-
Notifications
You must be signed in to change notification settings - Fork 0
/
app.py
53 lines (40 loc) · 1.79 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
from flask import Flask, request, render_template
import numpy as np
import pandas as pd
from src.pipeline.predict_pipeline import CustomData, PredictPipeline
from src.pipeline.train_pipeline import TrainPipeline
import os
from src.logger import logging
app = Flask(__name__)
# Directory to store pre-trained models
PRETRAINED_MODEL_DIR = 'artifacts'
# Route for the home page
@app.route('/')
def home():
return render_template('home.html')
@app.route('/predictdata', methods=['GET', 'POST'])
def predict_datapoint():
if request.method == 'GET':
return render_template('home.html')
else:
# Get ticker and model_name from form
ticker = request.form.get('ticker')
model_name = request.form.get('model_name', default='LSTM') # LSTM is default if no model is selected
# Check if the model for the selected ticker is already trained
model_file_path = os.path.join(PRETRAINED_MODEL_DIR, f'{model_name}_{ticker}.keras')
# if os.path.exists(model_file_path):
# Model exists, proceed to prediction
logging.info(f"Model for {ticker} already exists. Proceeding with prediction.")
# Initialize the custom data and predict pipeline
# data = CustomData(ticker=ticker, model_name=model_name)
# pred_df = data.get_data_as_data_frame()
# Initialize PredictPipeline
predict_pipeline = PredictPipeline()
# Perform prediction
logging.info("Running prediction...")
results = predict_pipeline.predict_next_day(symbol=ticker, model_type=model_name)
logging.info(f"Prediction results: {results}")
return render_template('home.html', results=results)
if __name__ == "__main__":
app.run(host="0.0.0.0", debug=True) # for deployment
# app.run(debug=True, port=5000)