Update app.py

app.py

@@ -37,39 +37,40 @@ feature_options = {
 #     prediction = model.predict(input_df)
 #     return 'Poisonous' if prediction[0] == 1 else 'Edible'
 
-# Prediction function
 def predict_mushroom(*inputs):
-    # Map the inputs to feature names
-    features = list(feature_options.keys())
-    user_input = dict(zip(features, inputs))
-
-    # Convert full names to letters using feature_options
-    numerical_features = {}
-    for feature, value in user_input.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:
-                raise ValueError(f"Invalid value '{value}' for feature '{feature}'.")
+    # Build a dictionary of feature: full name selections.
+    # The keys are the feature names (e.g., 'cap-shape') and the values are the full names (e.g., 'bell')
+    user_full = dict(zip(feature_options.keys(), inputs))
+    
+    # Convert the full names to letters using feature_options (reverse mapping)
+    user_letters = {}
+    for feature, full_value in user_full.items():
+        # Create reverse mapping: full name -> letter for this feature
+        inverse = {v: k for k, v in feature_options[feature].items()}
+        if full_value in inverse:
+            user_letters[feature] = inverse[full_value]
         else:
-            raise ValueError(f"Feature '{feature}' is not recognized.")
-
-    # Convert the numerical features into a DataFrame
-    input_df = pd.DataFrame([numerical_features])
-
-    # Load the trained model
+            raise ValueError(f"Invalid selection for {feature}: {full_value}")
+    
+    # Convert letters to numeric values using the mappings from mappings.pkl
+    numeric_features = {}
+    for feature, letter in user_letters.items():
+        if feature in mappings and letter in mappings[feature]:
+            numeric_features[feature] = mappings[feature][letter]
+        else:
+            raise ValueError(f"Mapping not found for feature {feature} with letter {letter}")
+    
+    # Create a DataFrame for model input
+    input_df = pd.DataFrame([numeric_features])
+    
+    # Load the trained model (assumes the file is in the working directory)
     model = joblib.load('mushroom_classifier.pkl')
-
-    # Predict using the trained model
+    
+    # Predict using the model
     prediction = model.predict(input_df)
-
-    # Interpret the prediction
-    if prediction[0] == 0:
-        return 'Edible'
-    else:
-        return 'Poisonous'
+    
+    # Return the human-readable result
+    return 'Edible' if prediction[0] == 0 else 'Poisonous'
 
 demo = gr.Interface(
     fn=predict_mushroom,