Fix style

app.py

@@ -1,26 +1,26 @@
-from io import BytesIO
-import streamlit as st
-import pandas as pd
 import json
 import os
+from io import BytesIO
+
+import matplotlib.pyplot as plt
 import numpy as np
-from streamlit.elements import markdown
+import pandas as pd
+import streamlit as st
+from mtranslate import translate
 from PIL import Image
+from streamlit.elements import markdown
+
 from model.flax_clip_vision_bert.modeling_clip_vision_bert import (
     FlaxCLIPVisionBertForSequenceClassification,
 )
+from session import _get_state
 from utils import (
-    get_transformed_image,
     get_text_attributes,
     get_top_5_predictions,
+    get_transformed_image,
     plotly_express_horizontal_bar_plot,
     translate_labels,
 )
-import matplotlib.pyplot as plt
-from mtranslate import translate
-
-
-from session import _get_state
 
 state = _get_state()
 
@@ -74,9 +74,9 @@ st.write(
     "[Gunjan Chhablani](https://huggingface.co/gchhablani), [Bhavitvya Malik](https://huggingface.co/bhavitvyamalik)"
 )
 
-image_col, intro_col = st.beta_columns([3,8])
-image_col.image("./misc/mvqa-logo-white.png", use_column_width='always')
-intro_col.write(read_markdown('intro.md'))
+image_col, intro_col = st.beta_columns([3, 8])
+image_col.image("./misc/mvqa-logo-white.png", use_column_width="always")
+intro_col.write(read_markdown("intro.md"))
 with st.beta_expander("Usage"):
     st.write(read_markdown("usage.md"))
 
@@ -85,7 +85,8 @@ with st.beta_expander("Article"):
     st.write(read_markdown("caveats.md"))
     st.write("## Methodology")
     st.image(
-        "./misc/Multilingual-VQA.png", caption="Masked LM model for Image-text Pretraining."
+        "./misc/Multilingual-VQA.png",
+        caption="Masked LM model for Image-text Pretraining.",
     )
     st.markdown(read_markdown("pretraining.md"))
     st.markdown(read_markdown("finetuning.md"))
@@ -110,7 +111,10 @@ if state.image_file is None:
 
 col1, col2 = st.beta_columns([6, 4])
 
-if col2.button("Get a random example", help="Get a random example from the 100 `seeded` image-text pairs."):
+if col2.button(
+    "Get a random example",
+    help="Get a random example from the 100 `seeded` image-text pairs.",
+):
     sample = dummy_data.sample(1).reset_index()
     state.image_file = sample.loc[0, "image_file"]
     state.question = sample.loc[0, "question"].strip("- ")
@@ -124,9 +128,15 @@ if col2.button("Get a random example", help="Get a random example from the 100 `
 
 col2.write("OR")
 
-uploaded_file = col2.file_uploader("Upload your image", type=["png", "jpg", "jpeg"], help="Upload a file of your choosing.")
+uploaded_file = col2.file_uploader(
+    "Upload your image",
+    type=["png", "jpg", "jpeg"],
+    help="Upload a file of your choosing.",
+)
 if uploaded_file is not None:
-    st.error("Uploading files does not work on HuggingFace spaces. This app only supports random examples for now.")
+    st.error(
+        "Uploading files does not work on HuggingFace spaces. This app only supports random examples for now."
+    )
     # state.image_file = os.path.join("images/val2014", uploaded_file.name)
     # state.image = np.array(Image.open(uploaded_file))
 
@@ -135,9 +145,13 @@ transformed_image = get_transformed_image(state.image)
 # Display Image
 col1.image(state.image, use_column_width="auto")
 
-new_col1, new_col2 = st.beta_columns([5,5])
+new_col1, new_col2 = st.beta_columns([5, 5])
 # Display Question
-question = new_col1.text_input(label="Question", value=state.question, help="Type your question regarding the image above in one of the four languages.")
+question = new_col1.text_input(
+    label="Question",
+    value=state.question,
+    help="Type your question regarding the image above in one of the four languages.",
+)
 new_col1.markdown(
     f"""**English Translation**: {question if state.question_lang_id == "en" else translate(question, 'en')}"""
 )
@@ -151,11 +165,17 @@ state.answer_lang_id = new_col2.selectbox(
     index=options.index(state.answer_lang_id),
     options=options,
     format_func=lambda x: code_to_name[x],
-    help="The language to be used to show the top-5 labels."
+    help="The language to be used to show the top-5 labels.",
 )
 
 actual_answer = answer_reverse_mapping[str(state.answer_label)]
-new_col2.markdown("**Actual Answer**: " + translate_labels([actual_answer], state.answer_lang_id)[0]+" ("+actual_answer+")")
+new_col2.markdown(
+    "**Actual Answer**: "
+    + translate_labels([actual_answer], state.answer_lang_id)[0]
+    + " ("
+    + actual_answer
+    + ")"
+)
 
 # Display Top-5 Predictions
 with st.spinner("Loading model..."):

translate_answer_mapping.py

@@ -1,9 +1,10 @@
-from mtranslate.core import translate
 import json
-from tqdm import tqdm
-import ray
 from asyncio import Event
+
+import ray
+from mtranslate.core import translate
 from ray.actor import ActorHandle
+from tqdm import tqdm
 
 ray.init()
 from typing import Tuple

utils.py

@@ -1,12 +1,13 @@
-from torchvision.io import read_image, ImageReadMode
-import torch
+import json
+
 import numpy as np
+import plotly.express as px
+import torch
+from PIL import Image
+from torchvision.io import ImageReadMode, read_image
 from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
 from torchvision.transforms.functional import InterpolationMode
 from transformers import BertTokenizerFast
-import plotly.express as px
-import json
-from PIL import Image
 
 
 class Transform(torch.nn.Module):