import gradio as gr
from model import GPTConfig, GPT
import torch              
from contextlib import nullcontext
import os
import pickle

def remove_caseifer(text):
    new_text = ""
    i = 0
    while i < len(text):
        if text[i] == "^":
            if i+1 < len(text):
                new_text += text[i+1].upper()
                i += 1
            else:
                pass  # skip this index
        else:
            new_text += text[i]
        i += 1
    return new_text
    
def add_caseifer(text):
    new_text = ""
    for char in text:
        if char.isupper():
            new_text += "^" + char.lower()
        else:
            new_text += char
    return new_text

max_new_tokens = 88 # number of tokens generated in each sample
temperature = 0.8 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
top_k = None # retain only the top_k most likely tokens, clamp others to have 0 probability
device = 'cpu' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.
dtype = 'bfloat16' # 'float32' or 'bfloat16' or 'float16'
out_dir = 'Eml' # ignored if init_from is not 'resume'


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 = os.path.join(out_dir, '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)

meta_path = os.path.join(out_dir, 'meta.pkl')
load_meta = os.path.exists(meta_path)

with open(meta_path, 'rb') as f:
    meta = pickle.load(f)
# TODO want to make this more general to arbitrary encoder/decoder schemes
stoi, itos = meta['stoi'], meta['itos']
encode = lambda s: [stoi[c] for c in s]
decode = lambda l: ''.join([itos[i] for i in l])

def gen(input):
    generated_text = ''
    start_ids = encode(add_caseifer(input))
    x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
    for idx_next in model.generate_streaming(x, max_new_tokens, temperature=temperature, top_k=top_k):
        # convert the index to a character and print it to the screen
        char = decode([idx_next])


        # check for newline character
        if char == '\n':
            out = remove_caseifer(generated_text)
            return input + out
        else:
            # append the character to the generated text
            generated_text += char
    
def load_model(model_name):
    ckpt_path = os.path.join(out_dir, model_name)
    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)
    return model

def get_model_list():
    models_dir = out_dir
    model_files = os.listdir(models_dir)
    model_files = [f for f in model_files if f.endswith('.pt')]
    return model_files

def gen(input, model_name):
    model = load_model(model_name)
    # the rest of your gen function using this model...

iface = gr.Interface(fn=gen, 
                     inputs=["text", gr.inputs.Dropdown(get_model_list(), label="Select Model")], 
                     outputs="text")
iface.launch()