I am working on a basic, personal ML project on predicting weather. First, I have finished working on Jupyter Notebook. And now, I am transforming it into create a Flask app.
I have just completed the code of my Flask app. It's working fine on localhost. But I am not sure if I have done everything in the right way. Would you please review my code on Github to see if I am doing everything right?
https://github.com/SteveAustin583/weather-prediction-flask
Here is my code on app.py file:
import os
from flask import Flask, request, jsonify, render_template
import joblib
import pandas as pd
import numpy as np
# Initialize Flask app
app = Flask(__name__)
# --- Load Model Artifacts ---
# Define paths to the model artifacts
# Ensure these paths are correct relative to app.py
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_ARTIFACTS_DIR = os.path.join(BASE_DIR, 'model_artifacts')
MODEL_PATH = os.path.join(MODEL_ARTIFACTS_DIR, 'weather_prediction_model.joblib')
ENCODER_PATH = os.path.join(MODEL_ARTIFACTS_DIR, 'weather_label_encoder.joblib')
FEATURES_PATH = os.path.join(MODEL_ARTIFACTS_DIR, 'classifier_feature_names.joblib')
# Load the artifacts
try:
model = joblib.load(MODEL_PATH)
label_encoder = joblib.load(ENCODER_PATH)
# These are the feature names the model was trained on (e.g., ['temp_min', 'temp_max', 'precipitation', 'wind'])
expected_features = joblib.load(FEATURES_PATH)
print(f"Model, Label Encoder, and Feature List loaded successfully from {MODEL_ARTIFACTS_DIR}")
print(f"Expected features for prediction: {expected_features}")
except FileNotFoundError as e:
print(f"Error loading model artifacts: {e}")
print("Please ensure 'weather_prediction_model.joblib', 'weather_label_encoder.joblib', and 'classifier_feature_names.joblib' are in the 'model_artifacts' directory.")
model = None
label_encoder = None
expected_features = None
except Exception as e:
print(f"An unexpected error occurred during artifact loading: {e}")
model = None
label_encoder = None
expected_features = None
# --- Flask Routes ---
@app.route('/', methods=['GET', 'POST'])
def index():
prediction_text = None
error_text = None
input_data_for_template = {feature: "" for feature in expected_features} if expected_features else {}
if request.method == 'POST':
if not model or not label_encoder or not expected_features:
error_text = "Model artifacts not loaded. Cannot make a prediction."
return render_template('index.html', prediction_text=prediction_text, error_text=error_text, input_data=input_data_for_template)
try:
# Get data from form
form_data = request.form.to_dict()
input_data_for_template = form_data.copy() # Store for re-populating form
# Validate and prepare features
feature_values = []
missing_features = []
type_errors = []
for feature_name in expected_features:
if feature_name not in form_data or form_data[feature_name] == '':
missing_features.append(feature_name)
continue
try:
feature_values.append(float(form_data[feature_name]))
except ValueError:
type_errors.append(f"Feature '{feature_name}' must be a number.")
if missing_features:
error_text = f"Missing input for: {', '.join(missing_features)}."
elif type_errors:
error_text = " ".join(type_errors)
else:
# Create DataFrame for prediction (model expects 2D array)
input_df = pd.DataFrame([feature_values], columns=expected_features)
# Make prediction
encoded_prediction = model.predict(input_df)
predicted_weather_category = label_encoder.inverse_transform(encoded_prediction)
prediction_text = f"Predicted Weather: {predicted_weather_category[0]}"
except Exception as e:
error_text = f"Error during prediction: {str(e)}"
print(f"Prediction error: {e}")
return render_template('index.html', prediction_text=prediction_text, error_text=error_text, input_data=input_data_for_template, expected_features=expected_features)
@app.route('/api/predict', methods=['POST'])
def api_predict():
if not model or not label_encoder or not expected_features:
return jsonify({'error': 'Model artifacts not loaded. Cannot make a prediction.'}), 500
try:
data = request.get_json(force=True) # Get data posted as JSON
# Validate and prepare features
feature_values = []
missing_features = []
type_errors = []
for feature_name in expected_features:
if feature_name not in data:
missing_features.append(feature_name)
continue
try:
feature_values.append(float(data[feature_name]))
except (TypeError, ValueError): # Handles if value is not a number or None
type_errors.append(f"Feature '{feature_name}' must be a valid number.")
if missing_features:
return jsonify({'error': f"Missing features in JSON payload: {', '.join(missing_features)}"}), 400
if type_errors:
return jsonify({'error': " ".join(type_errors)}), 400
# Create DataFrame for prediction
input_df = pd.DataFrame([feature_values], columns=expected_features)
# Make prediction
encoded_prediction = model.predict(input_df)
predicted_weather_category = label_encoder.inverse_transform(encoded_prediction)
return jsonify({'predicted_weather': predicted_weather_category[0]})
except Exception as e:
print(f"API Prediction error: {e}")
return jsonify({'error': f'Error processing request: {str(e)}'}), 500
# --- Run the App ---
if __name__ == '__main__':
# Check if artifacts loaded correctly before trying to run
if model and label_encoder and expected_features:
app.run(debug=True, port=5000) # Runs on http://127.0.0.1:5000/
else:
print("Application cannot start due to missing model artifacts. Please check the 'model_artifacts' directory and error messages.")
You can find my notebook right here:
