Update app.py

app.py

@@ -5,7 +5,7 @@ import numpy as np
 # Load the model and unique brand values
 model = joblib.load('model.joblib')
 unique_values = joblib.load('unique_values.joblib')
-brand_values = list(unique_values['Brand'])
+brand_values = list(unique_values['Brand'])  # Gradio's dropdown only accpets a list
 
 # Define the prediction function
 def predict(brand, screen_size, resolution_width, resolution_height):
@@ -14,8 +14,13 @@ def predict(brand, screen_size, resolution_width, resolution_height):
     resolution_width = int(resolution_width)
     resolution_height = int(resolution_height)
     
-    # Prepare the input array for prediction
-    input_data = np.array([[brand, screen_size, resolution_width, resolution_height]])
+    # Create a DataFrame with the input data
+    input_data = pd.DataFrame({
+        'Brand': [brand],
+        'Screen Size': [screen_size],
+        'Resolution (Width)': [resolution_width],
+        'Resolution (Height)': [resolution_height]
+    })
     
     # Perform the prediction
     prediction = model.predict(input_data)