Update app.py

app.py

@@ -2,74 +2,34 @@ import gradio as gr
 import pandas as pd
 import joblib
 from huggingface_hub import hf_hub_download
-import numpy as np
 
-# Download model and feature names from Hugging Face
-model_path = hf_hub_download(repo_id="alperugurcan/mercedes", filename="mercedes_model.joblib")
-feature_names_path = hf_hub_download(repo_id="alperugurcan/mercedes", filename="feature_names.joblib")
+model = joblib.load(hf_hub_download(repo_id="alperugurcan/mercedes", filename="mercedes_model.joblib"))
+feature_names = joblib.load(hf_hub_download(repo_id="alperugurcan/mercedes", filename="feature_names.joblib"))
 
-# Load the saved model and feature names
-model = joblib.load(model_path)
-feature_names = joblib.load(feature_names_path)
-
-# Most common X0 values with their frequencies
-FEATURE_OPTIONS = {
-    "z (Most Common - 360 cases)": "z",
-    "ak (349 cases)": "ak",
+CONFIGS = {
+    "z (360 cases)": "z",
+    "ak (349 cases)": "ak", 
     "y (324 cases)": "y",
     "ay (313 cases)": "ay",
     "t (306 cases)": "t",
     "x (300 cases)": "x",
     "o (269 cases)": "o",
-    "f (227 cases)": "f",
+    "f (227 cases)": "f", 
     "n (195 cases)": "n",
     "w (182 cases)": "w"
 }
 
-# Default values for other features
-DEFAULT_VALUES = {name: 0.0 for name in feature_names}
-
-def predict(selected_option):
-    try:
-        # Create a dictionary with all features set to default values
-        input_dict = DEFAULT_VALUES.copy()
-        
-        # Get the actual value from the selected option
-        selected_value = FEATURE_OPTIONS[selected_option]
-        
-        # Create dummy variable columns for X0
-        for val in set(FEATURE_OPTIONS.values()):
-            col_name = f'X0_{val}'
-            input_dict[col_name] = 1 if val == selected_value else 0
-        
-        # Create DataFrame with all features
-        df = pd.DataFrame([input_dict])
-        
-        # Make prediction
-        if hasattr(model, '_Booster'):
-            booster = model._Booster
-            prediction = booster.predict(df)[0]
-        else:
-            prediction = model.predict(df)[0]
-            
-        return f"Predicted manufacturing time: {prediction:.2f} seconds"
-    except Exception as e:
-        return f"Error in prediction: {str(e)}"
+def predict(option):
+    input_data = {name: 0.0 for name in feature_names}
+    input_data[f'X0_{CONFIGS[option]}'] = 1.0
+    prediction = model.predict(pd.DataFrame([input_data]))[0]
+    return f"Predicted manufacturing time: {prediction:.2f} seconds"
 
-# Create interface with dropdown
-interface = gr.Interface(
+gr.Interface(
     fn=predict,
-    inputs=gr.Dropdown(
-        choices=list(FEATURE_OPTIONS.keys()),
-        label="Select Manufacturing Configuration (X0)",
-        value=list(FEATURE_OPTIONS.keys())[0]
-    ),
-    outputs=gr.Textbox(label="Prediction Result"),
+    inputs=gr.Dropdown(choices=list(CONFIGS.keys()), label="Manufacturing Configuration"),
+    outputs=gr.Textbox(label="Prediction"),
     title="Mercedes-Benz Manufacturing Time Predictor",
-    description="Select one of the most common manufacturing configurations to predict the production time. The options are sorted by frequency of occurrence in the training data.",
-    examples=[[list(FEATURE_OPTIONS.keys())[0]]],
-    cache_examples=True,
+    description="Select a manufacturing configuration to predict production time.",
     theme=gr.themes.Soft()
-)
-
-interface.launch(debug=True)
+).launch(debug=True)