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FaheemKhan0817FaheemKhan0817
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updates in app.py for render
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‎app.py‎

Lines changed: 132 additions & 37 deletions
Original file line numberDiff line numberDiff line change
@@ -1,21 +1,143 @@
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# from flask import Flask, request, render_template, jsonify
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# import joblib
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# import numpy as np
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# import pandas as pd
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# from config.paths_config import MODEL_PATH, SCALER_PATH
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# from src.logger import get_logger
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# from src.feature_store import RedisFeatureStore
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# from alibi_detect.cd import KSDrift
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# import os
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# app = Flask(__name__, template_folder='templates', static_folder='static')
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# logger = get_logger(__name__)
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# # Redis configuration
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# REDIS_HOST = os.environ.get('REDIS_HOST', 'localhost')
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# REDIS_PORT = int(os.environ.get('REDIS_PORT', 6379))
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# REDIS_DB = int(os.environ.get('REDIS_DB', 0))
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# # Define the top 12 features
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# TOP_12_FEATURES = [
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# 'multiple_deliveries', 'Road_traffic_density', 'Vehicle_condition', 'Delivery_person_Ratings',
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# 'distance_deliveries', 'Weather_conditions', 'Festival', 'distance_traffic', 'distance',
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# 'Delivery_person_Age', 'prep_traffic', 'City'
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# ]
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# # Load model and scaler
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# try:
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# model = joblib.load(MODEL_PATH)
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# scaler = joblib.load(SCALER_PATH)
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# logger.info("Model and scaler loaded successfully from %s and %s", MODEL_PATH, SCALER_PATH)
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# except Exception as e:
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# logger.error(f"Error loading model or scaler: {e}")
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# raise
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# # Initialize RedisFeatureStore and fetch reference data
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# try:
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# feature_store = RedisFeatureStore(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB)
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# entity_ids = feature_store.get_all_entity_ids()
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# if not entity_ids:
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# raise ValueError("No reference data found in Redis")
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# batch_features = feature_store.get_batch_features(entity_ids[:1000])
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# reference_df = pd.DataFrame([features for features in batch_features.values()], columns=TOP_12_FEATURES)
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# reference_data = scaler.transform(reference_df)
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# logger.info(f"Fetched and transformed {len(reference_data)} reference rows from RedisFeatureStore")
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# except Exception as e:
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# logger.error(f"Error fetching reference data from Redis: {e}")
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# raise
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# # Mapping dictionaries for dropdowns
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# WEATHER_CONDITIONS = {'Sunny': 0, 'Cloudy': 1, 'Fog': 2, 'Sandstorms': 3, 'Stormy': 4, 'Windy': 5}
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# TRAFFIC_DENSITY = {'Low': 0, 'Medium': 1, 'High': 2, 'Jam': 3}
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# VEHICLE_CONDITION = {'Poor': 0, 'Good': 1, 'Excellent': 2}
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# FESTIVAL = {'No': 0, 'Yes': 1}
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# CITY = {'Urban': 0, 'Semi-Urban': 1, 'Metropolitan': 2}
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# # Initialize drift detector
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# ksd = KSDrift(x_ref=reference_data, p_val=0.05)
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# @app.route('/')
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# def index():
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# return render_template('index.html')
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# @app.route('/predict', methods=['POST'])
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# def predict():
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# try:
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# data = request.form.to_dict()
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# logger.info(f"Received form data: {data}")
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# def validate_dropdown(value, mapping, field_name):
70+
# if value not in mapping:
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# raise ValueError(f"Invalid value '{value}' for {field_name}. Valid options: {list(mapping.keys())}")
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# return mapping[value]
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# input_data = {
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# 'multiple_deliveries': float(data['multiple_deliveries']),
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# 'Road_traffic_density': validate_dropdown(data['Road_traffic_density'], TRAFFIC_DENSITY, 'Road_traffic_density'),
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# 'Vehicle_condition': validate_dropdown(data['Vehicle_condition'], VEHICLE_CONDITION, 'Vehicle_condition'),
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# 'Delivery_person_Ratings': float(data['Delivery_person_Ratings']),
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# 'distance_deliveries': float(data['distance_deliveries']),
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# 'Weather_conditions': validate_dropdown(data['Weather_conditions'], WEATHER_CONDITIONS, 'Weather_conditions'),
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# 'Festival': validate_dropdown(data['Festival'], FESTIVAL, 'Festival'),
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# 'distance_traffic': float(data['distance_traffic']),
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# 'distance': float(data['distance']),
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# 'Delivery_person_Age': float(data['Delivery_person_Age']),
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# 'prep_traffic': float(data['prep_traffic']),
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# 'City': validate_dropdown(data['City'], CITY, 'City')
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# }
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# df = pd.DataFrame([input_data], columns=TOP_12_FEATURES)
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# # Scale incoming data
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# features_scaled = scaler.transform(df)
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# # Data drift detection
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# drift_result = ksd.predict(features_scaled)
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# drift_detected = drift_result['data']['is_drift']
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# if drift_detected:
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# logger.warning(f"Data drift detected! p-values: {drift_result['data']['p_val']}")
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# else:
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# logger.info("No data drift detected.")
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# # Predict
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# prediction = float(model.predict(features_scaled)[0])
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# logger.info(f"Prediction: {prediction}")
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# # Return prediction and drift status
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# return jsonify({
107+
# 'prediction': round(prediction, 2),
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# 'drift_detected': bool(drift_detected),
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# 'drift_p_values': drift_result['data']['p_val'].tolist() # Convert to list for JSON
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# })
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# except KeyError as e:
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# logger.error(f"Missing field: {e}")
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# return jsonify({'error': f"Missing required field: {e}"}), 400
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# except ValueError as e:
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# logger.error(f"Invalid input: {e}")
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# return jsonify({'error': str(e)}), 400
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# except Exception as e:
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# logger.error(f"Error in prediction: {e}")
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# return jsonify({'error': 'Internal server error'}), 500
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# if __name__ == '__main__':
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# port = int(os.environ.get('PORT', 5000))
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# app.run(debug=True, host='0.0.0.0', port=port)
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############################################################################################################################################
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# for deployment purpose i am out of aws credits so i am removing data drift detection and redis feature store
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1130
from flask import Flask, request, render_template, jsonify
2131
import joblib
3132
import numpy as np
4133
import pandas as pd
5134
from config.paths_config import MODEL_PATH, SCALER_PATH
6135
from src.logger import get_logger
7-
from src.feature_store import RedisFeatureStore
8-
from alibi_detect.cd import KSDrift
9136
import os
10137

11138
app = Flask(__name__, template_folder='templates', static_folder='static')
12139
logger = get_logger(__name__)
13140

14-
# Redis configuration
15-
REDIS_HOST = os.environ.get('REDIS_HOST', 'localhost')
16-
REDIS_PORT = int(os.environ.get('REDIS_PORT', 6379))
17-
REDIS_DB = int(os.environ.get('REDIS_DB', 0))
18-
19141
# Define the top 12 features
20142
TOP_12_FEATURES = [
21143
'multiple_deliveries', 'Road_traffic_density', 'Vehicle_condition', 'Delivery_person_Ratings',
@@ -32,30 +154,13 @@
32154
logger.error(f"Error loading model or scaler: {e}")
33155
raise
34156

35-
# Initialize RedisFeatureStore and fetch reference data
36-
try:
37-
feature_store = RedisFeatureStore(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB)
38-
entity_ids = feature_store.get_all_entity_ids()
39-
if not entity_ids:
40-
raise ValueError("No reference data found in Redis")
41-
batch_features = feature_store.get_batch_features(entity_ids[:1000])
42-
reference_df = pd.DataFrame([features for features in batch_features.values()], columns=TOP_12_FEATURES)
43-
reference_data = scaler.transform(reference_df)
44-
logger.info(f"Fetched and transformed {len(reference_data)} reference rows from RedisFeatureStore")
45-
except Exception as e:
46-
logger.error(f"Error fetching reference data from Redis: {e}")
47-
raise
48-
49157
# Mapping dictionaries for dropdowns
50158
WEATHER_CONDITIONS = {'Sunny': 0, 'Cloudy': 1, 'Fog': 2, 'Sandstorms': 3, 'Stormy': 4, 'Windy': 5}
51159
TRAFFIC_DENSITY = {'Low': 0, 'Medium': 1, 'High': 2, 'Jam': 3}
52160
VEHICLE_CONDITION = {'Poor': 0, 'Good': 1, 'Excellent': 2}
53161
FESTIVAL = {'No': 0, 'Yes': 1}
54162
CITY = {'Urban': 0, 'Semi-Urban': 1, 'Metropolitan': 2}
55163

56-
# Initialize drift detector
57-
ksd = KSDrift(x_ref=reference_data, p_val=0.05)
58-
59164
@app.route('/')
60165
def index():
61166
return render_template('index.html')
@@ -90,23 +195,13 @@ def validate_dropdown(value, mapping, field_name):
90195
# Scale incoming data
91196
features_scaled = scaler.transform(df)
92197

93-
# Data drift detection
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drift_result = ksd.predict(features_scaled)
95-
drift_detected = drift_result['data']['is_drift']
96-
if drift_detected:
97-
logger.warning(f"Data drift detected! p-values: {drift_result['data']['p_val']}")
98-
else:
99-
logger.info("No data drift detected.")
100-
101198
# Predict
102199
prediction = float(model.predict(features_scaled)[0])
103200
logger.info(f"Prediction: {prediction}")
104201

105-
# Return prediction and drift status
202+
# Return prediction
106203
return jsonify({
107-
'prediction': round(prediction, 2),
108-
'drift_detected': bool(drift_detected),
109-
'drift_p_values': drift_result['data']['p_val'].tolist() # Convert to list for JSON
204+
'prediction': round(prediction, 2)
110205
})
111206
except KeyError as e:
112207
logger.error(f"Missing field: {e}")
@@ -120,4 +215,4 @@ def validate_dropdown(value, mapping, field_name):
120215

121216
if __name__ == '__main__':
122217
port = int(os.environ.get('PORT', 5000))
123-
app.run(debug=True, host='0.0.0.0', port=port)
218+
app.run(debug=True, host='0.0.0.0', port=port)

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