Add application file

app.py

@@ -0,0 +1,76 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import numpy as np
+import torch
+
+def main():
+
+    st.set_page_config(  # Alternate names: setup_page, page, layout
+        layout="centered",  # Can be "centered" or "wide". In the future also "dashboard", etc.
+        initial_sidebar_state="auto",  # Can be "auto", "expanded", "collapsed"
+        page_title="Emoji-motion!",  # String or None. Strings get appended with "• Streamlit".
+        page_icon=None,  # String, anything supported by st.image, or None.
+    )
+
+    st.title('Emoji-motion!')
+
+    example_prompts = [
+        "Today is going to be awesome!",
+        "Pity those who don't feel anything at all.",
+        "I envy people that know love.",
+        "Nature is so beautiful"]
+
+    example = st.selectbox("Choose a pre-defined example", example_prompts)
+
+    # Take the message which needs to be processed
+    message = st.text_area('Or type a sentence to see if our AL Algorithm can detect your emotion', example)
+    # st.title(message)
+    st.text('')
+    models_to_choose = ["AlekseyDorkin/xlm-roberta-en-ru-emoji"]
+
+    BASE_MODEL = st.selectbox("Choose a model", models_to_choose)
+    TOP_N = 5
+
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+    model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL)
+
+    def preprocess(text):
+        new_text = []
+        for t in text.split(" "):
+            t = '@user' if t.startswith('@') and len(t) > 1 else t
+            t = 'http' if t.startswith('http') else t
+            new_text.append(t)
+        return " ".join(new_text)
+
+    def get_top_emojis(text, top_n=TOP_N):
+        preprocessed = preprocess(text)
+        inputs = tokenizer(preprocessed, return_tensors="pt")
+        preds = model(**inputs).logits
+        scores = torch.nn.functional.softmax(preds, dim=-1).detach().numpy()
+        ranking = np.argsort(scores)
+        print(ranking)
+        ranking = ranking.squeeze()[::-1][:top_n]
+        print(scores)
+        print(ranking)
+        print(model.config.id2label)
+        emojis = [model.config.id2label[i] for i in ranking]
+        return ', '.join(map(str, emojis))
+
+    # Define function to run when submit is clicked
+    def submit(message):
+        if len(message)>0:
+            st.header(get_top_emojis(message))
+        else:
+            st.error("The text can't be empty")
+
+    # Run algo when submit button is clicked
+    if(st.button('Submit')):
+        submit(message)
+
+    st.text('')
+    st.markdown('<span style="color:blue; font-size:10px">App created by [@AlekseyDorkin](https://huggingface.co/AlekseyDorkin) \
+        and [@akshay7](https://huggingface.co/akshay7)</span>',unsafe_allow_html=True)
+
+
+if __name__ == "__main__":
+    main()