reformat code

app.py

@@ -7,7 +7,10 @@ import streamlit as st
 
 
 GITHUB_CODE = "https://huggingface.co/datasets/lvwerra/github-code"
-INCODER_IMG = "https://huggingface.co/datasets/loubnabnl/repo-images/raw/main/incoder.png"
+INCODER_IMG = (
+    "https://huggingface.co/datasets/loubnabnl/repo-images/raw/main/incoder.png"
+)
+
 
 @st.cache()
 def load_examples():
@@ -15,20 +18,34 @@ def load_examples():
         examples = json.load(f)
     return examples
 
+
 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]
+    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
-    
+
+
 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"])
+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"]
+tasks = [
+    " ",
+    "Pretraining datasets",
+    "Model architecture",
+    "Model evaluation",
+    "Code generation",
+]
 selected_task = st.sidebar.selectbox("Select a task", tasks)
 
 
@@ -37,25 +54,27 @@ if selected_task == " ":
     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}):")   
+    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)  
-        
+        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) 
+        st.markdown(text)
         if model == "InCoder":
             st.image(INCODER_IMG, caption="Figure 1: InCoder training", width=700)
 
@@ -64,31 +83,49 @@ elif selected_task == "Model 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)
+    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()
+    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)
+            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.code(output[i])