Update app.py

app.py

@@ -1,10 +1,41 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import pickle
+from huggingface_hub import hf_hub_download
+
+def load_artifacts():
+    model_path = hf_hub_download(repo_id="alperugurcan/poisonous-mushrooms", filename="model.pkl")
+    target_encoder_path = hf_hub_download(repo_id="alperugurcan/poisonous-mushrooms", filename="target_encoder.pkl")
+    label_encoders_path = hf_hub_download(repo_id="alperugurcan/poisonous-mushrooms", filename="label_encoders.pkl")
+    numeric_columns_path = hf_hub_download(repo_id="alperugurcan/poisonous-mushrooms", filename="numeric_columns.pkl")
+    categorical_columns_path = hf_hub_download(repo_id="alperugurcan/poisonous-mushrooms", filename="categorical_columns.pkl")
+    
+    with open(model_path, 'rb') as f:
+        model = pickle.load(f)
+    with open(target_encoder_path, 'rb') as f:
+        target_encoder = pickle.load(f)
+    with open(label_encoders_path, 'rb') as f:
+        label_encoders = pickle.load(f)
+    with open(numeric_columns_path, 'rb') as f:
+        numeric_columns = pickle.load(f)
+    with open(categorical_columns_path, 'rb') as f:
+        categorical_columns = pickle.load(f)
+        
+    return {
+        'model': model,
+        'target_encoder': target_encoder,
+        'feature_encoders': label_encoders,
+        'numeric_columns': numeric_columns,
+        'categorical_columns': categorical_columns
+    }
+
 def predict_mushroom(cap_diameter, gill_spacing, stem_root, veil_color, season):
     artifacts = load_artifacts()
     model = artifacts['model']
     target_encoder = artifacts['target_encoder']
     feature_encoders = artifacts['feature_encoders']
     
-    # Tüm özellikleri içeren sözlük
     input_data = {
         'cap-diameter': [cap_diameter],
         'cap-shape': ['x'],           # varsayılan değer
@@ -30,7 +61,6 @@ def predict_mushroom(cap_diameter, gill_spacing, stem_root, veil_color, season):
     
     df = pd.DataFrame(input_data)
     
-    # Kategorik özellikleri dönüştür
     categorical_features = [col for col in df.columns if col not in ['cap-diameter', 'stem-height', 'stem-width']]
     for col in categorical_features:
         if col in feature_encoders:
@@ -39,4 +69,51 @@ def predict_mushroom(cap_diameter, gill_spacing, stem_root, veil_color, season):
     prediction = model.predict(df)[0]
     class_prediction = target_encoder.inverse_transform([prediction])[0]
     
-    return "Edible" if class_prediction == 'e' else "Poisonous"
+    return "Edible" if class_prediction == 'e' else "Poisonous"
+
+iface = gr.Interface(
+    fn=predict_mushroom,
+    inputs=[
+        gr.Slider(
+            minimum=2.0,
+            maximum=20.0,
+            value=10.0,
+            step=0.5,
+            label="Cap Diameter (cm)",
+            info="Slide to select mushroom cap width"
+        ),
+        gr.Dropdown(
+            choices=['c', 'w'],
+            value='c',
+            label="Gill Spacing",
+            info="c: close, w: wide"
+        ),
+        gr.Dropdown(
+            choices=['b', 'e', 'c', 'r'],
+            value='b',
+            label="Stem Root",
+            info="b: bulbous, e: equal, c: club, r: rooted"
+        ),
+        gr.Dropdown(
+            choices=['w', 'n', 'o', 'y'],
+            value='w',
+            label="Veil Color",
+            info="w: white, n: brown, o: orange, y: yellow"
+        ),
+        gr.Dropdown(
+            choices=['s', 'u', 'a', 'w'],
+            value='s',
+            label="Season",
+            info="s: spring, u: summer, a: autumn, w: winter"
+        )
+    ],
+    outputs=gr.Label(label="Prediction"),
+    title="Mushroom Edibility Classifier",
+    description="""
+    Predict if a mushroom is edible or poisonous using its 5 most important characteristics.
+    WARNING: This is a demonstration only. Never eat wild mushrooms based on this prediction!
+    """
+)
+
+if __name__ == "__main__":
+    iface.launch()