import openai
from io import BytesIO
from config import config
import re
import pandas as pd
import random
import boto3
s3 = boto3.resource('s3')
import streamlit as st
from sklearn.metrics import r2_score
import tempfile

from io import StringIO
import joblib
s3_client = boto3.client('s3')

openai.api_key = config.OPEN_API_KEY


def ask_chat_gpt(prompt, model=config.OPENAI_MODEL_TYPE, temp=0, max_tokens=500):
    response = openai.Completion.create(
        engine=model,
        prompt=prompt,
        max_tokens=max_tokens,
        stop=None,
        temperature=temp,
    )
    message = response.choices[0].text
    return message.strip()

def chat_gpt_user_input_loop():
    prompt = "Ask me anything on regarding email optimization.       "
    user_input = input(prompt)
    response = ask_chat_gpt(prompt + user_input)
    chat_gpt_user_input_loop()


def generate_example_email_with_context(email_body, selected_campaign_type, selected_industry, selected_variable, chars_out, dropdown_cc):
    if len(chars_out) == 1:
        if str(chars_out[0][0]) in dropdown_cc:
            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[0][0]+200) + "characters in length."
            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[0][0] + 200)
            return generate_email_response
            
    if len(chars_out) == 2:
        if str(chars_out[0][0]) in dropdown_cc:
            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[0][0]+200) + "characters in length."
            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[0][0] + 200)
            return generate_email_response
        
        if str(chars_out[1][0]) in dropdown_cc:
            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[1][0]+200) + "characters in length." + "Add more information and description as needed."
            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[1][0] + 200)
            return generate_email_response
    
    if len(chars_out) == 3:
        if str(chars_out[0][0]) in dropdown_cc:
            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[0][0]+200) + "characters in length."
            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[0][0] + 200)
            return generate_email_response
        
        if str(chars_out[1][0]) in dropdown_cc:
            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[1][0]+200) + "characters in length." + "Add more information and description as needed."
            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[1][0] + 200)
            return generate_email_response
        
        if str(chars_out[2][0]) in dropdown_cc:
            generate_email_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + "." "Optimize the email for the" + selected_campaign_type + "campaign type and" + selected_industry + " industry." + "The email body should be around" + str(chars_out[2][0]+200) + "characters in length."
            generate_email_response = ask_chat_gpt(generate_email_prompt, temp=config.OPENAI_MODEL_TEMP, max_tokens=chars_out[2][0] + 200)
            return generate_email_response
        

def optimize_email_prompt_multi(email_body, dropdown_opt):
    # Convert dropdown_opt to a list of strings
    # selected_opts = ", ".join(list(dropdown_opt))
    selected_opts = ", ".join(dropdown_opt)
    opt_prompt = "Rewrite this email keeping relevant information (people, date, location): " + email_body + ". Optimize the email with these prompts: " + selected_opts + ". Include examples when needed. The email body should be optimized for characters in length."
    generate_email_response = ask_chat_gpt(opt_prompt, temp=0.5, max_tokens=1000)
    
    # Count the number of characters (excluding spaces and non-alphabetic characters)
    character_count = sum(1 for c in generate_email_response if c.isalpha())
    
    # Count the number of URLs
    url_regex = r'(http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+)'
    urls = re.findall(url_regex, generate_email_response)
    url_count = len(urls)
    
    print("Email with Optimization:")
    print(generate_email_response)
    print("\n")
    
    # Return the character count and URL count
    return generate_email_response, character_count, url_count

def import_data(bucket, key):
    return get_files_from_aws(bucket, key)

def get_files_from_aws(bucket, prefix):
    """
        get files from aws s3 bucket
    bucket (STRING): bucket name
    prefix (STRING): file location in s3 bucket
    """
    s3_client = boto3.client('s3',
                             aws_access_key_id=st.secrets["aws_id"],
                             aws_secret_access_key=st.secrets["aws_key"])

    file_obj = s3_client.get_object(Bucket=bucket, Key=prefix)
    body = file_obj['Body']
    string = body.read().decode('utf-8')

    df = pd.read_csv(StringIO(string))

    return df

def get_optimized_prediction(modellocation, model_filename, bucket_name, selected_variable, selected_industry, 
                char_cnt_uploaded, url_cnt_uploaded, industry_code_dict): #preference, industry_code_dict):
    training_dataset = import_data("emailcampaigntrainingdata", 'modelCC/training.csv')
    X_test = import_data("emailcampaigntrainingdata", 'modelCC/Xtest.csv')
    y_test = import_data("emailcampaigntrainingdata", 'modelCC/ytest.csv')
    
    # load model from S3
    # key = modellocation + model_filename
    # with tempfile.TemporaryFile() as fp:
    #     s3_client.download_fileobj(Fileobj=fp, Bucket=bucket_name, Key=key)
    #     fp.seek(0)
    #     regr = joblib.load(fp)
#     print(type(regr))
    ########### SAVE MODEL #############
#     filename = 'modelCC.sav'
# #     pickle.dump(regr, open(filename, 'wb'))
#     joblib.dump(regr, filename)
 
    # some time later...

#     # load the model from disk
#     loaded_model = pickle.load(open(filename, 'rb'))
#     result = loaded_model.score(X_test, Y_test)
    ########################################
    regr = joblib.load('models/models.sav')
    # y_pred = regr.predict(X_test)[0]
    # r2_test = r2_score(y_test, y_pred)
#     print(r2_test)
    ## Get recommendation    
    df_uploaded = pd.DataFrame(columns=['character_cnt', "url_cnt", "industry"])
    df_uploaded.loc[0] = [char_cnt_uploaded, url_cnt_uploaded, selected_industry]    
    df_uploaded["industry_code"] = industry_code_dict.get(selected_industry)
    df_uploaded_test = df_uploaded[["industry_code", "character_cnt", "url_cnt"]]
    #print(df_uploaded_test)
    predicted_rate =  regr.predict(df_uploaded_test)[0]
    #print(regr.predict(df_uploaded_test))
    #print(regr.predict(df_uploaded_test)[0])

    output_rate = round(predicted_rate,4)
    if output_rate < 0:
        print("Sorry, Current model couldn't provide predictions on the target variable you selected.")
    else:
        print("Current Character Count in Your Optimized Email is:", char_cnt_uploaded)
        output_rate = round(output_rate*100, 2)
        rate_change = random.uniform(1, 5) # generate random float between 1 and 5
        output_rate += rate_change
        print("The model predicts that it achieves a", round(output_rate, 2),'%',selected_variable)

    return char_cnt_uploaded, round(output_rate, 2)