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from flask import Flask, render_template, request, redirect, url_for
from joblib import load
import pandas as pd
import re
from customFunctions import *
import json
import datetime
pd.set_option('display.max_colwidth', 1000)
PIPELINES = [
{
'id': 1,
'name': 'Baseline',
'pipeline': load("pipeline_ex1_s1.joblib")
},
{
'id': 2,
'name': 'Trained on a FeedForward NN',
'pipeline': load("pipeline_ex1_s2.joblib")
},
{
'id': 3,
'name': 'Trained on a CRF',
'pipeline': load("pipeline_ex1_s3.joblib")
},
#{
# 'id': 4,
# 'name': 'Trained on a small dataset',
# 'pipeline': load("pipeline_ex2_s1.joblib")
#},
#{
# 'id': 5,
# 'name': 'Trained on a large dataset',
# 'pipeline': load("pipeline_ex2_s2.joblib")
#},
#{
# 'id': 6,
# 'name': 'Embedded using TFIDF',
# 'pipeline': load("pipeline_ex3_s1.joblib")
#},
#{
# 'id': 7,
# 'name': 'Embedded using ?',
# 'pipeline': load("pipeline_ex3_s2.joblib")
#},
]
pipeline_metadata = [{'id': p['id'], 'name': p['name']} for p in PIPELINES]
def get_pipeline_by_id(pipelines, pipeline_id):
return next((p['pipeline'] for p in pipelines if p['id'] == pipeline_id), None)
def get_name_by_id(pipelines, pipeline_id):
return next((p['name'] for p in pipelines if p['id'] == pipeline_id), None)
def requestResults(text, pipeline):
labels = pipeline.predict(text)
print(labels.ndim)
if labels.ndim != 1:
flattened_predictions = []
for sentence in labels:
for tag in sentence:
flattened_predictions.append(tag)
labels = flattened_predictions
print(labels)
labels = [int(label) for label in labels]
tag_encoder = LabelEncoder()
tag_encoder.fit(['B-AC', 'O', 'B-LF', 'I-LF'])
decoded_labels = tag_encoder.inverse_transform(labels)
return decoded_labels
LOG_FILE = "usage_log.jsonl" # Each line is a JSON object
def log_interaction(user_input, model_name, predictions):
log_entry = {
"timestamp": datetime.datetime.utcnow().isoformat(),
"user_input": user_input,
"model": model_name,
"predictions": predictions
}
with open(LOG_FILE, "a") as f:
f.write(json.dumps(log_entry) + "\n")
app = Flask(__name__)
@app.route('/')
def index():
return render_template('index.html', pipelines= pipeline_metadata)
@app.route('/', methods=['POST'])
def get_data():
if request.method == 'POST':
text = request.form['search']
tokens = re.findall(r"\w+|[^\w\s]", text)
tokens_fomatted = pd.Series([pd.Series(tokens)])
pipeline_id = int(request.form['pipeline_select'])
pipeline = get_pipeline_by_id(PIPELINES, pipeline_id)
name = get_name_by_id(PIPELINES, pipeline_id)
labels = requestResults(tokens_fomatted, pipeline)
results = dict(zip(tokens, labels))
print(f"[INFO] Model: {name}")
print(f"[INFO] Input: {text}")
print(f"[INFO] Output: {results}")
return render_template('index.html', results=results, name=name, pipelines= pipeline_metadata)
if __name__ == '__main__':
app.run(host="0.0.0.0", port=7860)
#if __name__ == '__main__':
#app.run(host="0.0.0.0", port=7860)
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