+        'face_size': ['small', 'medium', 'large', 'medium', 'small', 'large', 'small', 'large', 'medium', 'small'],
+        'mask_style': ['glitter_cat', 'gold_venetian', 'black_minimal', 'floral_masquerade', 'gold_venetian',
+                       'glitter_cat', 'black_minimal', 'floral_masquerade', 'gold_venetian', 'black_minimal']
+    }
+    return pd.DataFrame(data)
+
+# Load data
+df = load_data()
+
+# Label encode categorical features
+label_encoders = {}
+for column in ['face_shape', 'skin_tone', 'face_size', 'mask_style']:
+    le = LabelEncoder()
+    df[column] = le.fit_transform(df[column])
+    label_encoders[column] = le
+
 # Split features and target
 X = df[['face_shape', 'skin_tone', 'face_size']]
 y = df['mask_style']
@@ -25,26 +34,41 @@ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_
 
 # Train Random Forest
 rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
-
 rf_model.fit(X_train, y_train)
-   df = load_data() 
-   # ... (preprocessing, model training)
-
-   # ... (recommend_mask function)
-
-   # ... (Get unique values for dropdown choices)
-
-   # Define Gradio interface
-   iface = gr.Interface(
-       fn=recommend_mask,
-       inputs=[
-           gr.Dropdown(choices=face_shapes_labels, label="Face Shape"),
-           gr.Dropdown(choices=skin_tones_labels, label="Skin Tone"),
-           gr.Dropdown(choices=face_sizes_labels, label="Face Size"),
-       ],
-       outputs="text",
-       title="🎭 Party Face Mask Recommender",
-       description="Get personalized party face mask recommendations based on your facial features."
-   )
-
-   iface.launch(share=True, server_name="0.0.0.0", server_port=7860) # Updated launch for Hugging Face
+
+# Define the recommendation function
+def recommend_mask(face_shape, skin_tone, face_size):
+    input_data = pd.DataFrame({
+        'face_shape': [face_shape],
+        'skin_tone': [skin_tone],
+        'face_size': [face_size]
+    })
+    for col in input_data.columns:
+        input_data[col] = label_encoders[col].transform(input_data[col])
+    prediction_encoded = rf_model.predict(input_data)
+    predicted_label = label_encoders['mask_style'].inverse_transform(prediction_encoded)
+    return predicted_label[0]
+
+# Get unique values for dropdown choices
+face_shapes_labels = df['face_shape'].unique().tolist()
+face_shapes_labels = label_encoders['face_shape'].inverse_transform(face_shapes_labels).tolist()
+skin_tones_labels = df['skin_tone'].unique().tolist()
+skin_tones_labels = label_encoders['skin_tone'].inverse_transform(skin_tones_labels).tolist()
+face_sizes_labels = df['face_size'].unique().tolist()
+face_sizes_labels = label_encoders['face_size'].inverse_transform(face_sizes_labels).tolist()
+
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=recommend_mask,
+    inputs=[
+        gr.Dropdown(choices=face_shapes_labels, label="Face Shape"),
+        gr.Dropdown(choices=skin_tones_labels, label="Skin Tone"),
+        gr.Dropdown(choices=face_sizes_labels, label="Face Size"),
+    ],
+    outputs="text",
+    title="🎭 Party Face Mask Recommender",
+    description="Get personalized party face mask recommendations based on your facial features."
+)
+
+iface.launch(share=True, server_name="0.0.0.0", server_port=7860)