import os
import openai
import wget
import streamlit as st
from PIL import Image
from serpapi import GoogleSearch
import torch
from diffusers import StableDiffusionPipeline
from bokeh.models.widgets import Button
from bokeh.models import CustomJS
from streamlit_bokeh_events import streamlit_bokeh_events
import base64
from streamlit_player import st_player
from pytube import YouTube
from pytube import Search
import io
import warnings
from PIL import Image
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
from datetime import datetime
from google.oauth2 import service_account
from googleapiclient.discovery import build
import wget
import urllib.request
import sqlite3
import pandas as pd
import pandasql as ps

def clean(value):
    val = value.replace("'",'').replace("[",'').replace("]",'')
    return val

def save_uploadedfile(uploadedfile):
    with open(uploadedfile.name,"wb") as f:
        f.write(uploadedfile.getbuffer())

def gpt3(texts):
    # openai.api_key = os.environ["Secret"]
    openai.api_key = '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'#'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' #'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'
    response = openai.Completion.create(
    engine="text-davinci-002",
    prompt= texts,
        temperature=temp,
        max_tokens=750,
        top_p=1,
        frequency_penalty=0.0,          
        presence_penalty=0.0,
        stop = (";", "/*", "</code>"))
    x = response.choices[0].text 
    return x

def warning(sqlOutput):
    dl = []
    lst = ['DELETE','DROP','TRUNCATE','MERGE','ALTER','UPDATE','INSERT']
    op2 = " ".join(sqlOutput.split())
    op3 = op2.split(' ')
    op4 = list(map(lambda x: x.upper(), op3))
    for i in op4:
        if i in lst: 
            dl.append(i)
    for i in dl:
        st.warning("This query will " + i + " the data ",icon="⚠️")


stability_api = client.StabilityInference(
    key=st.secrets["STABILITY_KEY"], #os.environ("STABILITY_KEY"), # key=os.environ['STABILITY_KEY'], # API Key reference.
    verbose=True, # Print debug messages.
    engine="stable-diffusion-v1-5", # Set the engine to use for generation.
    # Available engines: stable-diffusion-v1 stable-diffusion-v1-5 stable-diffusion-512-v2-0 stable-diffusion-768-v2-0
    # stable-diffusion-512-v2-1 stable-diffusion-768-v2-1 stable-inpainting-v1-0 stable-inpainting-512-v2-0
)

def search_internet(question):
    params = {
        "q": question,
        "location": "Bengaluru, Karnataka, India",
        "hl": "hi",
        "gl": "in",
        "google_domain": "google.co.in",
        # "api_key": ""
        "api_key": st.secrets["GOOGLE_API"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API']
    }

    params = {
        "q": question,
        "location": "Bengaluru, Karnataka, India",
        "hl": "hi",
        "gl": "in",
        "google_domain": "google.co.in",
        # "api_key": ""
        "api_key": st.secrets["GOOGLE_API"] #os.environ("GOOGLE_API") #os.environ['GOOGLE_API']
    }

    search = GoogleSearch(params)
    results = search.get_dict()
    organic_results = results["organic_results"]
  

    snippets = ""
    counter = 1
    for item in organic_results:
        snippets += str(counter) + ". " + item.get("snippet", "") + '\n' + item['about_this_result']['source']['source_info_link'] + '\n'
        counter += 1

    # snippets

    response = openai.Completion.create(
        model="text-davinci-003",
        prompt=f'''following are snippets from google search with these as knowledge base only answer questions and print  reference link as well followed by answer. \n\n {snippets}\n\n question-{question}\n\nAnswer-''',
        temperature=0.49,
        max_tokens=256,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0)

    string_temp = response.choices[0].text
    st.write(string_temp)
    st.write(snippets)

# openai.api_key = ""
openai.api_key = st.secrets["OPENAI_KEY"] #os.environ("OPENAI_KEY") #os.environ['OPENAI_KEY']
date_time = str(datetime.now())

# dictionary = st.secrets("GSHEET_KEY")
# json_object = json.dumps(dictionary, indent=4)

def g_sheet_log(myinput, output):
  SERVICE_ACCOUNT_FILE = 'gsheet.json'

  credentials = service_account.Credentials.from_service_account_file(
      filename=SERVICE_ACCOUNT_FILE
  )

  service_sheets = build('sheets', 'v4', credentials=credentials)

  GOOGLE_SHEETS_ID = '16cM8lHm7n_X0ZVLgWfL5fcBhvKWIGO9LQz3zCl2Dn_8'
  worksheet_name = 'Prompt_Logs!'
  cell_range_insert = 'A:C'

  values = (
      (myinput, output, date_time),
  )

  value_range_body = {
      'majorDimension' : 'ROWS',
      'values' : values
  }

  service_sheets.spreadsheets().values().append(
      spreadsheetId=GOOGLE_SHEETS_ID,
      valueInputOption='USER_ENTERED',
      range=worksheet_name + cell_range_insert,
      body=value_range_body
  ).execute()


def openai_response(PROMPT):
    response = openai.Image.create(
    prompt=PROMPT,
    n=1,
    size="256x256",
)
    return response["data"][0]["url"]

#page_bg_img = """
#<style>
#[data-testid="stAppViewContainer"] {
#background-color: #ffffff;
#opacity: 0.8;
#background-image:  repeating-radial-gradient( circle at 0 0, transparent 0, #ffffff 40px ), repeating-linear-gradient( #55a6f655, #55a6f6 );
#}
#</style>
#"""

#st.markdown(page_bg_img, unsafe_allow_html=True)
st.title("Ask :red[Mukesh] anything!!🤖")
st.title("Puchne mai kya jaata hai??")

option_ = ['Random Questions','Questions based on custom CSV data']
Usage = st.selectbox('Select an option:', option_)
if Usage == 'Questions based on custom CSV data':
    st.text('''
    You can use your own custom csv files to test this feature or
    you can use the sample csv file which contains data about cars.

    Example question:
    - How many cars were manufactured each year between 2000 to 2008?
    ''')
    
    option = ['Sample_Cars_csv','Upload_csv']
    res = st.selectbox('Select from below options:',option)
    if res == 'Upload_csv':
        uploaded_file = st.file_uploader("Add dataset (csv) ",type=['csv'])
        if uploaded_file is not None:
            st.write("File Uploaded")
            file_name=uploaded_file.name     
            ext=file_name.split(".")[0]
            st.write(ext)
            df=pd.read_csv(uploaded_file)
            save_uploadedfile(uploaded_file)
            col= df.columns
            try:
                columns = str((df.columns).tolist())
                column = clean(columns)
                st.write('Columns:' )
                st.text(col)
            except:
                pass
    
        temp = st.slider('Temperature: ', 0.0, 1.0, 0.0)
        
        
        with st.form(key='columns_in_form2'):
            col3, col4 = st.columns(2)
        with col3:
            userPrompt = st.text_area("Input Prompt",'Enter Natural Language Query')
            submitButton = st.form_submit_button(label = 'Submit')
            if submitButton:
                try:
                    col_p ="Create SQL statement from instruction.  "+ext+" " " (" + column +")." +" Request:" + userPrompt  + "SQL statement:" 
                    result = gpt3(col_p)
                except:    
                    results = gpt3(userPrompt)
                st.success('loaded')
        with col4:
            try:
                sqlOutput = st.text_area('SQL Query', value=gpt3(col_p))
                warning(sqlOutput) 
                cars=pd.read_csv('cars.csv')
                result_tab2=ps.sqldf(sqlOutput)
                st.write(result_tab2)
                with open("fewshot_matplot.txt", "r") as file:
                            text_plot = file.read()

                result_tab = result_tab2.reset_index(drop=True)
                result_tab_string = result_tab.to_string()
                gr_prompt = text_plot + userPrompt + result_tab_string + "Plot graph for: " 
                
                if len(gr_prompt) > 4097:
                    st.write('OVERWHELMING DATA!!! You have given me more than 4097 tokens! ^_^')
                    st.write('As of today, the NLP model text-davinci-003 that I run on takes in inputs that have less than 4097 tokens. Kindly retry ^_^')
                
                elif len(result_tab2.columns) < 2:
                    st.write("I need more data to conduct analysis and provide visualizations for you... ^_^") 

                else:
                    st.success("Plotting...")
                    response_graph = openai.Completion.create(
                            engine="text-davinci-003",
                            prompt = gr_prompt,
                            max_tokens=1024,
                            n=1,
                            stop=None,
                            temperature=0.5,
                        )
                    
                    if response_graph['choices'][0]['text'] != "":    
                        print(response_graph['choices'][0]['text'])
                        exec(response_graph['choices'][0]['text'])
                    
                    else:
                        print('Retry! Graph could not be plotted *_*')

            except:
                pass

    elif res == "Sample_Cars_csv":
        df = pd.read_csv('cars.csv')
        col= df.columns
        try:
            columns = str((df.columns).tolist())
            column = clean(columns)
            st.write('Columns:' )
            st.text(col)
        except:
            pass
    
        temp = st.slider('Temperature: ', 0.0, 1.0, 0.0)
        
        
        with st.form(key='columns_in_form2'):
            col3, col4 = st.columns(2)
        with col3:
            userPrompt = st.text_area("Input Prompt",'Enter Natural Language Query')
            submitButton = st.form_submit_button(label = 'Submit')
            if submitButton:
                try:
                    col_p ="Create SQL statement from instruction.  "+ext+" " " (" + column +")." +" Request:" + userPrompt  + "SQL statement:" 
                    result = gpt3(col_p)
                except:    
                    results = gpt3(userPrompt)
                st.success('loaded')
        with col4:
            try:
                sqlOutput = st.text_area('SQL Query', value=gpt3(col_p))
                warning(sqlOutput) 
                cars=pd.read_csv('cars.csv')
                result_tab2=ps.sqldf(sqlOutput)
                st.write(result_tab2)
                with open("fewshot_matplot.txt", "r") as file:
                            text_plot = file.read()

                result_tab = result_tab2.reset_index(drop=True)
                result_tab_string = result_tab.to_string()
                gr_prompt = text_plot + userPrompt + result_tab_string + "Plot graph for: " 
                
                if len(gr_prompt) > 4097:
                    st.write('OVERWHELMING DATA!!! You have given me more than 4097 tokens! ^_^')
                    st.write('As of today, the NLP model text-davinci-003 that I run on takes in inputs that have less than 4097 tokens. Kindly retry ^_^')
                
                elif len(result_tab2.columns) < 2:
                    st.write("I need more data to conduct analysis and provide visualizations for you... ^_^") 

                else:
                    st.success("Plotting...")
                    response_graph = openai.Completion.create(
                            engine="text-davinci-003",
                            prompt = gr_prompt,
                            max_tokens=1024,
                            n=1,
                            stop=None,
                            temperature=0.5,
                        )
                    
                    if response_graph['choices'][0]['text'] != "":    
                        print(response_graph['choices'][0]['text'])
                        exec(response_graph['choices'][0]['text'])
                    
                    else:
                        print('Retry! Graph could not be plotted *_*')

            except:
                pass
        

elif Usage == 'Random Questions':
    st.text('''You can ask me: 
    1. All the things you ask ChatGPT.
    2. Generating paintings, drawings, abstract art. 
    3. Music or Videos
    4. Weather 
    5. Stocks 
    6. Current Affairs and News.
    7. Create or compose tweets or Linkedin posts or email.''')
    
    Input_type = st.radio(
        "**Input type:**",
        ('TEXT', 'SPEECH')
        )
    
    if Input_type == 'TEXT':
        #page_bg_img2 = """
        #<style>
        #[data-testid="stAppViewContainer"] {
        #background-color: #e5e5f7;
        #opacity: 0.8;
        #background-size: 20px 20px;
        #background-image:  repeating-linear-gradient(0deg, #32d947, #32d947 1px, #e5e5f7 1px, #e5e5f7);
        #}
        #</style>
        #"""
        #st.markdown(page_bg_img, unsafe_allow_html=True)
        st.write('**You are now in Text input mode**')
        mytext = st.text_input('**Go on! Ask me anything:**')
        if st.button("SUBMIT"):
            question=mytext
            response = openai.Completion.create(
              model="text-davinci-003",
              prompt=f'''Your name is alexa and  knowledge cutoff date is 2021-09, and it is not aware of any events after that time. if the  
                        Answer to following questions is not  from your knowledge base or in case of queries like weather 
                          updates / stock updates / current news Etc which requires you to have internet connection  then print i don't have access to internet to answer your question, 
                          if  question is related to  image or  painting or drawing generation then print ipython type output   function gen_draw("detailed prompt of image to be generated")
                          if the question is related to playing a song or video or music of a singer then print ipython type output  function vid_tube("relevent search query")
                          if the question is related to operating home appliances then print ipython type output function home_app(" action(ON/Off),appliance(TV,Geaser,Fridge,Lights,fans,AC)") . 
                          if question is realted to sending mail or sms then print ipython type output function messenger_app(" message of us ,messenger(email,sms)")
                          \nQuestion-{question}
                          \nAnswer -''',
              temperature=0.49,
              max_tokens=256,
              top_p=1,
              frequency_penalty=0,
              presence_penalty=0
            )
            string_temp=response.choices[0].text
    
            if ("gen_draw" in string_temp):
                try:
                    try:
                        wget.download(openai_response(prompt))
                        img2 = Image.open(wget.download(openai_response(prompt)))
                        img2.show()
                        rx = 'Image returned'
                        g_sheet_log(mytext, rx)
                    except:
                        urllib.request.urlretrieve(openai_response(prompt),"img_ret.png")
                        img = Image.open("img_ret.png")
                        img.show()
                        rx = 'Image returned'
                        g_sheet_log(mytext, rx)
                except:
                    # Set up our initial generation parameters.
                    answers = stability_api.generate(
                    prompt = mytext,
                    seed=992446758, # If a seed is provided, the resulting generated image will be deterministic.
                            # What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again.
                            # Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook.
                    steps=30, # Amount of inference steps performed on image generation. Defaults to 30.
                    cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt.
                        # Setting this value higher increases the strength in which it tries to match your prompt.
                        # Defaults to 7.0 if not specified.
                    width=512, # Generation width, defaults to 512 if not included.
                    height=512, # Generation height, defaults to 512 if not included.
                    samples=1, # Number of images to generate, defaults to 1 if not included.
                    sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with.
                                                        # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers.
                                                        # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m)
                    )
    
                    # Set up our warning to print to the console if the adult content classifier is tripped.
                    # If adult content classifier is not tripped, save generated images.
                    for resp in answers:
                        for artifact in resp.artifacts:
                            if artifact.finish_reason == generation.FILTER:
                                warnings.warn(
                                    "Your request activated the API's safety filters and could not be processed."
                                    "Please modify the prompt and try again.")
                            if artifact.type == generation.ARTIFACT_IMAGE:
                                img = Image.open(io.BytesIO(artifact.binary))
                                st.image(img)
                                img.save(str(artifact.seed)+ ".png") # Save our generated images with their seed number as the filename.
                                rx = 'Image returned'
                                g_sheet_log(mytext, rx)
                                
                # except:        
                #     st.write('image is being generated please wait...')
                #     def extract_image_description(input_string):
                #         return input_string.split('gen_draw("')[1].split('")')[0]
                #     prompt=extract_image_description(string_temp)
                #     # model_id = "CompVis/stable-diffusion-v1-4"
                #     model_id='runwayml/stable-diffusion-v1-5'
                #     device = "cuda"
    
    
                #     pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
                #     pipe = pipe.to(device)
    
                #     # prompt = "a photo of an astronaut riding a horse on mars"
                #     image = pipe(prompt).images[0]  
                    
                #     image.save("astronaut_rides_horse.png")
                #     st.image(image)
                #     # image
    
            elif ("vid_tube" in string_temp):
                s = Search(mytext)
                search_res = s.results
                first_vid = search_res[0]
                print(first_vid)
                string = str(first_vid)
                video_id = string[string.index('=') + 1:-1]
                # print(video_id)
                YoutubeURL = "https://www.youtube.com/watch?v="
                OurURL = YoutubeURL + video_id
                st.write(OurURL)
                st_player(OurURL)
                ry = 'Youtube link and video returned'
                g_sheet_log(mytext, ry)
    
            elif ("don't" in string_temp or "internet" in string_temp):
                st.write('searching internet ')
                search_internet(question)
                rz = 'Internet result returned'
                g_sheet_log(mytext, rz)
    
            else:
                st.write(string_temp)
                g_sheet_log(mytext, string_temp)
        
    elif Input_type == 'SPEECH':
        stt_button = Button(label="Speak", width=100)
        stt_button.js_on_event("button_click", CustomJS(code="""
            var recognition = new webkitSpeechRecognition();
            recognition.continuous = true;
            recognition.interimResults = true;
            recognition.onresult = function (e) {
                var value = "";
                for (var i = e.resultIndex; i < e.results.length; ++i) {
                    if (e.results[i].isFinal) {
                        value += e.results[i][0].transcript;
                    }
                }
                if ( value != "") {
                    document.dispatchEvent(new CustomEvent("GET_TEXT", {detail: value}));
                }
            }
            recognition.start();
            """))
        
        result = streamlit_bokeh_events(
        stt_button,
        events="GET_TEXT",
        key="listen",
        refresh_on_update=False,
        override_height=75,
        debounce_time=0)
    
        if result:
            if "GET_TEXT" in result:
                st.write(result.get("GET_TEXT"))
                question = result.get("GET_TEXT")
                response = openai.Completion.create(
                model="text-davinci-003",
                prompt=f'''Your knowledge cutoff is 2021-09, and it is not aware of any events after that time. if the  
                          Answer to following questions is not  from your knowledge base or in case of queries like weather 
                            updates / stock updates / current news Etc which requires you to have internet connection  then print i don't have access to internet to answer your question, 
                            if  question is related to  image or  painting or drawing generation then print ipython type output   function gen_draw("detailed prompt of image to be generated")
                            if the question is related to playing a song or video or music of a singer then print ipython type output  function vid_tube("relevent search query")
                            \nQuestion-{question}
                            \nAnswer -''',
                temperature=0.49,
                max_tokens=256,
                top_p=1,
                frequency_penalty=0,
                presence_penalty=0
                )
                string_temp=response.choices[0].text
    
                if ("gen_draw" in string_temp):
                    st.write('*image is being generated please wait..* ')
                    def extract_image_description(input_string):
                        return input_string.split('gen_draw("')[1].split('")')[0]
                    prompt=extract_image_description(string_temp)
                    # model_id = "CompVis/stable-diffusion-v1-4"
                    model_id='runwayml/stable-diffusion-v1-5'
                    device = "cuda"
    
                    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
                    pipe = pipe.to(device)
    
                    # prompt = "a photo of an astronaut riding a horse on mars"
                    image = pipe(prompt).images[0]  
                      
                    image.save("astronaut_rides_horse.png")
                    st.image(image)
                    # image 
                
                elif ("vid_tube" in string_temp):
                    s = Search(question)
                    search_res = s.results
                    first_vid = search_res[0]
                    print(first_vid)
                    string = str(first_vid)
                    video_id = string[string.index('=') + 1:-1]
                    # print(video_id)
                    YoutubeURL = "https://www.youtube.com/watch?v="
                    OurURL = YoutubeURL + video_id
                    st.write(OurURL)
                    st_player(OurURL)
    
                elif ("don't" in string_temp or "internet" in string_temp  ):
                    st.write('*searching internet*')
                    search_internet(question)
                else:
                    st.write(string_temp)