import requests
import tensorflow as tf
import pandas as pd
import numpy as np
from operator import add
from functools import reduce
import random
import tabulate

from keras import Model
from keras import regularizers
from keras.optimizers import Adam
from keras.layers import Conv2D, BatchNormalization, ReLU, Input, Flatten, Softmax
from keras.layers import Concatenate, Activation, Dense, GlobalAveragePooling2D, Dropout
from keras.layers import AveragePooling1D, Bidirectional, LSTM, GlobalAveragePooling1D, MaxPool1D, Reshape
from keras.layers import LayerNormalization, Conv1D, MultiHeadAttention, Layer
from keras.models import load_model
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from Bio import SeqIO
from Bio.SeqRecord import SeqRecord
from Bio.SeqFeature import SeqFeature, FeatureLocation
from Bio.Seq import Seq

import cyvcf2
import parasail

import re

ntmap = {'A': (1, 0, 0, 0),
         'C': (0, 1, 0, 0),
         'G': (0, 0, 1, 0),
         'T': (0, 0, 0, 1)
         }

def get_seqcode(seq):
    return np.array(reduce(add, map(lambda c: ntmap[c], seq.upper()))).reshape((1, len(seq), -1))

class PositionalEncoding(Layer):
    def __init__(self, sequence_len=None, embedding_dim=None,**kwargs):
        super(PositionalEncoding, self).__init__()
        self.sequence_len = sequence_len
        self.embedding_dim = embedding_dim

    def call(self, x):

        position_embedding = np.array([
            [pos / np.power(10000, 2. * i / self.embedding_dim) for i in range(self.embedding_dim)]
            for pos in range(self.sequence_len)])

        position_embedding[:, 0::2] = np.sin(position_embedding[:, 0::2])  # dim 2i
        position_embedding[:, 1::2] = np.cos(position_embedding[:, 1::2])  # dim 2i+1
        position_embedding = tf.cast(position_embedding, dtype=tf.float32)

        return position_embedding+x

    def get_config(self):
        config = super().get_config().copy()
        config.update({
            'sequence_len' : self.sequence_len,
            'embedding_dim' : self.embedding_dim,
        })
        return config

def MultiHeadAttention_model(input_shape):
    input = Input(shape=input_shape)

    conv1 = Conv1D(256, 3, activation="relu")(input)
    pool1 = AveragePooling1D(2)(conv1)
    drop1 = Dropout(0.4)(pool1)

    conv2 = Conv1D(256, 3, activation="relu")(drop1)
    pool2 = AveragePooling1D(2)(conv2)
    drop2 = Dropout(0.4)(pool2)

    lstm = Bidirectional(LSTM(128,
                               dropout=0.5,
                               activation='tanh',
                               return_sequences=True,
                               kernel_regularizer=regularizers.l2(0.01)))(drop2)

    pos_embedding = PositionalEncoding(sequence_len=int(((23-3+1)/2-3+1)/2), embedding_dim=2*128)(lstm)
    atten = MultiHeadAttention(num_heads=2,
                               key_dim=64,
                               dropout=0.2,
                               kernel_regularizer=regularizers.l2(0.01))(pos_embedding, pos_embedding)

    flat = Flatten()(atten)

    dense1 = Dense(512,
                   kernel_regularizer=regularizers.l2(1e-4),
                   bias_regularizer=regularizers.l2(1e-4),
                   activation="relu")(flat)
    drop3 = Dropout(0.1)(dense1)

    dense2 = Dense(128,
                   kernel_regularizer=regularizers.l2(1e-4),
                   bias_regularizer=regularizers.l2(1e-4),
                   activation="relu")(drop3)
    drop4 = Dropout(0.1)(dense2)

    dense3 = Dense(256,
                   kernel_regularizer=regularizers.l2(1e-4),
                   bias_regularizer=regularizers.l2(1e-4),
                   activation="relu")(drop4)
    drop5 = Dropout(0.1)(dense3)

    output = Dense(1, activation="linear")(drop5)

    model = Model(inputs=[input], outputs=[output])
    return model

def fetch_ensembl_transcripts(gene_symbol):
    url = f"https://rest.ensembl.org/lookup/symbol/homo_sapiens/{gene_symbol}?expand=1;content-type=application/json"
    response = requests.get(url)
    if response.status_code == 200:
        gene_data = response.json()
        if 'Transcript' in gene_data:
            return gene_data['Transcript']
        else:
            print("No transcripts found for gene:", gene_symbol)
            return None
    else:
        print(f"Error fetching gene data from Ensembl: {response.text}")
        return None

def fetch_ensembl_sequence(transcript_id):
    url = f"https://rest.ensembl.org/sequence/id/{transcript_id}?content-type=application/json"
    response = requests.get(url)
    if response.status_code == 200:
        sequence_data = response.json()
        if 'seq' in sequence_data:
            return sequence_data['seq']
        else:
            print("No sequence found for transcript:", transcript_id)
            return None
    else:
        print(f"Error fetching sequence data from Ensembl: {response.text}")
        return None

def find_crispr_targets(sequence, chr, start, end, strand, transcript_id, exon_id, pam="NGG", target_length=20):
    targets = []
    len_sequence = len(sequence)
    #complement = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'}
    dnatorna = {'A': 'A', 'T': 'U', 'C': 'C', 'G': 'G'}

    for i in range(len_sequence - len(pam) + 1):
        if sequence[i + 1:i + 3] == pam[1:]:
            if i >= target_length:
                target_seq = sequence[i - target_length:i + 3]
                if strand == -1:
                    tar_start = end - (i + 2)
                    tar_end = end - (i - target_length)
                    #seq_in_ref = ''.join([complement[base] for base in target_seq])[::-1]
                else:
                    tar_start = start + i - target_length
                    tar_end = start + i + 3 - 1
                    #seq_in_ref = target_seq
                gRNA = ''.join([dnatorna[base] for base in sequence[i - target_length:i]])
                #targets.append([target_seq, gRNA, chr, str(tar_start), str(tar_end), str(strand), transcript_id, exon_id, seq_in_ref])
                targets.append([target_seq, gRNA, chr, str(tar_start), str(tar_end), str(strand), transcript_id, exon_id])

    return targets

# Function to predict on-target efficiency and format output
def format_prediction_output(targets, model_path):
    model = MultiHeadAttention_model(input_shape=(23, 4))
    model.load_weights(model_path)

    formatted_data = []

    for target in targets:
        # Encode the gRNA sequence
        encoded_seq = get_seqcode(target[0])

        # Predict on-target efficiency using the model
        prediction = float(list(model.predict(encoded_seq, verbose=0)[0])[0])
        if prediction > 100:
            prediction = 100

        # Format output
        gRNA = target[1]
        chr = target[2]
        start = target[3]
        end = target[4]
        strand = target[5]
        transcript_id = target[6]
        exon_id = target[7]
        #seq_in_ref = target[8]
        #formatted_data.append([chr, start, end, strand, transcript_id, exon_id, target[0], gRNA, seq_in_ref, prediction[0]])
        formatted_data.append([chr, start, end, strand, transcript_id, exon_id, target[0], gRNA, prediction])

    return formatted_data

def process_gene(gene_symbol, model_path):
    # Fetch transcripts for the given gene symbol
    transcripts = fetch_ensembl_transcripts(gene_symbol)
    results = []
    all_exons = []  # To accumulate all exons
    all_gene_sequences = []  # To accumulate all gene sequences

    if transcripts:
        for transcript in transcripts:
            Exons = transcript['Exon']
            all_exons.extend(Exons)  # Add all exons from this transcript to the list
            transcript_id = transcript['id']

            for exon in Exons:
                exon_id = exon['id']
                gene_sequence = fetch_ensembl_sequence(exon_id)
                if gene_sequence:
                    all_gene_sequences.append(gene_sequence)  # Add this gene sequence to the list
                    start = exon['start']
                    end = exon['end']
                    strand = exon['strand']
                    chr = exon['seq_region_name']
                    # Find potential CRISPR targets within the exon
                    targets = find_crispr_targets(gene_sequence, chr, start, end, strand, transcript_id, exon_id)
                    if targets:
                        # Format the prediction output for the targets found
                        formatted_data = format_prediction_output(targets, model_path)
                        results.extend(formatted_data)  # Append results
                else:
                    print(f"Failed to retrieve gene sequence for exon {exon_id}.")
    else:
        print("Failed to retrieve transcripts.")

    # Sort results based on prediction score (assuming score is at the 8th index)
    sorted_results = sorted(results, key=lambda x: x[8], reverse=True)

    # Return the sorted output, combined gene sequences, and all exons
    return sorted_results, all_gene_sequences, all_exons


def create_genbank_features(data):
    features = []

    # If the input data is a DataFrame, convert it to a list of lists
    if isinstance(data, pd.DataFrame):
        formatted_data = data.values.tolist()
    elif isinstance(data, list):
        formatted_data = data
    else:
        raise TypeError("Data should be either a list or a pandas DataFrame.")

    for row in formatted_data:
        try:
            start = int(row[1])
            end = int(row[2])
        except ValueError as e:
            print(f"Error converting start/end to int: {row[1]}, {row[2]} - {e}")
            continue

        strand = 1 if row[3] == '+' else -1
        location = FeatureLocation(start=start, end=end, strand=strand)
        feature = SeqFeature(location=location, type="misc_feature", qualifiers={
            'label': row[7],  # Use gRNA as the label
            'note': f"Prediction: {row[8]}"  # Include the prediction score
        })
        features.append(feature)

    return features


def generate_genbank_file_from_df(df, gene_sequence, gene_symbol, output_path):
    # Ensure gene_sequence is a string before creating Seq object
    if not isinstance(gene_sequence, str):
        gene_sequence = str(gene_sequence)

    features = create_genbank_features(df)

    # Now gene_sequence is guaranteed to be a string, suitable for Seq
    seq_obj = Seq(gene_sequence)
    record = SeqRecord(seq_obj, id=gene_symbol, name=gene_symbol,
                       description=f'CRISPR Cas9 predicted targets for {gene_symbol}', features=features)
    record.annotations["molecule_type"] = "DNA"
    SeqIO.write(record, output_path, "genbank")


def create_bed_file_from_df(df, output_path):
    with open(output_path, 'w') as bed_file:
        for index, row in df.iterrows():
            chrom = row["Chr"]
            start = int(row["Start Pos"])
            end = int(row["End Pos"])
            strand = '+' if row["Strand"] == '1' else '-'
            gRNA = row["gRNA"]
            score = str(row["Prediction"])
            # transcript_id is not typically part of the standard BED columns but added here for completeness
            transcript_id = row["Transcript"]

            # Writing only standard BED columns; additional columns can be appended as needed
            bed_file.write(f"{chrom}\t{start}\t{end}\t{gRNA}\t{score}\t{strand}\n")


def create_csv_from_df(df, output_path):
    df.to_csv(output_path, index=False)