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))

        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)