import os
import torch
import gradio as gr
import requests
from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
from peft import PeftModel, PeftConfig
from textwrap import wrap, fill

## using Falcon 7b Instruct
Falcon_API_URL = "https://api-inference.huggingface.co/models/tiiuae/falcon-7b-instruct"
hf_token = os.getenv("HUGGINGFACE_TOKEN")
HEADERS = {"Authorization": "Bearer {hf_token}"}
def falcon_query(payload):
    response = requests.post(Falcon_API_URL, headers=HEADERS, json=payload)
    return response.json()
def falcon_inference(input_text):
    payload = {"inputs": input_text}
    return falcon_query(payload)

## using Mistral
Mistral_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
def mistral_query(payload):
    response = requests.post(Mistral_API_URL , headers=HEADERS, json=payload)
    return response.json()
def mistral_inference(input_text):
    payload = {"inputs": input_text}
    return mistral_query(payload)

# Functions to Wrap the Prompt Correctly
def wrap_text(text, width=90):
    lines = text.split('\n')
    wrapped_lines = [fill(line, width=width) for line in lines]
    wrapped_text = '\n'.join(wrapped_lines)
    return wrapped_text

class ChatbotInterface():
    def __init__(self, name, system_prompt="You are an expert medical analyst that helps users with any medical related information."):
        self.name = name
        self.system_prompt = system_prompt
        self.chatbot = gr.Chatbot()
        self.chat_history = []
        
        with gr.Row() as row:
            row.justify = "end"
            self.msg = gr.Textbox(scale=7)
            #self.msg.change(fn=, inputs=, outputs=)
            self.submit = gr.Button("Submit", scale=1)

        clear = gr.ClearButton([self.msg, self.chatbot])
        chat_history = []
        
        self.submit.click(self.respond, [self.msg, self.chatbot], [self.msg, self.chatbot])
    
    def respond(self, msg, chatbot):
        raise NotImplementedError

class GaiaMinimed(ChatbotInterface):
    def __init__(self, name, system_prompt="You are an expert medical analyst that helps users with any medical related information."):
        super().__init__(name, system_prompt)
        
    def respond(self, msg, history):
            formatted_input = f"{{{{ {self.system_prompt} }}}}\nUser: {msg}\n{self.name}:"
            input_ids = tokenizer.encode(
                formatted_input, 
                return_tensors="pt", 
                add_special_tokens=False
            )
            response = peft_model.generate(
                input_ids=input_ids, 
                max_length=500, 
                use_cache=False,
                early_stopping=False,
                bos_token_id=peft_model.config.bos_token_id,
                eos_token_id=peft_model.config.eos_token_id,
                pad_token_id=peft_model.config.eos_token_id,
                temperature=0.4,
                do_sample=True
            )
            response_text = tokenizer.decode(response[0], skip_special_tokens=True)
            
            self.chat_history.append([formatted_input, response_text])

            return "", self.chat_history

class FalconBot(ChatbotInterface):
    def __init__(self, name, system_prompt="You are an expert medical analyst that helps users with any medical related information."):
        super().__init__(name, system_prompt)
        
    def respond(self, msg, chatbot):
        falcon_response = falcon_inference(msg)
        falcon_output = falcon_response[0]["generated_text"]
        self.chat_history.append([msg, falcon_output])
        return "", falcon_output

class MistralBot(ChatbotInterface):
    def __init__(self, name, system_prompt="You are an expert medical analyst that helps users with any medical related information."):
        super().__init__(name, system_prompt)
    
    def respond(self, msg, chatbot):
        mistral_response = mistral_inference(msg)
        mistral_output = mistral_response[0]["generated_text"]
        self.chat_history.append([msg, mistral_output])
        return "", mistral_output

if __name__ == "__main__":
    # Define the device
    device = "cuda" if torch.cuda.is_available() else "cpu"

    # Use the base model's ID
    base_model_id = "tiiuae/falcon-7b-instruct"
    model_directory = "Tonic/GaiaMiniMed"

    # Instantiate the Tokenizer
    tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True, padding_side="left")
    
    # Specify the configuration class for the model
    model_config = AutoConfig.from_pretrained(base_model_id)
    # Load the PEFT model with the specified configuration
    peft_model = AutoModelForCausalLM.from_pretrained(model_directory, config=model_config)
    peft_model = PeftModel.from_pretrained(peft_model, model_directory)
    
    with gr.Blocks() as demo:        
        with gr.Row() as intro:
            gr.Markdown(
                """
                ## MedChat
                Welcome to MedChat, a medical assistant chatbot! You can currently chat with three chatbots that are trained on the same medical dataset.
                
                If you want to compare the output of each model, click the submit to all button and see the magic happen!
                """
            )
        with gr.Row() as row:
            with gr.Column() as col1:
                with gr.Tab("GaiaMinimed") as gaia:
                    gaia_bot = GaiaMinimed("GaiaMinimed")
            with gr.Column() as col2:
                with gr.Tab("MistralMed") as mistral:
                    mistral_bot = MistralBot("MistralMed") 
                with gr.Tab("Falcon-7B") as falcon7b:
                    falcon_bot = FalconBot("Falcon-7B")
        
        gaia_bot.msg.input(fn=lambda s: (s[::1], s[::1]), inputs=gaia_bot.msg, outputs=[mistral_bot.msg, falcon_bot.msg])
        mistral_bot.msg.input(fn=lambda s: (s[::1], s[::1]), inputs=mistral_bot.msg, outputs=[gaia_bot.msg, falcon_bot.msg])
        falcon_bot.msg.input(fn=lambda s: (s[::1], s[::1]), inputs=falcon_bot.msg, outputs=[gaia_bot.msg, mistral_bot.msg])
                
    demo.launch()