make blog

app.py

@@ -10,7 +10,7 @@ GITHUB_CODE = "https://huggingface.co/datasets/lvwerra/github-code"
 INCODER_IMG = (
     "https://huggingface.co/datasets/loubnabnl/repo-images/raw/main/incoder.png"
 )
-
+MODELS = ["CodeParrot", "InCoder"]
 
 @st.cache()
 def load_examples():
@@ -32,100 +32,92 @@ def generate_code(model_name, gen_prompt, max_new_tokens, temperature, seed):
 
 st.set_page_config(page_icon=":laptop:", layout="wide")
 
-st.sidebar.header("Models")
-models = ["CodeParrot", "InCoder"]
+# Introduction
+st.title("Code generation with 🤗")
+with open("utils/intro.txt", "r") as f:
+    intro = f.read()
+st.markdown(intro)
+
+# Pretraining datasets
+st.title("1 - Pretraining datasets 📚")
+st.markdown(
+    f"Preview of some code files from Github repositories in [Github-code dataset]({GITHUB_CODE}):"
+)
+df = pd.read_csv("utils/data_preview.csv")
+st.dataframe(df)
+st.header("Model")
+selected_model = st.selectbox(
+    "Select a code generation model", MODELS, default=["CodeParrot"]
+)
+with open(f"datasets/{selected_model.lower()}.txt", "r") as f:
+    text = f.read()
+st.markdown(text)
+
+# Model architecture
+st.title("Model architecture")
+st.markdow("Most code generation models use GPT style architectures trained on code. Some use encoder-decoder architectures such as AlphaCode.")
+st.header("Model")
+selected_model = st.selectbox(
+    "Select a code generation model", MODELS, default=["CodeParrot"]
+)
+with open(f"architectures/{selected_model.lower()}.txt", "r") as f:
+    text = f.read()
+st.markdown(text)
+if model == "InCoder":
+    st.image(INCODER_IMG, caption="Figure 1: InCoder training", width=700)
+
+# Model evaluation
+st.title("Code models evaluation 📊")
+with open("evaluation/intro.txt", "r") as f:
+    intro = f.read()
+st.markdown(intro)
+
+# Code generation
+st.title("Code generation 💻")
+st.header("Models")
 selected_models = st.sidebar.multiselect(
-    "Select code generation models to compare", models, default=["CodeParrot"]
+    "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")
-    with open("utils/intro.txt", "r") as f:
-        intro = f.read()
-    st.markdown(intro)
-
-elif selected_task == "Pretraining datasets":
-    st.title("Pretraining datasets 📚")
-    st.markdown(
-        f"Preview of some code files from Github repositories in [Github-code dataset]({GITHUB_CODE}):"
-    )
-    df = pd.read_csv("utils/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")
-    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)
-        if model == "InCoder":
-            st.image(INCODER_IMG, caption="Figure 1: InCoder training", width=700)
-
-elif selected_task == "Model evaluation":
-    st.title("Code models evaluation 📊")
-    with open("evaluation/intro.txt", "r") as f:
-        intro = f.read()
-    st.markdown(intro)
-
-elif selected_task == "Code generation":
-    st.title("Code generation 💻")
-    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 or implement yours", 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..."):
-            # 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])
+st.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.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"]
+st.header("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=8,
+    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=220,
+).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])