feat: Update example (#149)

* house price

* formatting

Author: JasonHonKL

examples/house_prices.py

@@ -19,28 +19,14 @@
 import pandas as pd
 
 import plexe
+from plexe import ModelBuilder
 from plexe.internal.common.provider import ProviderConfig
 from plexe.callbacks import MLFlowCallback
 
 
 # Step 1: Define the model
 # Note: for conciseness we leave the input schema empty and let plexe infer it
-model = plexe.Model(
-    intent=(
-        "With 79 explanatory variables describing aspects of residential homes in Ames, Iowa, predict "
-        "the final price of each home. Use only linear regression and decision tree models, no ensembling. "
-        "The models must be extremely simple and quickly trainable on extremely constrained hardware."
-    ),
-    output_schema={
-        "SalePrice": float,
-    },
-)
-
-# Step 2: Build the model using the training dataset
-# 2B: Build the model with the dataset
-# NOTE: In order to run this example, you will need to download the dataset from Kaggle
-model.build(
-    datasets=[pd.read_csv("examples/datasets/house-prices-train.csv")],
+model = ModelBuilder(
     provider=ProviderConfig(
         default_provider="openai/gpt-4o",
         orchestrator_provider="anthropic/claude-sonnet-4-20250514",
@@ -49,10 +35,25 @@
         ops_provider="anthropic/claude-3-7-sonnet-20250219",
         tool_provider="openai/gpt-4o",
     ),
+    verbose=False,
+)
+
+# Step 2: Build the model using the training dataset
+# 2B: Build the model with the dataset
+# NOTE: In order to run this example, you will need to download the dataset from Kaggle
+m = model.build(
+    datasets=[pd.read_csv("examples/datasets/house-prices-train.csv")],
+    intent=(
+        "With 79 explanatory variables describing aspects of residential homes in Ames, Iowa, predict "
+        "the final price of each home. Use only linear regression and decision tree models, no ensembling. "
+        "The models must be extremely simple and quickly trainable on extremely constrained hardware."
+    ),
+    output_schema={
+        "SalePrice": float,
+    },
     max_iterations=2,
     timeout=1800,  # 30 minute timeout
     run_timeout=180,
-    verbose=False,
     callbacks=[
         MLFlowCallback(
             tracking_uri="http://127.0.0.1:8080",
@@ -62,15 +63,15 @@
 )
 
 # Step 3: Save the model
-plexe.save_model(model, "house-prices.tar.gz")
+plexe.save_model(m, "house-prices.tar.gz")
 
 # Step 4: Run a prediction on the built model
 test_df = pd.read_csv("examples/datasets/house-prices-test.csv").sample(10)
-predictions = pd.DataFrame.from_records([model.predict(x) for x in test_df.to_dict(orient="records")])
+predictions = pd.DataFrame.from_records([m.predict(x) for x in test_df.to_dict(orient="records")])
 
 # Step 5: print a sample of predictions
 print(predictions)
 
 # Step 6: Print model description
-description = model.describe()
+description = m.describe()
 print(description.as_text())