import gradio as gr
import requests
import pandas as pd
import ast

import boto3
import re
import time

df= pd.DataFrame()

def _connect():  
    '''
    Creates and returns the s3 connection by passing the access key and secret key to boto3.client
    '''
    s3 = boto3.client("s3",aws_access_key_id='AKIA2BAL5ERM4EL4HRAE',aws_secret_access_key='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')
    return s3

def _upload(s3, tmp_filepath, s3_filepath, acl="public-read", content_type=None):
    
    '''
    This function uploads the files to specified bucket in s3 and returns the file url 
    '''
    S3_LOCATION = 'https://{}.s3.ap-south-1.amazonaws.com/'.format('edwisely-academic-materials-v2')
    _content_type = content_type
    try:
        with open(tmp_filepath, 'rb') as data:
            
            s3.upload_fileobj(data, 'edwisely-academic-materials-v2', s3_filepath,
                              ExtraArgs={
                                  "ACL": acl,
                                  "ContentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
                              })
        return "{}{}".format(S3_LOCATION, s3_filepath)
    except Exception as e:
        print("Something Happened: ", e)
        raise


def upload_file(tmp_filepath, faculty_name):
    s3_obj = _connect()
    s3_filepath = "{}/{}/{}.xlsx".format('q_gen', 'test',int(time.time()))
    s3_uploaded_path = _upload(s3_obj, tmp_filepath, s3_filepath)
    return s3_uploaded_path

def q_gen(topics_list, count, difficulty):
    url = "https://d5cdtgvt04.execute-api.ap-south-1.amazonaws.com/test/q_gen"
    # params = {
    #     "grand_topic_ids": "16595,898,746",
    #     "topic_ids": "59559,59564,59565,59563",
    #     "sub_topic_ids": "54345,31152,4011",
    #     "count":"10",
    #     "difficulty":"8"
        
    # }
    # params = {
    # 	"grand_topic_ids": "D04B01GT02,D07B03GT03,D12B190GT12",
    # 	"topic_ids": "D13B10GT06T03,D13B10GT07T02,D13B10GT07T03,D13B10GT07T04",
    # 	"sub_topic_ids": "D04B01GT04T02ST01,D03B29GT01T02ST010122,D12B190GT21T01ST02",
    # 	"count":"10",
    # 	"difficulty":"8"
    	
    # }
    topics_list = topics_list.split(",")
    grand_topics, topics, sub_topics = "","",""
    for i in topics_list:
        if len(i.split("T")) == 2:
            temp = i.strip()
            if grand_topics != "":
                grand_topics = grand_topics+","+temp
            else:
                grand_topics = i
        elif len(i.split("T")) == 3:
            temp = i.strip()
            if topics != "":
                topics = topics+","+temp
            else:
                topics = temp
        elif len(i.split("T")) == 4:
            temp = i.strip()
            if sub_topics != "":
                sub_topics = sub_topics+","+temp
            else:
                sub_topics = temp
    params = {
        "grand_topic_ids": grand_topics,
        "topic_ids": topics,
        "sub_topic_ids": sub_topics,
        "count":count,
        "difficulty":difficulty,
        "test_id":1,
        "faculty_id":1
        
    }
    df = pd.DataFrame()
    path = ""
    try:
        response = requests.get(url, params=params)
        response.raise_for_status()  # Raise an exception if the request was not successful
    
        # Access the response content
        content = response.text
        df = pd.DataFrame(ast.literal_eval(content))
        if df.columns[0] == "Not enough questions":
            return "Not enough questions", df
        options_df = pd.DataFrame(df['questions_options'])
        columns_shape = pd.DataFrame(df['questions_options'].tolist()).shape[1]
        for i in range(columns_shape):
            options_df[f"option_{i+1}"] = pd.DataFrame(df['questions_options'].tolist())[i].apply(pd.Series)['name']
        # df =df[['name', 'media','blooms_level', 'type_name', 'type_code']]
        df['com'] = range(df.shape[0])
        options_df['com'] = range(df.shape[0])
        df = pd.merge(df, options_df, on=['com'])
        df = df[['id','name', 'media','blooms_level','type_name', 'type_code', 'option_1', 'option_2', 'option_3', 'option_4']]
        df.to_excel('temp.xlsx')
        file_path = "temp.xlsx"
        path = upload_file(file_path, 'krushna')
    
    except requests.exceptions.RequestException as e:
        print("Error occurred:", e)
    return path, df

demo = gr.Interface(fn=q_gen, inputs=[
    gr.Textbox(value="D04B01GT02,D12B190GT12, D13B10GT06T03,D13B10GT07T02, D12B190GT21T01ST02", label = "topics"),
    gr.Textbox(value="10", label = "Count"),
    gr.Textbox(value="8", label = "Difficulty")], outputs=["text","dataframe"])
demo.launch()