Update app.py

app.py

@@ -1,34 +1,50 @@
-import gradio as gr
-import tensorflow as tf
-import cv2
-import numpy as np
 import pandas as pd
-from sklearn.preprocessing import LabelEncoder
-
-# Load trained model
-model = tf.keras.models.load_model("cnn_mask_model.h5")
-
-# Load dataset & encode mask types
-df = pd.read_excel("mask_dataset.xlsx")
-label_encoder = LabelEncoder()
-df["mask_type"] = label_encoder.fit_transform(df["mask_type"])
-
-# Define function for prediction
-def predict_mask(image):
-    img_resized = cv2.resize(image, (128, 128)).reshape(1, 128, 128, 3) / 255.0
-    mask_pred = model.predict(img_resized).argmax()
-    
-    mask_name = label_encoder.inverse_transform([mask_pred])[0]  # Convert label back
-
-    return f"Recommended Mask: {mask_name}"
-
-# Gradio interface for Hugging Face Spaces
-iface = gr.Interface(
-    fn=predict_mask,
-    inputs=gr.Image(type="numpy", label="Upload Your Face Image"),
-    outputs="text",
-    title="🎭 Party Mask Recommendation App",
-    description="Upload a face image and get the best party mask recommendation!"
-)
-
-iface.launch()
+   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)
+# Split features and target
+X = df[['face_shape', 'skin_tone', 'face_size']]
+y = df['mask_style']
+
+# Split into train and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42, stratify=y)
+
+# 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