Update app.py

app.py

@@ -20,7 +20,121 @@ st.write(pydicom)
 
 #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),
+    )
+
+print ("before main")
+
+
+st.title("Transformers Interpet Demo App")
+
+print ("before main")
+
+#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)
+
+
+print ("Model loaded")
+if model_name.startswith("textattack/"):
+    model.config.id2label = {0: "NEGATIVE (0) ", 1: "POSITIVE (1)"}
+model.eval()
+print ("Model Evaluated")
+cls_explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer)
+print ("Model Explained")
+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 a 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,
+)
+print ("Before button")
+if st.button('Say hello'):
+    st.write('Why hello there')
+else:
+    st.write('Goodbye')
+print ("After test button")
+
+if st.button("Interpret Text"):
+    #print_memory_usage()
+    st.text("Output")
+    with st.spinner("Interpreting your text (This may take some time)"):
+        print ("Interpreting text")
+        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:
+        print ("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
+        )
+    print ("end of stuff")