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app.py

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+import os
+
+import gdown as gdown
+import nltk
+import streamlit as st
+from nltk.tokenize import sent_tokenize
+
+from source.pipeline import MultiLabelPipeline, inputs_to_dataset
+
+
+def download_models(ids):
+    """
+    Download all models.
+    :param ids: name and links of models
+    :return:
+    """
+
+    # Download sentence tokenizer
+    nltk.download('punkt')
+
+    # Download model from drive if not stored locally
+    for key in ids:
+        if not os.path.isfile(f"model/{key}.pt"):
+            url = f"https://drive.google.com/uc?id={ids[key]}"
+            gdown.download(url=url, output=f"model/{key}.pt")
+
+
+@st.cache
+def load_labels():
+    """
+    Load model labels.
+    :return:
+    """
+
+    return [
+        "admiration",
+        "amusement",
+        "anger",
+        "annoyance",
+        "approval",
+        "caring",
+        "confusion",
+        "curiosity",
+        "desire",
+        "disappointment",
+        "disapproval",
+        "disgust",
+        "embarrassment",
+        "excitement",
+        "fear",
+        "gratitude",
+        "grief",
+        "joy",
+        "love",
+        "nervousness",
+        "optimism",
+        "pride",
+        "realization",
+        "relief",
+        "remorse",
+        "sadness",
+        "surprise",
+        "neutral"
+    ]
+
+
+@st.cache(allow_output_mutation=True)
+def load_model(model_path):
+    """
+    Load model and cache it.
+    :param model_path: path to model
+    :return:
+    """
+
+    model = MultiLabelPipeline(model_path=model_path)
+
+    return model
+
+
+# Page config
+st.set_page_config(layout="centered")
+st.title("Multiclass Emotion Classification")
+st.write("DeepMind Language Perceiver for Multiclass Emotion Classification (Eng). ")
+
+maintenance = False
+if maintenance:
+    st.write("Unavailable for now (file downloads limit). ")
+else:
+    # Variables
+    ids = {'perceiver-go-emotions': st.secrets['model']}
+    labels = load_labels()
+
+    # Download all models from drive
+    download_models(ids)
+
+    # Display labels
+    st.markdown(f"__Labels:__ {', '.join(labels)}")
+
+    # Model selection
+    left, right = st.columns([4, 2])
+    inputs = left.text_area('', max_chars=4096, value='This is a space about multiclass emotion classification. Write '
+                                                      'something here to see what happens!')
+    model_path = right.selectbox('', options=[k for k in ids], index=0, help='Model to use. ')
+    split = right.checkbox('Split into sentences', value=True)
+    model = load_model(model_path=f"model/{model_path}.pt")
+    right.write(model.device)
+
+    if split:
+        if not inputs.isspace() and inputs != "":
+            with st.spinner('Processing text... This may take a while.'):
+                left.write(model(inputs_to_dataset(sent_tokenize(inputs)), batch_size=1))
+    else:
+        if not inputs.isspace() and inputs != "":
+            with st.spinner('Processing text... This may take a while.'):
+                left.write(model(inputs_to_dataset([inputs]), batch_size=1))