Update app.py

app.py

@@ -37,18 +37,18 @@ feature_options = {
 #     prediction = model.predict(input_df)
 #     return 'Poisonous' if prediction[0] == 1 else 'Edible'
 
-# Prediction function for Gradio
+# Prediction function
 def predict_mushroom(*inputs):
     # Map the inputs to feature names
     features = list(feature_options.keys())
     user_input = dict(zip(features, inputs))
 
-    # Convert the string inputs (full names) directly to corresponding letters using the mappings
+    # Convert full names to letters using feature_options
     numerical_features = {}
     for feature, value in user_input.items():
-        if feature in mappings:
-            # Reverse the mapping to find the corresponding letter
-            inverse_mapping = {v: k for k, v in mappings[feature].items()}
+        if feature in feature_options:
+            # Reverse the feature_options dictionary to map full names to letter codes
+            inverse_mapping = {v: k for k, v in feature_options[feature].items()}
             if value in inverse_mapping:
                 numerical_features[feature] = inverse_mapping[value]  # Map full name to letter
             else:
@@ -59,6 +59,9 @@ def predict_mushroom(*inputs):
     # Convert the numerical features into a DataFrame
     input_df = pd.DataFrame([numerical_features])
 
+    # Load the trained model
+    model = joblib.load('mushroom_classifier.pkl')
+
     # Predict using the trained model
     prediction = model.predict(input_df)