from gpt_index import GPTListIndex, SimpleWebPageReader, BeautifulSoupWebReader, GPTSimpleVectorIndex,LLMPredictor
from IPython.display import Markdown, display
from langchain.agents import load_tools, Tool, initialize_agent
from langchain.llms import OpenAI
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain.agents import initialize_agent, Tool
from langchain import LLMChain
from langchain import PromptTemplate
import gradio as gr
import pandas as pd
import openai
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
from openai.embeddings_utils import get_embedding

import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import datetime
from datetime import datetime, date, time, timedelta
import os
from PIL import Image
from PIL import ImageOps
from PIL import Image, ImageDraw, ImageFont
from io import BytesIO
import requests

import gcsfs
fs = gcsfs.GCSFileSystem(project='createinsightsproject',token='anon')
fs.ls('trends_chrome_extension_bucket')
print('Started')
###download both text and image from cloud to display
with fs.open('trends_chrome_extension_bucket/lastradartext.txt', 'rb') as file:
    data_old = file.read()
print(data_old)
value1,value2,value3,value4,value5,value6=str(data_old.decode()).split('SEPERATOR')

img_data = requests.get('https://storage.googleapis.com/trends_chrome_extension_bucket/lasttechradar.png').content
with open('lasttechradar.png', 'wb') as handler:
    handler.write(img_data)

def getlastimage():
    #print('Came into getlastimage')
    img_data = requests.get('https://storage.googleapis.com/trends_chrome_extension_bucket/lasttechradar.png').content
    with open('lasttechradar1.png', 'wb') as handler:
        handler.write(img_data)

    with fs.open('trends_chrome_extension_bucket/lastradartext.txt', 'rb') as file:
        data_old = file.read()
    #print(data_old)
    value1,value2,value3,value4,value5,value6=str(data_old.decode()).split('SEPERATOR')
    return 'lasttechradar1.png',value1.strip(),value2.strip(),value3.strip(),value4.strip(),value5.strip(),value6.strip()


def getstuff(openapikey):
  dateforfilesave=datetime.today().strftime("%d-%m-%Y %I:%M%p")
  print(dateforfilesave)
  os.environ['OPENAI_API_KEY'] = str(openapikey)

  mainlistofanswers=[]
  for each in ['www.mckinsey.com','www.bcg.com','www.bain.com','www.accenture.com']:
    print(each)
    Input_URL = "https://"+each 
    documents = SimpleWebPageReader(html_to_text=True).load_data([Input_URL])
    index = GPTSimpleVectorIndex(documents)
    print('Came here 0')
    #@title # Creating your Langchain Agent
    def querying_db(query: str):
      response = index.query(query)
      return response

    tools = [
        Tool(
            name = "QueryingDB",
            func=querying_db,
            description="This function takes a query string as input and returns the most relevant answer from the documentation as output"
        )]
    llm = OpenAI(temperature=0,openai_api_key=openapikey)
    print('Came here 1')
    query_string = "what are the top technologies mentioned?" 

    agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
    result = agent.run(query_string)
    mainlistofanswers.append(result)
  
  print('Came here 2')
  print(mainlistofanswers)
  newlistoftech=[]
  newlistofcompanies=[]
  for i in range(len(mainlistofanswers)):
    each=mainlistofanswers[i]
    each=each.replace("The top technologies mentioned are ","").replace("The technologies mentioned are ","")
    each=each.replace(":","").replace(" and ",",").replace("and ",",").replace(" and",",").replace(" the "," ").replace("the "," ").replace(" the"," ").strip()
    for item in each.split(","):
        if item!='':
          newlistoftech.append(item.strip())
          newlistofcompanies.append(i)
  tech_df=pd.DataFrame()
  tech_df['tech']=newlistoftech
  tech_df['company']=newlistofcompanies
  print(newlistoftech)
  print('Came here 3')
  embedding_model = "text-embedding-ada-002"
  embedding_encoding = "cl100k_base"  # this the encoding for text-embedding-ada-002
  max_tokens = 8000  # the maximum for text-embedding-ada-002 is 8191

  tech_df["embedding"] = tech_df['tech'].apply(lambda x: get_embedding(x, engine=embedding_model))

  print('Came here 4')

  # Load the embeddings

  # Convert to a list of lists of floats
  matrix = np.array(tech_df['embedding'].to_list())
  perplexityvalue=max(int(len(tech_df['embedding'].to_list()))/2,5) ###original value was a constant of 15
    
  # Create a t-SNE model and transform the data
  tsne = TSNE(n_components=2, perplexity=perplexityvalue, random_state=42, init='random', learning_rate=200)
  vis_dims = tsne.fit_transform(matrix)

  n_clusters = 5

  kmeans = KMeans(n_clusters=n_clusters, init="k-means++", random_state=42)
  kmeans.fit(matrix)
  labels = kmeans.labels_
  tech_df["Cluster"] = labels
  print('Came here 5')
  colors = ["red", "darkorange", "darkgrey", "blue", "darkgreen"]
  x = [x for x,y in vis_dims]
  y = [y for x,y in vis_dims]
  color_indices = tech_df['Cluster'].values 

  colormap = matplotlib.colors.ListedColormap(colors)
  #plt.scatter(x, y, c=color_indices, cmap=colormap, alpha=0.3,)
  fig, ax = plt.subplots(figsize=(12,8))
  ax.scatter(x, y, c=color_indices, cmap=colormap, alpha=1, s=100)

  for i, txt in enumerate(tech_df['tech'].tolist()):
      ax.annotate(txt, (x[i], y[i]),fontsize=14)

  plt.title("Top Technologies as of "+dateforfilesave,fontsize=20)
  plt.axis('off')
  plt.savefig('lasttechradar.png', bbox_inches='tight')
  print('Came here 6')
  response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f'I will give you top technologies list. Write a paragraph on it.\n\nTechnologies:'+",".join(tech_df['tech'].tolist()),
        temperature=0,
        max_tokens=1024,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0,
    )
  print(response["choices"][0]["text"].replace("\n", ""))
  desc_tmp=response["choices"][0]["text"].replace("\n", "")
  print('Came here 7')
  # Reading a review which belong to each group.
  rev_per_cluster = 5

  clusterstextlist=[]
  for i in range(n_clusters):
      print(f"Cluster {i} Theme:", end=" ")

      reviews = "\n".join(tech_df[tech_df['Cluster'] == i]['tech'].tolist())
      response = openai.Completion.create(
          engine="text-davinci-003",
          prompt=f'What do the following technologies have in common?\n\nCustomer reviews:\n"""\n{reviews}\n"""\n\nTheme:',
          temperature=0,
          max_tokens=64,
          top_p=1,
          frequency_penalty=0,
          presence_penalty=0,
      )
      print(response["choices"][0]["text"].replace("\n", ""))

      print(reviews)
      clusterstextlist.append("Cluster "+str(i)+"\nTheme:"+response["choices"][0]["text"].replace("\n", "")+'\n'+reviews+'\n'+"-" * 10+'\n\n')
  
  textlist=[mainlistofanswers[0],"SEPERATOR",mainlistofanswers[1],"SEPERATOR",mainlistofanswers[2],"SEPERATOR",mainlistofanswers[3],"SEPERATOR",desc_tmp,"SEPERATOR","".join(clusterstextlist)]
  ###create file with new info locally & upload to bucket
  with open('lastradartext.txt', 'w') as f:
    for line in textlist:
        f.write(f"{line}\n")
        
  with fs.open('trends_chrome_extension_bucket/lastradartext.txt', 'wb') as file:
      for line in textlist:
        file.write(f"{line}\n".encode())
          
  print('Came here 8')   

  ###read it and put in output

  with open('lastradartext.txt', 'r') as file:
    data_old = file.read()
  value1,value2,value3,value4,value5,value6=str(data_old).split('SEPERATOR')

  ###upload image to cloud for next run display
  with open('lasttechradar.png','rb') as image_file:
    image_string = image_file.read()
    with fs.open('trends_chrome_extension_bucket/lasttechradar.png', 'wb') as file:
        file.write(image_string)
    
  return 'lasttechradar.png',mainlistofanswers[0],mainlistofanswers[1],mainlistofanswers[2],mainlistofanswers[3],desc_tmp,"".join(clusterstextlist)

with gr.Blocks() as demo:
    gr.Markdown("<h1><center>ChatGPT Technology Radar</center></h1>")
    gr.Markdown(
        """What are the top technologies as of now? Let us query top consulting company websites & use ChatGPT to understand. \n\nShowcases ChatGPT integrated with real data. It shows how to get real-time data and marry it with ChatGPT capabilities. This demonstrates 'Chain of Thought' thinking using ChatGPT.\nLangChain & GPT-Index are both used.\n ![visitors](https://visitor-badge.glitch.me/badge?page_id=hra.ChatGPT-Tech-Radar)"""
        )
    
    with gr.Row() as row:
      textboxopenapi = gr.Textbox(placeholder="Enter OpenAPI Key...", lines=1,label='OpenAPI Key')
      btn = gr.Button("Refresh")
    with gr.Row() as row:
      with gr.Column():
        output_image = gr.components.Image(label="Tech Radar",value='lasttechradar.png')    
      with gr.Column():
        outputMck = gr.Textbox(placeholder=value1, lines=1,label='McKinsey View')
        outputBcg = gr.Textbox(placeholder=value2, lines=1,label='BCG View')
        outputBain = gr.Textbox(placeholder=value3, lines=1,label='Bain View')
        outputAcc = gr.Textbox(placeholder=value4, lines=1,label='Accenture View')
    with gr.Row() as row:
      with gr.Column():
        outputdesc = gr.Textbox(placeholder=value5, lines=1,label='Description')
      with gr.Column():
        outputclusters = gr.Textbox(placeholder=value6, lines=1,label='Clusters')


    btn.click(getstuff, inputs=[textboxopenapi],outputs=[output_image,outputMck,outputBcg,outputBain,outputAcc,outputdesc,outputclusters])
    
    demo.load(getlastimage,[],[output_image,outputMck,outputBcg,outputBain,outputAcc,outputdesc,outputclusters])
    
demo.launch(debug=True)