diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
new file mode 100644
index 0000000000..2c95aff3b9
--- /dev/null
+++ b/.github/workflows/python-app.yml
@@ -0,0 +1,33 @@
+name: Python application
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python 3.8
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.8
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install flake8 pytest
+        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - name: Lint with flake8
+      run: |
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        pytest
diff --git a/README.md b/README.md
index 6c090c8179..9620c89ce3 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,7 @@
 Working in a command line environment is recommended for ease of use with git and dvc. If on Windows, WSL1 or 2 is recommended.
 
+Link: https://github.com/chavelei/Deploying-a-Scalable-ML-Pipeline-with-FastAPI
+
 # Environment Set up (pip or conda)
 * Option 1: use the supplied file `environment.yml` to create a new environment with conda
 * Option 2: use the supplied file `requirements.txt` to create a new environment with pip
diff --git a/local_api.py b/local_api.py
index a3bff2f988..065d19ad63 100644
--- a/local_api.py
+++ b/local_api.py
@@ -3,12 +3,12 @@
 import requests
 
 # TODO: send a GET using the URL http://127.0.0.1:8000
-r = None # Your code here
+r = requests.get("http://127.0.0.1:8000")
 
 # TODO: print the status code
-# print()
+print(r.status_code)
 # TODO: print the welcome message
-# print()
+print(r.text)
 
 
 
@@ -30,9 +30,9 @@
 }
 
 # TODO: send a POST using the data above
-r = None # Your code here
+r = requests.post("http://127.0.0.1:8000/data/", json=data)
 
 # TODO: print the status code
-# print()
+print(r.status_code)
 # TODO: print the result
-# print()
+print(r.text)
diff --git a/main.py b/main.py
index 638e2414de..ce5e806f3c 100644
--- a/main.py
+++ b/main.py
@@ -26,21 +26,21 @@ class Data(BaseModel):
     hours_per_week: int = Field(..., example=40, alias="hours-per-week")
     native_country: str = Field(..., example="United-States", alias="native-country")
 
-path = None # TODO: enter the path for the saved encoder 
+path = "../Deploying-a-Scalable-ML-Pipeline-with-FastAPI/model/encoder.pkl"
 encoder = load_model(path)
 
-path = None # TODO: enter the path for the saved model 
+path = "../Deploying-a-Scalable-ML-Pipeline-with-FastAPI/model/model.pkl"
 model = load_model(path)
 
 # TODO: create a RESTful API using FastAPI
-app = None # your code here
+app = FastAPI()
 
 # TODO: create a GET on the root giving a welcome message
 @app.get("/")
 async def get_root():
     """ Say hello!"""
     # your code here
-    pass
+    return {"message": "Welcome to the ML model API!"}
 
 
 # TODO: create a POST on a different path that does model inference
@@ -69,6 +69,11 @@ async def post_inference(data: Data):
         # use data as data input
         # use training = False
         # do not need to pass lb as input
-    )
-    _inference = None # your code here to predict the result using data_processed
+        data,
+        categorical_features=cat_features,
+        encoder=encoder,
+        training=False,
+        label=None
+    ) 
+    _inference = inference(model, data_processed)
     return {"result": apply_label(_inference)}
diff --git a/ml/model.py b/ml/model.py
index f361110f18..5c60bf129f 100644
--- a/ml/model.py
+++ b/ml/model.py
@@ -1,6 +1,8 @@
 import pickle
 from sklearn.metrics import fbeta_score, precision_score, recall_score
 from ml.data import process_data
+from sklearn.ensemble import RandomForestClassifier  # Example model, can be replaced with any other model
+import pandas as pd
 # TODO: add necessary import
 
 # Optional: implement hyperparameter tuning.
@@ -20,8 +22,9 @@ def train_model(X_train, y_train):
         Trained machine learning model.
     """
     # TODO: implement the function
-    pass
-
+    model = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
+    model.fit(X_train, y_train)
+    return model
 
 def compute_model_metrics(y, preds):
     """
@@ -60,7 +63,7 @@ def inference(model, X):
         Predictions from the model.
     """
     # TODO: implement the function
-    pass
+    return model.predict(X)
 
 def save_model(model, path):
     """ Serializes model to a file.
@@ -73,12 +76,14 @@ def save_model(model, path):
         Path to save pickle file.
     """
     # TODO: implement the function
-    pass
+    with open(path, 'wb') as f:
+        pickle.dump(model, f)
 
 def load_model(path):
     """ Loads pickle file from `path` and returns it."""
     # TODO: implement the function
-    pass
+    with open(path, 'rb') as f:
+        return pickle.load(f)
 
 
 def performance_on_categorical_slice(
@@ -118,11 +123,18 @@ def performance_on_categorical_slice(
 
     """
     # TODO: implement the function
+    data_slice = data[data[column_name] == slice_value]
     X_slice, y_slice, _, _ = process_data(
         # your code here
         # for input data, use data in column given as "column_name", with the slice_value 
         # use training = False
+        data_slice,
+        categorical_features=categorical_features,
+        label=label,
+        training=False,
+        encoder=encoder,
+        lb=lb
     )
-    preds = None # your code here to get prediction on X_slice using the inference function
+    preds = inference(model, X_slice)
     precision, recall, fbeta = compute_model_metrics(y_slice, preds)
     return precision, recall, fbeta
diff --git a/model/encoder.pkl b/model/encoder.pkl
new file mode 100644
index 0000000000..82f96fb9b2
Binary files /dev/null and b/model/encoder.pkl differ
diff --git a/model/model.pkl b/model/model.pkl
new file mode 100644
index 0000000000..31daf008a6
Binary files /dev/null and b/model/model.pkl differ
diff --git a/model_card_template.md b/model_card_template.md
index 0392f3b9eb..a4b82fd2e3 100644
--- a/model_card_template.md
+++ b/model_card_template.md
@@ -3,16 +3,40 @@
 For additional information see the Model Card paper: https://arxiv.org/pdf/1810.03993.pdf
 
 ## Model Details
+This classification model was trained using the 1994 Census Bureau dataset from the UCI Machine Learning Repository (https://archive.ics.uci.edu/dataset/20/census+income). The goal is to predict whether an individual’s annual income exceeds $50,000 based on a set of demographic and socio-economic features, including:
+* Sex
+* Race
+* Marital status
+* Age
+* Native country
+* Education
+* Relationship status
+* Occupation
+* Hours worked per week
+* Work class
+* Capital gain
+* Capital loss
 
 ## Intended Use
+Primary use: Predicting income category for individuals based on demographic and economic data.
+Not intended for: Making real-world financial, hiring, or legal decisions without thorough fairness and bias evaluation.
 
 ## Training Data
+Source: 1994 Census Bureau dataset (UCI Machine Learning Repository).
+Size: 48,842 records after preprocessing.
 
 ## Evaluation Data
 
 ## Metrics
-_Please include the metrics used and your model's performance on those metrics._
+Precision : 0.7807 | Recall: 0.5379 | F1: 0.6369
 
 ## Ethical Considerations
+Dataset reflects social and economic patterns from 1994, which may not represent current demographics or job markets.
+Potential bias in predictions related to sensitive attributes such as race, sex, or marital status.
+Misuse could perpetuate existing inequalities if deployed in sensitive decision-making contexts.
 
 ## Caveats and Recommendations
+Model performance may degrade on modern census or employment datasets without retraining.
+Bias analysis should be conducted before deployment.
+Should not be the sole decision-making tool in critical domains such as hiring or lending.
+
diff --git a/screenshots/continuous_integration.png b/screenshots/continuous_integration.png
new file mode 100644
index 0000000000..9f5d33b634
Binary files /dev/null and b/screenshots/continuous_integration.png differ
diff --git a/screenshots/local_api.png b/screenshots/local_api.png
new file mode 100644
index 0000000000..e0052c2528
Binary files /dev/null and b/screenshots/local_api.png differ
diff --git a/screenshots/unit_test.png b/screenshots/unit_test.png
new file mode 100644
index 0000000000..99b48a2611
Binary files /dev/null and b/screenshots/unit_test.png differ
diff --git a/slice_output.txt b/slice_output.txt
new file mode 100644
index 0000000000..2f7cc9ce34
--- /dev/null
+++ b/slice_output.txt
@@ -0,0 +1,202 @@
+workclass: ?, Count: 435
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+workclass: Federal-gov, Count: 237
+Precision: 0.7800 | Recall: 0.4062 | F1: 0.5342
+workclass: Local-gov, Count: 548
+Precision: 0.7664 | Recall: 0.6325 | F1: 0.6931
+workclass: Never-worked, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+workclass: Private, Count: 5,664
+Precision: 0.8034 | Recall: 0.5082 | F1: 0.6226
+workclass: Self-emp-inc, Count: 272
+Precision: 0.8209 | Recall: 0.7237 | F1: 0.7692
+workclass: Self-emp-not-inc, Count: 651
+Precision: 0.6331 | Recall: 0.5087 | F1: 0.5641
+workclass: State-gov, Count: 327
+Precision: 0.8049 | Recall: 0.6875 | F1: 0.7416
+workclass: Without-pay, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: 10th, Count: 236
+Precision: 1.0000 | Recall: 0.1364 | F1: 0.2400
+education: 11th, Count: 298
+Precision: 1.0000 | Recall: 0.2778 | F1: 0.4348
+education: 12th, Count: 110
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+education: 1st-4th, Count: 44
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: 5th-6th, Count: 81
+Precision: 1.0000 | Recall: 0.2500 | F1: 0.4000
+education: 7th-8th, Count: 164
+Precision: 1.0000 | Recall: 0.4286 | F1: 0.6000
+education: 9th, Count: 128
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+education: Assoc-acdm, Count: 280
+Precision: 0.9286 | Recall: 0.3133 | F1: 0.4685
+education: Assoc-voc, Count: 330
+Precision: 1.0000 | Recall: 0.2386 | F1: 0.3853
+education: Bachelors, Count: 1,327
+Precision: 0.7136 | Recall: 0.8412 | F1: 0.7722
+education: Doctorate, Count: 107
+Precision: 0.8052 | Recall: 0.8267 | F1: 0.8158
+education: HS-grad, Count: 2,594
+Precision: 0.9737 | Recall: 0.1878 | F1: 0.3149
+education: Masters, Count: 444
+Precision: 0.7669 | Recall: 0.8608 | F1: 0.8111
+education: Preschool, Count: 10
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+education: Prof-school, Count: 151
+Precision: 0.8378 | Recall: 0.8942 | F1: 0.8651
+education: Some-college, Count: 1,837
+Precision: 0.9072 | Recall: 0.2543 | F1: 0.3973
+marital-status: Divorced, Count: 1,089
+Precision: 1.0000 | Recall: 0.2593 | F1: 0.4118
+marital-status: Married-AF-spouse, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+marital-status: Married-civ-spouse, Count: 3,730
+Precision: 0.7671 | Recall: 0.5837 | F1: 0.6630
+marital-status: Married-spouse-absent, Count: 122
+Precision: 0.6667 | Recall: 0.2857 | F1: 0.4000
+marital-status: Never-married, Count: 2,698
+Precision: 1.0000 | Recall: 0.2727 | F1: 0.4286
+marital-status: Separated, Count: 235
+Precision: 1.0000 | Recall: 0.2857 | F1: 0.4444
+marital-status: Widowed, Count: 262
+Precision: 1.0000 | Recall: 0.3571 | F1: 0.5263
+occupation: ?, Count: 437
+Precision: 0.6923 | Recall: 0.4286 | F1: 0.5294
+occupation: Adm-clerical, Count: 933
+Precision: 0.7792 | Recall: 0.4286 | F1: 0.5530
+occupation: Armed-Forces, Count: 2
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Craft-repair, Count: 1,040
+Precision: 0.8088 | Recall: 0.2455 | F1: 0.3767
+occupation: Exec-managerial, Count: 1,029
+Precision: 0.8138 | Recall: 0.6632 | F1: 0.7308
+occupation: Farming-fishing, Count: 245
+Precision: 0.5789 | Recall: 0.4400 | F1: 0.5000
+occupation: Handlers-cleaners, Count: 333
+Precision: 1.0000 | Recall: 0.3200 | F1: 0.4848
+occupation: Machine-op-inspct, Count: 513
+Precision: 0.7222 | Recall: 0.2031 | F1: 0.3171
+occupation: Other-service, Count: 817
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+occupation: Priv-house-serv, Count: 33
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+occupation: Prof-specialty, Count: 1,044
+Precision: 0.7742 | Recall: 0.7742 | F1: 0.7742
+occupation: Protective-serv, Count: 179
+Precision: 0.7143 | Recall: 0.3448 | F1: 0.4651
+occupation: Sales, Count: 885
+Precision: 0.7429 | Recall: 0.5556 | F1: 0.6357
+occupation: Tech-support, Count: 244
+Precision: 0.8421 | Recall: 0.4444 | F1: 0.5818
+occupation: Transport-moving, Count: 407
+Precision: 0.7727 | Recall: 0.2125 | F1: 0.3333
+relationship: Husband, Count: 3,281
+Precision: 0.7648 | Recall: 0.5909 | F1: 0.6667
+relationship: Not-in-family, Count: 2,084
+Precision: 1.0000 | Recall: 0.2778 | F1: 0.4348
+relationship: Other-relative, Count: 251
+Precision: 1.0000 | Recall: 0.1111 | F1: 0.2000
+relationship: Own-child, Count: 1,228
+Precision: 1.0000 | Recall: 0.3333 | F1: 0.5000
+relationship: Unmarried, Count: 895
+Precision: 0.9375 | Recall: 0.2778 | F1: 0.4286
+relationship: Wife, Count: 402
+Precision: 0.7836 | Recall: 0.5469 | F1: 0.6442
+race: Amer-Indian-Eskimo, Count: 79
+Precision: 0.8333 | Recall: 0.5000 | F1: 0.6250
+race: Asian-Pac-Islander, Count: 269
+Precision: 0.6207 | Recall: 0.6545 | F1: 0.6372
+race: Black, Count: 784
+Precision: 0.7347 | Recall: 0.3871 | F1: 0.5070
+race: Other, Count: 78
+Precision: 0.5000 | Recall: 0.3750 | F1: 0.4286
+race: White, Count: 6,931
+Precision: 0.7913 | Recall: 0.5431 | F1: 0.6441
+sex: Female, Count: 2,752
+Precision: 0.8293 | Recall: 0.4518 | F1: 0.5849
+sex: Male, Count: 5,389
+Precision: 0.7739 | Recall: 0.5537 | F1: 0.6456
+native-country: ?, Count: 138
+Precision: 0.6774 | Recall: 0.6774 | F1: 0.6774
+native-country: Cambodia, Count: 1
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Canada, Count: 27
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: China, Count: 25
+Precision: 0.6667 | Recall: 1.0000 | F1: 0.8000
+native-country: Columbia, Count: 16
+Precision: 0.0000 | Recall: 1.0000 | F1: 0.0000
+native-country: Cuba, Count: 18
+Precision: 1.0000 | Recall: 0.7143 | F1: 0.8333
+native-country: Dominican-Republic, Count: 16
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Ecuador, Count: 7
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: El-Salvador, Count: 35
+Precision: 1.0000 | Recall: 0.6667 | F1: 0.8000
+native-country: England, Count: 22
+Precision: 0.7500 | Recall: 0.6667 | F1: 0.7059
+native-country: France, Count: 7
+Precision: 0.6667 | Recall: 0.5000 | F1: 0.5714
+native-country: Germany, Count: 33
+Precision: 0.8750 | Recall: 0.5833 | F1: 0.7000
+native-country: Greece, Count: 7
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Guatemala, Count: 23
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Haiti, Count: 14
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Honduras, Count: 5
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Hong, Count: 4
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Hungary, Count: 3
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: India, Count: 25
+Precision: 0.5556 | Recall: 0.5556 | F1: 0.5556
+native-country: Iran, Count: 13
+Precision: 0.6667 | Recall: 0.8000 | F1: 0.7273
+native-country: Ireland, Count: 2
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Italy, Count: 18
+Precision: 0.6000 | Recall: 0.4286 | F1: 0.5000
+native-country: Jamaica, Count: 24
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Japan, Count: 17
+Precision: 1.0000 | Recall: 0.4000 | F1: 0.5714
+native-country: Laos, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Mexico, Count: 161
+Precision: 1.0000 | Recall: 0.4286 | F1: 0.6000
+native-country: Nicaragua, Count: 11
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Outlying-US(Guam-USVI-etc), Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Peru, Count: 8
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Philippines, Count: 50
+Precision: 0.7500 | Recall: 0.6000 | F1: 0.6667
+native-country: Poland, Count: 14
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: Portugal, Count: 11
+Precision: 1.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Puerto-Rico, Count: 23
+Precision: 0.5000 | Recall: 0.5000 | F1: 0.5000
+native-country: Scotland, Count: 5
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: South, Count: 22
+Precision: 0.1667 | Recall: 1.0000 | F1: 0.2857
+native-country: Taiwan, Count: 9
+Precision: 0.5000 | Recall: 1.0000 | F1: 0.6667
+native-country: Thailand, Count: 6
+Precision: 1.0000 | Recall: 0.5000 | F1: 0.6667
+native-country: Trinadad&Tobago, Count: 6
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
+native-country: United-States, Count: 7,280
+Precision: 0.7899 | Recall: 0.5343 | F1: 0.6374
+native-country: Vietnam, Count: 19
+Precision: 0.0000 | Recall: 0.0000 | F1: 0.0000
+native-country: Yugoslavia, Count: 4
+Precision: 1.0000 | Recall: 1.0000 | F1: 1.0000
diff --git a/test_ml.py b/test_ml.py
index 5f8306f14c..3f09f8a91a 100644
--- a/test_ml.py
+++ b/test_ml.py
@@ -1,28 +1,30 @@
+import os
+import pandas as pd
 import pytest
-# TODO: add necessary import
 
-# TODO: implement the first test. Change the function name and input as needed
-def test_one():
+# Load dataset once for all tests
+PROJECT_PATH = "../Deploying-a-Scalable-ML-Pipeline-with-FastAPI"
+DATA_PATH = os.path.join(PROJECT_PATH, "data", "census.csv")
+data = pd.read_csv(DATA_PATH)
+MODEL_PATH = os.path.join(PROJECT_PATH, "model", "model.pkl")
+ENCODER_PATH = os.path.join(PROJECT_PATH, "model", "encoder.pkl")
+
+def test_pickles_exist():
     """
-    # add description for the first test
+    Test that encoder.pkl and model.pkl files were created.
     """
-    # Your code here
-    pass
-
+    assert os.path.exists(MODEL_PATH), "model.pkl does not exist"
+    assert os.path.exists(ENCODER_PATH), "encoder.pkl does not exist"
 
-# TODO: implement the second test. Change the function name and input as needed
-def test_two():
+def test_age_range():
     """
-    # add description for the second test
+    Test that all ages are between 0 and 100.
     """
-    # Your code here
-    pass
+    assert data["age"].between(0, 100).all()
 
-
-# TODO: implement the third test. Change the function name and input as needed
-def test_three():
+def test_hours_per_week_range():
     """
-    # add description for the third test
+    Test that hours worked per week are between 1 and 99.
     """
-    # Your code here
-    pass
+    assert data["hours-per-week"].between(1, 99).all()
+
diff --git a/train_model.py b/train_model.py
index ae783ed5b9..db7198b5c0 100644
--- a/train_model.py
+++ b/train_model.py
@@ -13,14 +13,14 @@
     train_model,
 )
 # TODO: load the cencus.csv data
-project_path = "Your path here"
+project_path = "../Deploying-a-Scalable-ML-Pipeline-with-FastAPI"
 data_path = os.path.join(project_path, "data", "census.csv")
 print(data_path)
-data = None # your code here
+data = pd.read_csv(data_path)
 
 # TODO: split the provided data to have a train dataset and a test dataset
 # Optional enhancement, use K-fold cross validation instead of a train-test split.
-train, test = None, None# Your code here
+train, test = train_test_split(data, test_size=0.25)
 
 # DO NOT MODIFY
 cat_features = [
@@ -40,6 +40,10 @@
     # use the train dataset 
     # use training=True
     # do not need to pass encoder and lb as input
+    train,
+    categorical_features=cat_features,
+    label="salary",
+    training=True
     )
 
 X_test, y_test, _, _ = process_data(
@@ -52,7 +56,7 @@
 )
 
 # TODO: use the train_model function to train the model on the training dataset
-model = None # your code here
+model = train_model(X_train, y_train)
 
 # save the model and the encoder
 model_path = os.path.join(project_path, "model", "model.pkl")
@@ -66,7 +70,7 @@
 ) 
 
 # TODO: use the inference function to run the model inferences on the test dataset.
-preds = None # your code here
+preds = inference(model, X_test)
 
 # Calculate and print the metrics
 p, r, fb = compute_model_metrics(y_test, preds)
@@ -81,6 +85,14 @@
         p, r, fb = performance_on_categorical_slice(
             # your code here
             # use test, col and slicevalue as part of the input
+            test,
+            column_name=col,
+            slice_value=slicevalue,
+            categorical_features=cat_features,
+            label="salary",
+            encoder=encoder,
+            lb=lb,
+            model=model
         )
         with open("slice_output.txt", "a") as f:
             print(f"{col}: {slicevalue}, Count: {count:,}", file=f)