File size: 4,791 Bytes
c9e8e4a
3bce3fb
a16fa71
aa07439
41d27ac
 
c9e8e4a
68bc50c
cddb272
fa5e188
25f90dd
c9e8e4a
 
f4313df
c9e8e4a
 
 
7c0d726
aa07439
 
 
25f90dd
 
 
 
 
 
aa07439
7c0d726
 
 
 
 
 
 
25f90dd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7c0d726
2dc5a7a
1e77c56
807f36d
 
c5fafcd
0d5adbc
 
1e77c56
0d5adbc
f70b655
7212da7
1e77c56
 
25f90dd
9d2b32b
0b16412
1e77c56
4bd868a
0d5adbc
 
7036561
1e77c56
25f90dd
29136c5
46dbbb1
1e77c56
0d5adbc
 
a7dffcb
1e77c56
 
0d5adbc
 
7036561
1e77c56
25f90dd
29136c5
606a970
29136c5
25f90dd
 
77615a0
25f90dd
 
596c6fa
606a970
12798fb
 
 
 
33147c8
12798fb
 
 
606a970
 
12798fb
 
 
 
 
 
 
cc14b64
12798fb
 
25f90dd
06d2b63
 
 
33147c8
06d2b63
 
 
aa07439
 
 
 
 
06d2b63
 
 
 
 
aa07439
06d2b63
aa07439
a5aaccd
 
cc3091d
 
 
091c31a
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
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
import json
import pandas as pd
import requests
import threading
import streamlit as st


MODELS = ["CodeParrot", "InCoder", "CodeGen", "PolyCoder"]
GENERATION_MODELS = ["CodeParrot", "InCoder", "CodeGen"]


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


def read_markdown(path):
    with open(path, "r") as f:
        output = f.read()
    st.markdown(output, unsafe_allow_html=True)


def generate_code(
    generations, model_name, gen_prompt, max_new_tokens, temperature, seed
):
    # call space using its API endpoint
    url = (
        f"https://hf.space/embed/loubnabnl/{model_name.lower()}-subspace/+/api/predict/"
    )
    r = requests.post(
        url=url, json={"data": [gen_prompt, max_new_tokens, temperature, seed]}
    )
    generated_text = r.json()["data"][0]
    generations.append(generated_text)


def generate_code_threads(
    generations, models, gen_prompt, max_new_tokens, temperature, seed
):
    threads = []
    for model_name in models:
        # create the thread
        threads.append(
            threading.Thread(
                target=generate_code,
                args=(
                    generations,
                    model_name,
                    gen_prompt,
                    max_new_tokens,
                    temperature,
                    seed,
                ),
            )
        )
        threads[-1].start()

    for t in threads:
        t.join()


st.set_page_config(page_icon=":laptop:", layout="wide")
with open("utils/table_contents.md", "r") as f:
    contents = f.read()
st.sidebar.markdown(contents)

# Introduction
st.title("Code generation with 🤗")
read_markdown("utils/intro.md")

# Code datasets
st.subheader("1 - Code datasets")
read_markdown("datasets/intro.md")
read_markdown("datasets/github_code.md")
col1, col2 = st.columns([1, 2])
with col1:
    selected_model = st.selectbox("", MODELS, key=1)
read_markdown(f"datasets/{selected_model.lower()}.md")


# Model architecture
st.subheader("2 - Model architecture")
read_markdown("architectures/intro.md")
col1, col2 = st.columns([1, 2])
with col1:
    selected_model = st.selectbox("", MODELS, key=2)
read_markdown(f"architectures/{selected_model.lower()}.md")

# Model evaluation
st.subheader("3 - Code models evaluation")
read_markdown("evaluation/intro.md")
read_markdown("evaluation/demo_humaneval.md")

# Code generation
st.subheader("4 - Code generation ✨")
read_markdown("generation/intro.md")
col1, col2, col3 = st.columns([7, 1, 6])
with col1:
    st.markdown("**Models**")
    selected_models = st.multiselect(
        "Select code generation models to compare:",
        GENERATION_MODELS,
        default=GENERATION_MODELS,
        key=3,
    )
    st.markdown(" ")
    st.markdown("**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.selectbox(
        "Select one of the following examples or implement yours:", example_names
    )
    example_text = examples[name2id[selected_example]]["value"]
    default_length = examples[name2id[selected_example]]["length"]
with col3:
    st.markdown("**Generation settings**")
    temperature = st.slider(
        "Temperature:", value=0.2, min_value=0.0, step=0.1, max_value=2.0
    )
    max_new_tokens = st.slider(
        "Number of tokens to generate:",
        value=default_length,
        min_value=8,
        step=4,
        max_value=256,
    )
    seed = st.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=200,
).strip()
if st.button("Generate code!"):
    with st.spinner("Generating code..."):
        # use threading
        generations = []
        generate_code_threads(
            generations,
            selected_models,
            gen_prompt=gen_prompt,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            seed=seed,
        )
        for i in range(len(generations)):
            st.markdown(f"**{selected_models[i]}**")
            st.code(generations[i])
        if len(generations) < len(selected_models):
            st.markdown("<span style='color:red'>Warning: Some models run into timeout, you can try generating code using the original subspaces: [InCoder](https://huggingface.co/spaces/loubnabnl/incoder-subspace), [CodeGen](https://huggingface.co/spaces/loubnabnl/codegen-subspace), [CodeParrot](https://huggingface.co/spaces/loubnabnl/codeparrot-subspace)</span>", unsafe_allow_html=True)

# Resources
st.subheader("Resources")
read_markdown("utils/resources.md")