Updated app at sön 19 nov 2023 21:58:06 CET

app.py

@@ -6,9 +6,6 @@ import joblib
 import pandas as pd
 
 
-# Placeholder for loading your trained models
-# Load your models here, e.g., model_red = joblib.load('path_to_red_wine_model')
-# model_white = joblib.load('path_to_white_wine_model')
 
 def wine_quality(wine_type, fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides,
                  free_sulfur_dioxide, total_sulfur_dioxide, density, ph, sulphates, alcohol):
@@ -21,14 +18,11 @@ def wine_quality(wine_type, fixed_acidity, volatile_acidity, citric_acid, residu
     print("Predicting...")
     print(df)
 
-    # Use the appropriate model based on the wine type
+
+
     if wine_type == 'Red':
-        # Placeholder for prediction with the red wine model
-        # quality = model_red.predict(df)[0]
         quality = "Red Wine Quality Prediction Placeholder"
     else:
-        # Placeholder for prediction with the white wine model
-        # quality = model_white.predict(df)[0]
         quality = "White Wine Quality Prediction Placeholder"
 
     return quality
@@ -36,10 +30,10 @@ def wine_quality(wine_type, fixed_acidity, volatile_acidity, citric_acid, residu
 
 # Define the Gradio interface
 iface = gr.Interface(fn=wine_quality,
-                     inputs=["dropdown", "number", "number", "number", "number", "number",
+                     inputs=["text", "number", "number", "number", "number", "number",
                              "number", "number", "number", "number", "number", "number"],
                      outputs="text",
-                     examples=[['Red', 7.4, 0.70, 0.00, 1.9, 0.076, 11.0, 34.0, 0.9978, 3.51, 0.56, 9.4],
-                               ['White', 7.0, 0.27, 0.36, 20.7, 0.045, 45.0, 170.0, 1.0010, 3.00, 0.45, 8.8]])
+                     examples=[['red', 7.4, 0.70, 0.00, 1.9, 0.076, 11.0, 34.0, 0.9978, 3.51, 0.56, 9.4],
+                               ['white', 7.0, 0.27, 0.36, 20.7, 0.045, 45.0, 170.0, 1.0010, 3.00, 0.45, 8.8]])
 
 iface.launch()