import streamlit as st
from contextlib import nullcontext
import torch
import tiktoken
from model import GPTConfig, GPT
import sys

init_from = 'resume' # either 'resume' (from an out_dir) or a gpt2 variant (e.g. 'gpt2-xl')
out_dir = 'out' # ignored if init_from is not 'resume'
#start = "\n" # or "<|endoftext|>" or etc. Can also specify a file, use as: "FILE:prompt.txt"
num_samples = 1 # number of samples to draw
max_new_tokens = 400 # number of tokens generated in each sample
temperature = 0.3 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
top_k = 200 # retain only the top_k most likely tokens, clamp others to have 0 probability
seed = 1337
device = 'cpu' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.
dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32' or 'bfloat16' or 'float16'
compile = False # use PyTorch 2.0 to compile the model to be faster
exec(open('configurator.py').read()) # overrides from command line or config file
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast
ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)


# init from a model saved in a specific directory
ckpt_path = ('FineTune_ckpt.pt')
checkpoint = torch.load(ckpt_path, map_location=device)
gptconf = GPTConfig(**checkpoint['model_args'])
model = GPT(gptconf)
state_dict = checkpoint['model']
unwanted_prefix = '_orig_mod.'
for k,v in list(state_dict.items()):
    if k.startswith(unwanted_prefix):
        state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
model.load_state_dict(state_dict)

model.eval()
model.to(device)
if compile:
    model = torch.compile(model) # requires PyTorch 2.0 (optional)

enc = tiktoken.get_encoding("gpt2")
encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"})
decode = lambda l: enc.decode(l)

def get_response(prompt):
    start_ids = encode(prompt)
    x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])


    with torch.no_grad():
        with ctx:
            for k in range(num_samples):
                y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
                return (decode(y[0].tolist()).split('[EndOfText]')[0])


# ai_logo_path = 'https://raw.githubusercontent.com/chrischenhub/AlphaGPT/04dda92907aa0aee109b9a05c4521748288a3b7e/alphaplaylogo.svg'
# customer_logo_path = 'https://raw.githubusercontent.com/chrischenhub/AlphaGPT/04dda92907aa0aee109b9a05c4521748288a3b7e/montreal-canadiens-seeklogo.svg'
# # App title
# # Replicate Credentials
# with st.sidebar:
#     col1, col2 = st.columns([1, 3])
#     with col1:
#         st.image(ai_logo_path, width=60)
#     with col2:
#         st.title('AlphaPlay')
#     st.write('The following keys are used to generate system prompts')


# # Store LLM generated responses
# if "messages" not in st.session_state.keys():
#     st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]

# # Display or clear chat messages
# for message in st.session_state.messages:
#     if message["role"]=='assistant':
#         with st.chat_message(message["role"],avatar=ai_logo_path):
#             st.write(message["content"])
#     else:
#         with st.chat_message(message["role"],avatar=customer_logo_path):
#             st.write(message["content"]) 

# def clear_chat_history():
#     st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]
# st.sidebar.button('Clear Chat History', on_click=clear_chat_history)


# # User-provided prompt
# if prompt := st.chat_input():
#     st.session_state.messages.append({"role": "user", "content": prompt})
#     with st.chat_message("user", avatar=customer_logo_path):
#         st.write(prompt)

# # Generate a new response if last message is not from assistant
# if st.session_state.messages[-1]["role"] != "assistant":
#     with st.chat_message("assistant",avatar=ai_logo_path):
#         with st.spinner("Thinking..."):
#             response = get_response(prompt)
#             placeholder = st.empty()
#             full_response = ''
#             for item in response:
#                 full_response += item
#                 placeholder.markdown(full_response)
#             placeholder.markdown(full_response)
#     message = {"role": "assistant", "content": full_response}
#     st.session_state.messages.append(message)