from typing import List, Tuple
from typing_extensions import Literal
import logging
import pandas as pd
from pandas import DataFrame, Series
from utils.config import getconfig
from utils.preprocessing import processingpipeline
import streamlit as st
from transformers import pipeline

## Labels dictionary ###
_lab_dict = {
            '0':'NO',
            '1':'YES',
            }

def get_target_labels(preds):

    """
    Function that takes the numerical predictions as an input and returns a list of the labels.
    
    """
    
    # Get label names
    preds_list = preds.tolist()
    
    predictions_names=[]
    
    # loop through each prediction
    for ele in preds_list:
    
      # see if there is a value 1 and retrieve index
      try:
        index_of_one = ele.index(1)
      except ValueError:
        index_of_one = "NA"
    
      # Retrieve the name of the label (if no prediction made = NA)
      if index_of_one != "NA":
        name  = label_dict[index_of_one]
      else:
        name = "Other"
    
      # Append name to list
      predictions_names.append(name)
    
    return predictions_names

@st.cache_resource
def load_targetClassifier(config_file:str = None, classifier_name:str = None):
    """
    loads the document classifier using haystack, where the name/path of model
    in HF-hub as string is used to fetch the model object.Either configfile or 
    model should be passed.
    1. https://docs.haystack.deepset.ai/reference/document-classifier-api
    2. https://docs.haystack.deepset.ai/docs/document_classifier
    Params
    --------
    config_file: config file path from which to read the model name
    classifier_name: if modelname is passed, it takes a priority if not \
    found then will look for configfile, else raise error.
    Return: document classifier model
    """
    if not classifier_name:
        if not config_file:
            logging.warning("Pass either model name or config file")
            return
        else:
            config = getconfig(config_file)
            classifier_name = config.get('target','MODEL')
    
    logging.info("Loading classifier")  
      
    doc_classifier = pipeline("text-classification", 
                            model=classifier_name, 
                            top_k =1)

    return doc_classifier


@st.cache_data
def target_classification(haystack_doc:pd.DataFrame,
                        threshold:float = 0.5, 
                        classifier_model:pipeline= None
                        )->Tuple[DataFrame,Series]:
    """
    Text-Classification on the list of texts provided. Classifier provides the 
    most appropriate label for each text. There labels indicate whether the paragraph
    references a specific action, target or measure in the paragraph.
    ---------
    haystack_doc: List of haystack Documents. The output of Preprocessing Pipeline 
    contains the list of paragraphs in different format,here the list of 
    Haystack Documents is used.
    threshold: threshold value for the model to keep the results from classifier
    classifiermodel: you can pass the classifier model directly,which takes priority
    however if not then looks for model in streamlit session.
    In case of streamlit avoid passing the model directly.
    Returns
    ----------
    df: Dataframe with two columns['SDG:int', 'text']
    x: Series object with the unique SDG covered in the document uploaded and 
    the number of times it is covered/discussed/count_of_paragraphs. 
    """
                            
    logging.info("Working on target/action identification")
                            
    haystack_doc['Target Label'] = 'NA'

    if not classifier_model:

        classifier_model = st.session_state['target_classifier']

        # Get predictions
        predictions = classifier_model(list(haystack_doc.text))

        # Get labels for predictions
        pred_labels = getlabels(predictions)

        # Save labels
        haystack_doc['Target Label'] = pred_labels

                            
    # logging.info("Working on action/target extraction")
    # if not classifier_model:
#     #     classifier_model = st.session_state['target_classifier']
    
#     # results = classifier_model(list(haystack_doc.text))
#     # labels_= [(l[0]['label'],
#     #            l[0]['score']) for l in results]
           

#     # df1 = DataFrame(labels_, columns=["Target Label","Target Score"])
#     # df = pd.concat([haystack_doc,df1],axis=1)
    
#     # df = df.sort_values(by="Target Score", ascending=False).reset_index(drop=True)
#     # df['Target Score'] = df['Target Score'].round(2)
#     # df.index += 1
#     # # df['Label_def'] = df['Target Label'].apply(lambda i: _lab_dict[i])

     return haystack_doc