Create app_threading.py

app_threading.py

@@ -0,0 +1,208 @@
+import json
+import os
+import pandas as pd
+import requests
+import threading
+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(
+    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.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!", key=4):
+    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")