File size: 3,546 Bytes
c9e8e4a
a16fa71
c9e8e4a
 
 
3bce3fb
a16fa71
c9e8e4a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1e8c169
dc737c0
 
7982bc6
a16fa71
 
 
7982bc6
d297c51
fc158ea
c9e8e4a
7cf1a13
33c3beb
3bce3fb
7982bc6
 
 
a16fa71
9be3f4c
f25abd8
3e9df94
1cb474a
3bce3fb
c9e8e4a
 
 
3bce3fb
a16fa71
 
f25abd8
5b9eb09
f25abd8
 
 
 
7cf1a13
a16fa71
22fef42
 
 
 
 
a16fa71
7cf1a13
 
 
a16fa71
 
 
7cf1a13
 
 
 
3283b93
a16fa71
 
7cf1a13
 
 
 
a16fa71
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import pipeline
import torch
import json
import pandas as pd
import requests

@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", "InCoder"]
selected_models = st.sidebar.multiselect('Select code generation models to compare:', models, default=["CodeParrot"])

st.sidebar.header("Tasks")
tasks = [" ", "Pretraining datasets", "Model architecture", "Model evaluation", "Code generation"]
selected_task = st.sidebar.selectbox("Select a task:", tasks)


if selected_task == " ":
    st.title("Code Generation Models comparison")
    with open("intro.txt", "r") as f:
        intro = f.read()
    st.markdown(intro)
    
elif selected_task == "Pretraining datasets":
    st.title("Pretraining datasets πŸ“š")
    st.markdown("Preview of some code files from Github repositories")   
    df = pd.read_csv("data_preview.csv")
    st.dataframe(df)
    for model in selected_models:
        with open(f"datasets/{model.lower()}.txt", "r") as f:
            text = f.read()
        st.markdown(f"### {model}:")
        st.markdown(text)  
        
elif selected_task == "Model architecture":
    st.title("Model architecture πŸ”¨")
    for model in selected_models:
        with open(f"architectures/{model.lower()}.txt", "r") as f:
            text = f.read()
        st.markdown(f"## {model}:")
        st.markdown(text) 
        
elif selected_task == "Model evaluation":
    st.title("Code models evaluation πŸ“Š")
    with open("evaluation/intro.txt", "r") as f:
        intro = f.read()
    st.markdown(intro)
    
elif selected_task == "Code generation":
    st.title("Code generation πŸ’»")
    st.sidebar.header("Examples")
    examples = load_examples()
    example_names = [example["name"] for example in examples]
    name2id = dict([(name, i) for i, name in enumerate(example_names)])
    selected_example = st.sidebar.selectbox("Select one of the following examples:", example_names)
    example_text = examples[name2id[selected_example]]["value"]
    default_length = examples[name2id[selected_example]]["length"]
    st.sidebar.header("Generation settings")
    temperature = st.sidebar.slider("Temperature:", value=0.2, min_value=0.0, step=0.1, max_value=2.0)
    max_new_tokens = st.sidebar.slider("Number of tokens to generate:", value=default_length, min_value=8, step=8, max_value=256)
    seed = st.sidebar.slider("Random seed:", value=42, min_value=0, step=1, max_value=1000)
    gen_prompt = st.text_area("Generate code with prompt:", value=example_text, height=220,).strip()
    if st.button("Generate code!"):
        with st.spinner("Generating code..."):
            for model in selected_models:
                url = f'https://hf.space/embed/loubnabnl/{model.lower()}-subspace/+/api/predict/'
                r = requests.post(url=url, json={"data": [gen_prompt, max_new_tokens, temperature, seed]})
                generated_text = r.json()['data'][0]
                st.markdown(f"{model}:")
                st.code(generated_text)