# Copyright (c) Microsoft
# Modified from Visual ChatGPT Project https://github.com/microsoft/TaskMatrix/blob/main/visual_chatgpt.py

import os
import gradio as gr
import re
import uuid
from PIL import Image, ImageDraw, ImageOps
import numpy as np
import argparse
import inspect

from langchain.agents.initialize import initialize_agent
from langchain.agents.tools import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.llms.openai import OpenAI
import torch
from PIL import Image, ImageDraw, ImageOps
from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering

VISUAL_CHATGPT_PREFIX = """
    Caption Anything Chatbox (short as CATchat) is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. CATchat is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.

    As a language model, CATchat can not directly read images, but it has a list of tools to finish different visual tasks. CATchat can invoke different tools to indirectly understand pictures. 
    
    Visual ChatGPT  has access to the following tools:"""


# VISUAL_CHATGPT_PREFIX = """Visual ChatGPT is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. Visual ChatGPT is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.

# Visual ChatGPT is able to process and understand large amounts of text and images. As a language model, Visual ChatGPT can not directly read images, but it has a list of tools to finish different visual tasks. Each image will have a file name formed as "chat_image/xxx.png", and Visual ChatGPT can invoke different tools to indirectly understand pictures. When talking about images, Visual ChatGPT is very strict to the file name and will never fabricate nonexistent files. Visual ChatGPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the image content and image file name.

# Visual ChatGPT is aware of the coordinate of an object in the image, which is represented as a point (X, Y) on the object. Note that (0, 0) represents the bottom-left corner of the image. 

# Human may provide new figures to Visual ChatGPT with a description. The description helps Visual ChatGPT to understand this image, but Visual ChatGPT should use tools to finish following tasks, rather than directly imagine from the description.

# Overall, Visual ChatGPT is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. 


# TOOLS:
# ------

# Visual ChatGPT  has access to the following tools:"""

VISUAL_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format:

"Thought: Do I need to use a tool? Yes
Action: the action to take, should be one of [{tool_names}], remember the action must to be one tool
Action Input: the input to the action
Observation: the result of the action"

When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:

"Thought: Do I need to use a tool? No
{ai_prefix}: [your response here]"

"""

VISUAL_CHATGPT_SUFFIX = """
Begin Chatting!

Previous conversation history:
{chat_history}

New input: {input}
Since CATchat is a text language model, CATchat must use tools iteratively to observe images rather than imagination.
The thoughts and observations are only visible for CATchat, CATchat should remember to repeat important information in the final response for Human. 

Thought: Do I need to use a tool? {agent_scratchpad} (You are strictly to use the aforementioned "Thought/Action/Action Input/Observation" format as the answer.)"""

os.makedirs('chat_image', exist_ok=True)


def prompts(name, description):
    def decorator(func):
        func.name = name
        func.description = description
        return func
    return decorator

def cut_dialogue_history(history_memory, keep_last_n_words=500):
    if history_memory is None or len(history_memory) == 0:
        return history_memory
    tokens = history_memory.split()
    n_tokens = len(tokens)
    print(f"history_memory:{history_memory}, n_tokens: {n_tokens}")
    if n_tokens < keep_last_n_words:
        return history_memory
    paragraphs = history_memory.split('\n')
    last_n_tokens = n_tokens
    while last_n_tokens >= keep_last_n_words:
        last_n_tokens -= len(paragraphs[0].split(' '))
        paragraphs = paragraphs[1:]
    return '\n' + '\n'.join(paragraphs)

def get_new_image_name(folder='chat_image', func_name="update"):
    this_new_uuid = str(uuid.uuid4())[:8]
    new_file_name = f'{func_name}_{this_new_uuid}.png'
    return os.path.join(folder, new_file_name)

class VisualQuestionAnswering:
    def __init__(self, device):
        print(f"Initializing VisualQuestionAnswering to {device}")
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.device = device
        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
        self.model = BlipForQuestionAnswering.from_pretrained(
            "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype).to(self.device)
        # self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-capfilt-large")
        # self.model = BlipForQuestionAnswering.from_pretrained(
            # "Salesforce/blip-vqa-capfilt-large", torch_dtype=self.torch_dtype).to(self.device)

    @prompts(name="Answer Question About The Image",
             description="useful when you need an answer for a question based on an image. "
                         "like: what is the background color of the last image, how many cats in this figure, what is in this figure. "
                         "The input to this tool should be a comma separated string of two, representing the image_path and the question")
    def inference(self, inputs):
        image_path, question = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        raw_image = Image.open(image_path).convert('RGB')
        inputs = self.processor(raw_image, question, return_tensors="pt").to(self.device, self.torch_dtype)
        out = self.model.generate(**inputs)
        answer = self.processor.decode(out[0], skip_special_tokens=True)
        print(f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, "
              f"Output Answer: {answer}")
        return answer
    
def build_chatbot_tools(load_dict):
    print(f"Initializing ChatBot, load_dict={load_dict}")
    models = {}
    # Load Basic Foundation Models
    for class_name, device in load_dict.items():
        models[class_name] = globals()[class_name](device=device)

    # Load Template Foundation Models
    for class_name, module in globals().items():
        if getattr(module, 'template_model', False):
            template_required_names = {k for k in inspect.signature(module.__init__).parameters.keys() if k!='self'}
            loaded_names = set([type(e).__name__ for e in models.values()])
            if template_required_names.issubset(loaded_names):
                models[class_name] = globals()[class_name](
                    **{name: models[name] for name in template_required_names})
                
    tools = []
    for instance in models.values():
        for e in dir(instance):
            if e.startswith('inference'):
                func = getattr(instance, e)
                tools.append(Tool(name=func.name, description=func.description, func=func))
    return tools

class ConversationBot:
    def __init__(self, tools, api_key=""):
        # load_dict = {'VisualQuestionAnswering':'cuda:0', 'ImageCaptioning':'cuda:1',...}
        llm = OpenAI(model_name="gpt-3.5-turbo", temperature=0, openai_api_key=api_key)
        self.llm = llm
        self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output')
        self.tools = tools
        self.current_image = None
        self.point_prompt = ""
        self.agent = initialize_agent(
            self.tools,
            self.llm,
            agent="conversational-react-description",
            verbose=True,
            memory=self.memory,
            return_intermediate_steps=True,
            agent_kwargs={'prefix': VISUAL_CHATGPT_PREFIX, 'format_instructions': VISUAL_CHATGPT_FORMAT_INSTRUCTIONS,
                          'suffix': VISUAL_CHATGPT_SUFFIX}, )

    def constructe_intermediate_steps(self, agent_res):
        ans = []
        for action, output in agent_res:
            if hasattr(action, "tool_input"):
                use_tool = "Yes"
                act = (f"Thought: Do I need to use a tool? {use_tool}\nAction: {action.tool}\nAction Input: {action.tool_input}", f"Observation: {output}")
            else:
                use_tool = "No"
                act = (f"Thought: Do I need to use a tool? {use_tool}", f"AI: {output}")
            act= list(map(lambda x: x.replace('\n', '<br>'), act))
            ans.append(act)
        return ans
    
    def run_text(self, text, state, aux_state):
        self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500)
        if self.point_prompt != "":
            Human_prompt = f'\nHuman: {self.point_prompt}\n'
            AI_prompt = 'Ok'
            self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt
            self.point_prompt = ""
        res = self.agent({"input": text})
        res['output'] = res['output'].replace("\\", "/")
        response = re.sub('(chat_image/\S*png)', lambda m: f'![](/file={m.group(0)})*{m.group(0)}*', res['output'])
        state = state + [(text, response)]
        
        aux_state = aux_state + [(f"User Input: {text}", None)]
        aux_state = aux_state + self.constructe_intermediate_steps(res['intermediate_steps'])
        print(f"\nProcessed run_text, Input text: {text}\nCurrent state: {state}\n"
              f"Current Memory: {self.agent.memory.buffer}\n"
              f"Aux state: {aux_state}\n"
              )
        return state, state, aux_state, aux_state


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--load', type=str, default="VisualQuestionAnswering_cuda:0")
    parser.add_argument('--port', type=int, default=1015)
    
    args = parser.parse_args()
    load_dict = {e.split('_')[0].strip(): e.split('_')[1].strip() for e in args.load.split(',')}
    tools = build_chatbot_tools(load_dict)
    bot = ConversationBot(tools)
    with gr.Blocks(css="#chatbot .overflow-y-auto{height:500px}") as demo:
        with gr.Row():
            chatbot = gr.Chatbot(elem_id="chatbot", label="Visual ChatGPT").style(height=1000,scale=0.5)
            auxwindow = gr.Chatbot(elem_id="chatbot", label="Aux Window").style(height=1000,scale=0.5)
        state = gr.State([])
        aux_state = gr.State([])
        with gr.Row():
            with gr.Column(scale=0.7):
                txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(
                    container=False)
            with gr.Column(scale=0.15, min_width=0):
                clear = gr.Button("Clear")
            with gr.Column(scale=0.15, min_width=0):
                btn = gr.UploadButton("Upload", file_types=["image"])

        txt.submit(bot.run_text, [txt, state, aux_state], [chatbot, state, aux_state, auxwindow])
        txt.submit(lambda: "", None, txt)
        btn.upload(bot.run_image, [btn, state, txt, aux_state], [chatbot, state, txt, aux_state, auxwindow])
        clear.click(bot.memory.clear)
        clear.click(lambda: [], None, chatbot)
        clear.click(lambda: [], None, auxwindow)
        clear.click(lambda: [], None, state)
        clear.click(lambda: [], None, aux_state)
        demo.launch(server_name="0.0.0.0", server_port=args.port, share=True)