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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
import numpy as np
from huggingface_hub import hf_hub_download
import torch
pd.set_option('display.max_colwidth', 1000)
# Patch torch.load to always load on CPU
original_torch_load = torch.load
def cpu_load(*args, **kwargs):
return original_torch_load(*args, map_location=torch.device('cpu'), **kwargs)
torch.load = cpu_load
def load_pipeline_from_hub(filename):
repo_id = 'hw01558/nlp-coursework-pipelines'
local_path = hf_hub_download(repo_id=repo_id, filename=filename)
return load(local_path)
PIPELINES = [
{
'id': 1,
'name': 'Baseline',
'pipeline': load_pipeline_from_hub("pipeline_ex1_s1.joblib")
},
{
'id': 2,
'name': 'Trained on a FeedForward NN',
'pipeline': load_pipeline_from_hub("pipeline_ex1_s2.joblib")
},
{
'id': 3,
'name': 'Trained on a CRF',
'pipeline': load_pipeline_from_hub("pipeline_ex1_s2.joblib")
},
{
'id': 4,
'name': 'Trained on a small dataset',
'pipeline': load_pipeline_from_hub("pipeline_ex2_s3.joblib")
},
{
'id': 5,
'name': 'Trained on a large dataset',
'pipeline': load_pipeline_from_hub("pipeline_ex2_s2.joblib")
},
{
'id': 6,
'name': 'Embedded using TFIDF',
'pipeline': load_pipeline_from_hub("pipeline_ex3_s2.joblib")
},
{
'id': 7,
'name': 'Embedded using GloVe',
'pipeline': load_pipeline_from_hub("pipeline_ex3_s3.joblib")
},
{
'id': 8,
'name': 'Embedded using Bio2Vec',
'pipeline': load_pipeline_from_hub("pipeline_ex3_s4.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)
if isinstance(labels, np.ndarray):
labels = labels.tolist()
return labels[0]
import os
LOG_FILE = "/tmp/usage_log.jsonl" # Use temporary file path for Hugging Face Spaces
def log_interaction(user_input, model_name, predictions):
log_entry = {
"timestamp": datetime.datetime.utcnow().isoformat(),
"model": model_name,
"user_input": user_input,
"predictions": predictions
}
try:
os.makedirs(os.path.dirname(LOG_FILE), exist_ok=True) # Ensure the directory exists
with open(LOG_FILE, "a") as log_file:
log_file.write(json.dumps(log_entry) + "\n")
except Exception as e:
print(f"Error writing to log: {e}")
# You could also return a response with the error, or raise an error to stop the process
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))
log_interaction(text, name, 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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