Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+# 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")
+
+# Load the saved model and feature names
+model = joblib.load(model_path)
+feature_names = joblib.load(feature_names_path)
+
+def predict(*features):
+    # Create a DataFrame with the input features
+    df = pd.DataFrame([features], columns=feature_names)
+    
+    # Make prediction
+    prediction = model.predict(df)[0]
+    return f"Predicted time: {prediction:.2f}"
+
+# Create the interface
+inputs = [gr.Number(label=f"Feature {i+1}") for i in range(len(feature_names))]
+output = gr.Textbox(label="Prediction")
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=output,
+    title="Mercedes-Benz Manufacturing Time Prediction",
+    description="Enter feature values to predict manufacturing time"
+)
+
+interface.launch()