refactor code into app and utils script

.gitignore

@@ -135,3 +135,5 @@ notebooks/data_extraction.ipynb
 notebooks/predict_model.ipynb
 notebooks/train_model.ipynb
 notebooks/clustering.ipynb
+
+*.db

src/app/app.py

@@ -2,48 +2,24 @@
 from pydantic import BaseModel
 import os
 from dotenv import load_dotenv
-from pathlib import Path
-import openai
-from sqlalchemy.engine import create_engine
 import pandas as pd
+from pathlib import Path
+from .app_utils import Prompter, init_database, data_cleaner
 
-class Prompter:
- def __init__(self, api_key, gpt_model):
- if not api_key:
- raise Exception("Please provide the OpenAI API key")
-
-
- openai.api_key = os.environ.get("OPENAI_API_KEY")
-
- self.gpt_model = gpt_model
-
- def prompt_model_return(self, messages: list):
- response = openai.ChatCompletion.create(model=self.gpt_model, 
- messages=messages,
- temperature=0.2)
- return response["choices"][0]["message"]["content"]
-
-def data_cleaner(df):
- df.columns = df.columns.str.replace('.', '_')
-
- # Replace the character '/' with '_per_' all entries
- df.columns = df.columns.str.replace('/', '_per_')
-
- df.columns = df.columns.str.replace('(', '_')
+def init_data():
+ # Set the path to the raw data
+ # Convert the current working directory to a Path object
+ script_dir = Path(os.getcwd())
+ predicted_data_path = script_dir / 'data' / 'predicted-data' / 'vehicle_data_with_clusters.csv'
 
- df.columns = df.columns.str.replace(')', '_')
-
- # drop column hybrid_in_fuel hybrid_in_electric aggregate_levels vehicle_type_cat
- df = df.drop(['hybrid_in_fuel', 'hybrid_in_electric', 'aggregate_levels','transmission_','fuel_type'], axis=1)
-
- return df
-
-def init_database(df):
- # Set up engine
- engine = create_engine("sqlite:https://")
- df.to_sql("vehicleDB", engine)
+ # Load the CSV file into a DataFrame
+ dirty_df = pd.read_csv(predicted_data_path)
+ global df
+ df = data_cleaner(dirty_df)
+ global sample_values
+ sample_values = {df.columns[i]: df.values[0][i] for i in range(len(df.columns))}
 
- return engine
+ return df, sample_values
 
 app = FastAPI()
 
@@ -66,17 +42,7 @@ async def startup_event():
  global prompter
  prompter = Prompter(openai_api_key, "gpt-3.5-turbo")
 
- # Set the path to the raw data
- # Convert the current working directory to a Path object
- script_dir = Path(os.getcwd())
- predicted_data_path = script_dir / 'data' / 'predicted-data' / 'vehicle_data_with_clusters.csv'
-
- # Load the CSV file into a DataFrame
- dirty_df = pd.read_csv(predicted_data_path)
- global df
- df = data_cleaner(dirty_df)
- global sample_values
- sample_values = {df.columns[i]: df.values[0][i] for i in range(len(df.columns))}
+ df, sample_values = init_data()
 
  # Set up engine
  global engine

src/app/app_utils.py

@@ -0,0 +1,43 @@
+import pandas as pd
+import os
+import openai
+from sqlalchemy.engine import create_engine
+from pathlib import Path
+
+class Prompter:
+ def __init__(self, api_key, gpt_model):
+ if not api_key:
+ raise Exception("Please provide the OpenAI API key")
+
+
+ openai.api_key = os.environ.get("OPENAI_API_KEY")
+
+ self.gpt_model = gpt_model
+
+ def prompt_model_return(self, messages: list):
+ response = openai.ChatCompletion.create(model=self.gpt_model, 
+ messages=messages,
+ temperature=0.2)
+ return response["choices"][0]["message"]["content"]
+
+def data_cleaner(df):
+
+
+ # Replace the characters '.', '/', '(' and ')'with '_per_' all entries
+ df.columns = df.columns.str.replace('.', '_')
+ df.columns = df.columns.str.replace('/', '_per_')
+ df.columns = df.columns.str.replace('(', '_')
+ df.columns = df.columns.str.replace(')', '_')
+
+ # drop column hybrid_in_fuel hybrid_in_electric aggregate_levels vehicle_type_cat
+ df = df.drop(['hybrid_in_fuel', 'hybrid_in_electric', 'aggregate_levels','transmission_','fuel_type'], axis=1)
+
+ return df
+
+def init_database(df):
+ # Set up engine
+ engine = create_engine("sqlite:https://")
+ df.to_sql("vehicleDB", engine)
+
+ return engine
+