chore: init project

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.xlsx
+*.csv

Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+ PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

LICENSE.md

@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2022 Himesh Samarasekera
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

Procfile

@@ -0,0 +1 @@
+web: uvicorn main:app --host 0.0.0.0 --port $PORT

main.py

@@ -0,0 +1,161 @@
+#!/usr/bin/env python
+# encoding: utf-8
+
+from fastapi import FastAPI, Form, Depends
+from pydantic import BaseModel
+import numpy as np
+import pandas as pd
+from xgboost import XGBRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import RobustScaler
+from skforecast.ForecasterAutoreg import ForecasterAutoreg
+
+from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
+
+import joblib
+
+app = FastAPI()
+
+class Msg(BaseModel):
+    msg: str
+
+
+class RetrainRequest(BaseModel):
+    lag: int
+    differentiation: str
+    transformer: str
+    externalTransformation: str
+    test_size: int
+
+class PredictRequest(BaseModel):
+    steps: int
+    test_size: int
+    externalTransformation: str
+
+@app.get("/welcomeMessage")
+async def welcome():
+    return {"message": "Hello World. Welcome to FastAPI!"}
+
+@app.get("/")
+async def root():
+    return {"message": "Hello World"}
+
+def form_retrain(lag: str = Form(...), differentiation: str = Form(...), transformer: str = Form(...), externalTransformation: str = Form (...), test_size: str = Form(...)):
+    return RetrainRequest(lag=int(lag), differentiation=str(differentiation), transformer=transformer, externalTransformation=externalTransformation, test_size=int(test_size))
+
+def form_prediction(steps: str = Form(...), externalTransformation: str = Form (...), test_size: str = Form(...)):
+    return PredictRequest(steps=int(steps), externalTransformation=externalTransformation, test_size=int(test_size))
+
+def apply_transformation(data, transform_type):
+    if transform_type == 'Log':
+        return np.log1p(data)
+    elif transform_type == 'Square Root':
+        return np.sqrt(data)
+    else:
+        return data
+
+def reverse_transformation(transformed_data, transform_type):
+    if transform_type == 'Log':
+        return np.expm1(transformed_data)
+    elif transform_type == 'Square Root':
+        return np.square(transformed_data)
+    else:
+        return transformed_data
+
+@app.post("/retrain")
+async def retrain(requess: RetrainRequest = Depends(form_retrain), test_size: int = 3):
+    with open('ammonia_market_monthly_avg_new.xlsx', 'rb') as file:
+            df = pd.read_excel(file)
+    
+    df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')
+    df.set_index('date', inplace=True)
+
+    lags = requess.lag
+    differentiation = requess.differentiation
+    if differentiation == "0":
+        differentiation = None
+    else:
+        differentiation = int(differentiation)
+
+    transformer_y = requess.transformer
+    test_size = requess.test_size
+    externalTransformation=requess.externalTransformation
+    
+    target_column = 'southeast_asia'
+    train = df.iloc[:-(test_size)]
+    test = df.iloc[-(test_size):]
+
+    train_transformed = apply_transformation(train[target_column], externalTransformation)
+
+    if transformer_y == 'StandardScaler':
+        transformer_y = StandardScaler()
+    elif transformer_y == 'MinMaxScaler':
+        transformer_y = MinMaxScaler()
+    elif transformer_y == 'RobustScaler':
+        transformer_y = RobustScaler()
+    else:
+        transformer_y = None
+    
+    forecaster = ForecasterAutoreg(
+         regressor       = XGBRegressor(random_state=123),
+         lags            = lags,
+         differentiation = differentiation,
+         transformer_y   = transformer_y
+         )
+
+    forecaster.fit(y=train_transformed)
+    predictions = forecaster.predict(steps=test_size)
+    pred = reverse_transformation(predictions, externalTransformation)
+    
+    df_reset = df.reset_index()
+    last_date = df_reset.iloc[-(test_size)]['date']
+    months_ahead = pd.date_range(last_date, periods=test_size, freq='M')
+
+    preds = round(pred, 2).tolist()
+    actual = test[target_column]
+    date_value_pairs = dict(zip(months_ahead.tolist(), preds))
+    rmse = np.sqrt(mean_squared_error(actual, preds))
+    mape = mean_absolute_percentage_error(actual, preds)
+    joblib.dump(forecaster, filename='forecaster_new.py')
+
+    return {
+        'predictions': date_value_pairs,
+        'actual': actual,
+        'rmse': rmse,
+        'mape': mape
+    }
+
+@app.post("/predict")
+async def predict(requess: PredictRequest = Depends(form_prediction), steps: int = 3, externalTransformation: str = "None", test_size: int = 3):
+    try:
+        with open('forecaster_new.py', 'rb') as file:
+            forecaster_southeast = joblib.load(file)
+    except FileNotFoundError:
+        forecaster_southeast = joblib.load('forecaster_southeast.py')
+    
+    try:
+        with open('ammonia_market_monthly_avg_new.xlsx', 'rb') as file:
+            df = pd.read_excel(file)
+    except FileNotFoundError:
+        df = pd.read_excel('ammonia_market_monthly_avg_2010_2020.xlsx')
+    
+    df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')
+    df.set_index('date', inplace=True)
+
+    steps = requess.steps
+    test_size = requess.test_size
+    
+    predictions = forecaster_southeast.predict(steps=steps)
+    pred = reverse_transformation(predictions, requess.externalTransformation)
+    preds = round(pred, 2).tolist()
+    
+    df_reset = df.reset_index()
+    last_date = df_reset.iloc[-(test_size)]['date']
+    months_ahead = pd.date_range(last_date, periods=steps, freq='M')
+    date_value_pairs = dict(zip(months_ahead.tolist(), preds))
+
+    return {
+        "steps": steps,
+        "predictions": date_value_pairs
+    }

pyproject.toml

@@ -0,0 +1,17 @@
+[tool.poetry]
+name = "railway-fastapi-template"
+version = "0.1.0"
+description = "This example starts up a FastAPI server"
+authors = ["DeviousLab <deviouslab@gmail.com>"]
+license = "MIT"
+
+[tool.poetry.dependencies]
+python = "^3.10"
+fastapi = "^0.80.0"
+uvicorn = "^0.18.3"
+
+[tool.poetry.dev-dependencies]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

requirements.txt

@@ -0,0 +1,18 @@
+anyio>=3.7.1
+click==8.1.7
+fastapi==0.103.2
+h11==0.14.0
+httptools==0.6.0
+idna==3.4
+pydantic==2.4.2
+python-dotenv==1.0.0
+python-multipart==0.0.6
+PyYAML==6.0.1
+sniffio==1.3.0
+starlette>=0.27.0
+typing_extensions==4.8.0
+uvicorn==0.23.2
+watchfiles==0.20.0
+websockets==11.0.3
+scikit-learn
+jinja2