model demo test

app.py

@@ -1,4 +1,127 @@
+import asyncio
+import gc
+import logging
+import os
+
+import pandas as pd
+import psutil
 import streamlit as st
+from PIL import Image
+from streamlit import components
+# from streamlit.caching import clear_cache
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from transformers_interpret import SequenceClassificationExplainer
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+logging.basicConfig(
+    format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO
+)
+
+
+def print_memory_usage():
+    logging.info(f"RAM memory % used: {psutil.virtual_memory()[2]}")
+
+
+@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=1)
+def load_model(model_name):
+    return (
+        AutoModelForSequenceClassification.from_pretrained(model_name),
+        AutoTokenizer.from_pretrained(model_name),
+    )
+
+
+def main():
+
+    st.title("Transformers Interpet Demo App")
+
+    image = Image.open("./images/tight@1920x_transparent.png")
+    st.sidebar.image(image, use_column_width=True)
+    st.sidebar.markdown(
+        "Check out the package on [Github](https://github.com/cdpierse/transformers-interpret)"
+    )
+    st.info(
+        "Due to limited resources only low memory models are available. Run this [app locally](https://github.com/cdpierse/transformers-interpret-streamlit) to run the full selection of available models. "
+    )
+
+    # uncomment the options below to test out the app with a variety of classification models.
+    models = {
+        # "textattack/distilbert-base-uncased-rotten-tomatoes": "",
+        # "textattack/bert-base-uncased-rotten-tomatoes": "",
+        # "textattack/roberta-base-rotten-tomatoes": "",
+        # "mrm8488/bert-mini-finetuned-age_news-classification": "BERT-Mini finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.",
+        # "nateraw/bert-base-uncased-ag-news": "BERT finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.",
+        "distilbert-base-uncased-finetuned-sst-2-english": "DistilBERT model finetuned on SST-2 sentiment analysis task. Predicts positive/negative sentiment.",
+        # "ProsusAI/finbert": "BERT model finetuned to predict sentiment of financial text. Finetuned on Financial PhraseBank data. Predicts positive/negative/neutral.",
+        "sampathkethineedi/industry-classification": "DistilBERT Model to classify a business description into one of 62 industry tags.",
+        "MoritzLaurer/policy-distilbert-7d": "DistilBERT model finetuned to classify text into one of seven political categories.",
+        # # "MoritzLaurer/covid-policy-roberta-21": "(Under active development ) RoBERTA model finetuned to identify COVID policy measure classes ",
+        # "mrm8488/bert-tiny-finetuned-sms-spam-detection": "Tiny bert model finetuned for spam detection. 0 == not spam, 1 == spam",
+    }
+    model_name = st.sidebar.selectbox(
+        "Choose a classification model", list(models.keys())
+    )
+    model, tokenizer = load_model(model_name)
+    if model_name.startswith("textattack/"):
+        model.config.id2label = {0: "NEGATIVE (0) ", 1: "POSITIVE (1)"}
+    model.eval()
+    cls_explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer)
+    if cls_explainer.accepts_position_ids:
+        emb_type_name = st.sidebar.selectbox(
+            "Choose embedding type for attribution.", ["word", "position"]
+        )
+        if emb_type_name == "word":
+            emb_type_num = 0
+        if emb_type_name == "position":
+            emb_type_num = 1
+    else:
+        emb_type_num = 0
+
+    explanation_classes = ["predicted"] + list(model.config.label2id.keys())
+    explanation_class_choice = st.sidebar.selectbox(
+        "Explanation class: The class you would like to explain output with respect to.",
+        explanation_classes,
+    )
+    my_expander = st.beta_expander(
+        "Click here for description of models and their tasks"
+    )
+    with my_expander:
+        st.json(models)
+
+    # st.info("Max char limit of 350 (memory management)")
+    text = st.text_area(
+        "Enter text to be interpreted",
+        "I like you, I love you",
+        height=400,
+        max_chars=850,
+    )
+
+    if st.button("Interpret Text"):
+        print_memory_usage()
+
+        st.text("Output")
+        with st.spinner("Interpreting your text (This may take some time)"):
+            if explanation_class_choice != "predicted":
+                word_attributions = cls_explainer(
+                    text,
+                    class_name=explanation_class_choice,
+                    embedding_type=emb_type_num,
+                    internal_batch_size=2,
+                )
+            else:
+                word_attributions = cls_explainer(
+                    text, embedding_type=emb_type_num, internal_batch_size=2
+                )
+
+        if word_attributions:
+            word_attributions_expander = st.beta_expander(
+                "Click here for raw word attributions"
+            )
+            with word_attributions_expander:
+                st.json(word_attributions)
+            components.v1.html(
+                cls_explainer.visualize()._repr_html_(), scrolling=True, height=350
+            )
+
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+if __name__ == "__main__":
+    main()

requirement.txt

@@ -0,0 +1,5 @@
+streamlit==0.82.0
+transformers-interpret==0.5.1
+pandas==1.0.3
+transformers==4.3.2
+psutil==5.7.0