Delete app_threading.py

app_threading.py

@@ -1,205 +0,0 @@
-import json
-import os
-import pandas as pd
-import requests
-from multiprocessing import Pool
-from functools import partial
-import streamlit as st
-from datasets import load_dataset, load_metric
-
-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 load_evaluation():
-    # load task 2 of HumanEval and code_eval_metric
-    os.environ["HF_ALLOW_CODE_EVAL"] = "1"
-    human_eval = load_dataset("openai_humaneval")
-    entry_point = f"check({human_eval['test'][2]['entry_point']})"
-    test_func = "\n" + human_eval["test"][2]["test"] + "\n" + entry_point
-    code_eval = load_metric("code_eval")
-    return code_eval, test_func
-
-
-def read_markdown(path):
-    with open(path, "r") as f:
-        output = f.read()
-    st.markdown(output, unsafe_allow_html=True)
-
-def generate_code(model_name, gen_prompt, max_new_tokens, temperature, seed):
-    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]
-    return 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.cache(show_spinner=False)
-def generate_teaser(gen_prompt):
-    generations = []
-    generate_code(generations, "CodeGen", gen_prompt, 10, 0.2, 42)
-    return generations[0]
-    
-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/summary.md")
-## teaser
-example_text = "def print_hello_world():"
-col1, col2, col3 = st.columns([1, 2, 1])
-with col2:
-    gen_prompt = st.text_area(
-        "",
-        value=example_text,
-        height=100,
-    ).strip()
-    if st.button("Generate code!", key=1):
-        with st.spinner("Generating code..."):
-            st.code(generate_teaser(gen_prompt)) 
-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 model evaluation")
-read_markdown("evaluation/intro.md")
-read_markdown("evaluation/demo_humaneval.md")
-## quiz
-st.markdown("Below you can try solving this problem or visualize the solution of CodeParrot:")
-with open("evaluation/problem.md", "r") as f:
-    problem = f.read()
-with open("evaluation/solution.md", "r") as f:
-    solution = f.read()
-    
-candidate_solution = st.text_area(
-    "Complete the problem:",
-    value=problem,
-    height=240,
-).strip()
-if st.button("Test my solution", key=2):
-    with st.spinner("Testing..."):
-        code_eval, test_func = load_evaluation()
-        test_cases = [test_func]
-        candidates = [[candidate_solution]]
-        pass_at_k, _ = code_eval.compute(references=test_cases, predictions=candidates)
-        text = "Your solution didn't pass the test, pass@1 is 0 😕" if pass_at_k['pass@1'] < 1  else "Congrats your pass@1 is 1! 🎉"
-        st.markdown(text)
-if st.button("Show model solution", key=3):
-    st.markdown(solution)
-    
-# 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..."):
-        # Create a multiprocessing Pool
-        pool = Pool()
-        generate_parallel = partial(
-            generate_code,
-
-            gen_prompt=gen_prompt,
-            max_new_tokens=max_new_tokens,
-            temperature=temperature,
-            seed=seed,
-        )
-        output = pool.map(generate_parallel, selected_models)
-        for i in range(len(output)):
-            st.markdown(f"**{selected_models[i]}**")
-            st.code(output[i])
-        if len(output) < 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")