#Importing all the necessary needed  libraries
import torch
import requests
import numpy as np
import pandas as pd
import gradio as gr
from io import BytesIO
from PIL import Image as PILIMAGE
from IPython.display import Image
from IPython.core.display import HTML
from transformers import CLIPProcessor, CLIPModel, CLIPTokenizer
from sentence_transformers import SentenceTransformer, util
import os
import json
import requests
import langchain
from tqdm import tqdm
from langchain.text_splitter import CharacterTextSplitter
images = [] 
prompt_templates = {"DefaultChatGPT": ""}
# Streaming endpoint
API_URL = "https://api.openai.com/v1/chat/completions"  # os.getenv("API_URL") + "/generate_stream"
convo_id = 'default'
#5c72c157a8fd54357bd13112cd71952a
import time
images1= pd.read_csv("./images.csv")

openai_api_key='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'
m=0
style1= pd.read_csv('./stylesu.csv')
feature_info= list(style1.columns)
feature_info = ' '.join([str(elem) for elem in feature_info])
info= style1.values.tolist()
final_info=''
for i in info:
    li=''
    li=' '.join([str(elem) for elem in i])
    final_info += li+'\n'



def on_prompt_template_change(prompt_template):
    if not isinstance(prompt_template, str): return
    if prompt_template:
        return prompt_templates[prompt_template]
    else:
        ''

def get_empty_state():
    return {"total_tokens": 0, "messages": []}

def get_prompt_templates():
    with open('./prompts.json','r',encoding='utf8') as fp:
        json_data = json.load(fp)
        for data in json_data:
            act = data['act']
            prompt = data['prompt']
            prompt_templates[act] = prompt
        # reader = csv.reader(csv_file)
        # next(reader)  # skip the header row
        # for row in reader:
        #     if len(row) >= 2:
        #         act = row[0].strip('"')
        #         prompt = row[1].strip('"')
        #         prompt_templates[act] = prompt

        choices = list(prompt_templates.keys())
        choices = choices[:1] + sorted(choices[1:])
        return gr.update(value=choices[0], choices=choices)



def run(pr=gr.Progress(track_tqdm=True)):
    #if(chat_counter==0):
    message_prompt=[]
    x=len(final_info)
    print(x/2000)
    for i in range(0,x,2000): #final_texts:
        message_prompt.append(final_info[i:i+2000]+" Remember this along with previous prompts as it makes up the csv file")
            #//there
    prompt_template = "I want you to act as a Product recommender and read the CSV file I will provide you. I need you to thoroughly review the CSV file and give recommendations based on the input afterward. You should recommend me the product by displaying its id, and description. The csv features are:" +feature_info+ "The csv information is as follows:"
    payload = {
                    "model": "gpt-3.5-turbo",
                    "messages": [{"role":"system", "content":prompt_template}],
                    "temperature": 0.1,
                    "top_p": 1.0,
                    "n": 1,
                    "stream": True,
                    "presence_penalty": 0,
                    "frequency_penalty": 0,
                }
            
    headers = {
                    "Content-Type": "application/json",
                    "Authorization": f"Bearer {openai_api_key}"
                }
    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
          
    for i in pr.tqdm(message_prompt):
        payload = {
                    "model": "gpt-3.5-turbo",
                    "messages": [{"role":"system", "content":i}],
                    "temperature": 0.1,
                    "top_p": 1.0,
                    "n": 1,
                    "stream": True,
                    "presence_penalty": 0,
                    "frequency_penalty": 0, }
        response = requests.post(API_URL, headers=headers, json=payload, stream=True)
        time.sleep(0.01)
        pr(1/2210)

    print("completed")
    
def predict(inputs, prompt_template, temperature, openai_api_key, chat_counter, context_length, chatbot=[],
            history=[]):

 # # repetition_penalty, top_k
    if inputs==None:
        inputs = ''
    prompt_template = "I want you to act as a Product recommender and read the CSV file I will provide you. I need you to thoroughly review the CSV file and give recommendations based on the input afterward. You should recommend me the product by displaying its id, and description. The csv features are:" +feature_info+ "The csv information is as follows:"
        
    headers = {
                    "Content-Type": "application/json",
                    "Authorization": f"Bearer {openai_api_key}"
                }    
    payload = {
        "model": "gpt-3.5-turbo",
        "messages": [{"role": "user", "content": f"{inputs}"}],
        "temperature": 0.1,
        "top_p": 1.0,
        "n": 1,
        "stream": True,
        "presence_penalty": 0,
        "frequency_penalty": 0,
    }
    
        
        
                # print(f"chat_counter - {chat_counter}")
    if chat_counter != 0:
        messages = []
        # print(chatbot)
        # print(chatbot[-context_length:])
        # print(context_length)
        for data in chatbot[-context_length:]:
            temp1 = {}
            temp1["role"] = "user"
            temp1["content"] = data[0]
            temp2 = {}
            temp2["role"] = "assistant"
            temp2["content"] = data[1]
            messages.append(temp1)
            messages.append(temp2)
        temp3 = {}
        temp3["role"] = "user"
        temp3["content"] = inputs
        messages.append(temp3)
        # print(messages)
        # messages
        payload = {
            "model": "gpt-3.5-turbo",
            "messages": [{"role": "system", "content": prompt_template}]+messages,  # [{"role": "user", "content": f"{inputs}"}],
            "temperature": temperature,  # 1.0,
            "n": 1,
            "stream": True,
            "presence_penalty": 0,
            "frequency_penalty": 0,
        }



    history.append(inputs)
    # print(f"payload is - {payload}")
    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
    # print('payload',payload)
    response = requests.post(API_URL, headers=headers, json=payload, stream=True)

    # print('response', response)
    # print('content',response.content)
    # print('text', response.text)
    if response.status_code != 200:
        try:
            payload['id'] = response.content['id']
            response = requests.post(API_URL, headers=headers, json=payload, stream=True)
            if response.status_code != 200:
                payload['id'] = response.content['id']
                response = requests.post(API_URL, headers=headers, json=payload, stream=True)
        except:
            pass

    # print('status_code', response.status_code)
    # response = requests.post(API_URL, headers=headers, json=payload, stream=True)
    token_counter = 0
    partial_words = ""
    counter = 0
    if response.status_code==200:
        chat_counter += 1
        # print('chunk')
        for chunk in response.iter_lines():
            # Skipping first chunk
            if counter == 0:
                counter += 1
                continue
            # check whether each line is non-empty
            chunk = chunk.decode("utf-8")[6:]
            if chunk:
                # print(chunk)
                if chunk=='[DONE]':
                    break
                resp: dict = json.loads(chunk)
                choices = resp.get("choices")
                if not choices:
                    continue
                delta = choices[0].get("delta")
                if not delta:
                    continue
                # decode each line as response data is in bytes
                if len(chunk) > 12 and "content" in resp['choices'][0]['delta']:
                    # if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
                    #  break
                    partial_words = partial_words + resp['choices'][0]["delta"]["content"]
                    # print(partial_words)
                    if token_counter == 0:
                        history.append(" " + partial_words)
                    else:
                        history[-1] = partial_words
                    chat = [(history[i], history[i + 1]) for i in
                            range(0, len(history) - 1, 2)]  # convert to tuples of list
                    # print(chat)
                    token_counter += 1
                    yield chat, history, chat_counter  # resembles {chatbot: chat, state: history}
    else:
        chat = [(history[i], history[i + 1]) for i in
                range(0, len(history) - 1, 2)]  # convert to tuples of list
        chat.append((inputs, "OpenAI Network Error. please try again"))
        token_counter += 1
        yield chat, history, chat_counter  # resembles {chatbot: chat, state: history}
       



def reset_textbox():
    return gr.update(value='')

def clear_conversation(chatbot):
    return gr.update(value=None, visible=True), [], [], gr.update(value=0)


   
def galleryim():
    
    count=0
    for i in images1['filename']:
        count+=1
        if count==50:
             break
        photo_data = images1[images1["filename"] == i].iloc[0]
        response = requests.get(photo_data["link"] )
        try:
            img = PILIMAGE.open(BytesIO(response.content))
        except:
            print("File not found")
        else:
            images.append(img)
    return images

title = """<h1 align="center">ChatGPTDatasetSearch</h1>"""
description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
```
User: <utterance>
Assistant: <utterance>
User: <utterance>
Assistant: <utterance>
...
```
In this app, you can explore the outputs of a gpt-3.5-turbo LLM.
"""
with gr.Blocks(css="""#col_container {width: 800px; margin-left: auto; margin-right: auto;}
                #chatbot {height: 500px; overflow: auto;}
                #inputs {font-size: 20px;}
                #prompt_template_preview {padding: 1em; border-width: 1px; border-style: solid; border-color: #e0e0e0; border-radius: 4px;}""") as demo:
    gr.HTML(title)
    with gr.Column(variant="panel"):
        
        gr.HTML(  """<b><center><h1>1001Epochs</h1></center></b>
                    <p><center>TOP THREE images that best match the search query provided by the user</center></p>
                    """)
    
    with gr.Row():
        with gr.Column(scale=0.50):
            gallery = gr.Gallery( value=galleryim(),
            label="Generated images", show_label=False, elem_id="gallery",every=60).style(columns=5, container=True)
       
        with gr.Column(elem_id="col_container"):
            
            openai_api_key = gr.Textbox(type='password', label="Enter API Key",placeholder="sk-xxxxxxxx")
            button1=gr.Button("feed the csv into model")
            button1.click(run, show_progress=True)
            chatbot = gr.Chatbot(elem_id='chatbot')  # c
            inputs = gr.Textbox(show_label=False, placeholder="Enter Content",elem_id="inputs",value='')  # t
            state = gr.State([])  # s
            # state = gr.State(get_empty_state())
            b1 = gr.Button("Submit")
            btn_clear_conversation = gr.Button("🔃 New Conversation")
    
            # inputs, top_p, temperature, top_k, repetition_penalty
            with gr.Accordion("Advanced settings", open=False,):
                context_length = gr.Slider(minimum=1, maximum=6, value=2, step=1, label="Dialogue Length",
                                           info="Associate the previous rounds of dialogues, the higher the value, the more tokens will be consumed")
                temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, label="Temperature",
                                        info="The higher the value, the stronger the creativity")
                prompt_template = gr.Dropdown(label="Choose robot type",
                                              choices=list(prompt_templates.keys()),visible=False)
                prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview",visible=False)
                # top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
                # repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
                chat_counter = gr.Number(value=0, visible=False, precision=0)

    inputs.submit(predict, [inputs, prompt_template, temperature, openai_api_key, chat_counter, context_length, chatbot, state],
                  [chatbot, state, chat_counter], )
    b1.click(predict, [inputs, prompt_template, temperature, openai_api_key, chat_counter, context_length, chatbot, state],
             [chatbot, state, chat_counter], )
    b1.click(reset_textbox, [], [inputs])

    btn_clear_conversation.click(clear_conversation, [], [inputs, chatbot, state, chat_counter])

    inputs.submit(reset_textbox, [], [inputs])
    prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview])
    demo.load(get_prompt_templates, inputs=None, outputs=[prompt_template], queur=False)

    # gr.Markdown(description)
    demo.queue(concurrency_count=10)
    demo.launch(debug=True)