import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
from transformers import pipeline
import torch
import json


@st.cache(allow_output_mutation=True)
def load_tokenizer(model_ckpt):
    return AutoTokenizer.from_pretrained(model_ckpt)

@st.cache(allow_output_mutation=True)
def load_model(model_ckpt):
    model = AutoModelForCausalLM.from_pretrained(model_ckpt, low_cpu_mem_usage=True)
    return model

@st.cache()
def load_examples():
    with open("examples.json", "r") as f:
        examples = json.load(f)
    return examples

st.set_page_config(page_icon=':laptop:', layout="wide")

st.sidebar.header("Models:")
models = ["CodeParrot", "OPT", "InCoder"]
selected_models = st.sidebar.multiselect('Select code generation models to compare', 
                         models,
                         default=["CodeParrot"])
st.sidebar.header("Tasks:")
tasks = ["Model architecture", "Model evaluation", "Pretraining dataset", "Prompting"]
selected_task = st.sidebar.selectbox("Select a task:", tasks)

architectures = {}
datasets = {}
pipelines = {}

if selected_task == "Pretraining dataset":
    st.title("Pretraining Datasets 📚")
    for model in selected_models:
        with open(f"datasets/{model.lower()}.txt", "r") as f:
            text = f.read()
        #datasets[model] = text 
        st.markdown(f"## {model}:")
        st.markdown(text)
elif selected_task == "Prompting":
    for model in selected_models:
        if model == "CodeParrot":
            tokenizer = load_tokenizer("lvwerra/codeparrot")
            model = load_model("lvwerra/codeparrot")
            pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
            pipelines[model] = pipe
        elif model == "InCoder":
            tokenizer = load_tokenizer("facebook/incoder-1B")
            model = load_model("facebook/incoder-1B")
            pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
            pipelines[model] = pipe
        else:
            tokenizer = load_tokenizer("facebook/opt-1.3b")
            model = load_model("facebook/opt-1.3b")
            pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
            pipelines[model] = pipe