Create app.py

app.py

@@ -0,0 +1,93 @@
+import re
+
+import seaborn as sns
+import streamlit as st
+
+from utils import load_model, process_text
+
+st.set_page_config(
+    page_title="BERT Keyword Extractor",
+    page_icon="🎈",
+)
+
+
+def _max_width_():
+    max_width_str = "max-width: 1400px;"
+    st.markdown(
+        f"""
+    <style>
+    .reportview-container .main .block-container{{
+        {max_width_str}
+    }}
+    </style>
+    """,
+        unsafe_allow_html=True,
+    )
+
+
+st.header("🔑 Automated Essay Evaluator")
+
+with st.expander("ℹ️ - About this app", expanded=True):
+    st.write(
+        """
+        -   This application demonstrates how automated essay evaluation works: given as an input text with max. \
+        length of 512, it scores it (from 1.0 to 4.0) for different criteria: cohesion, syntax, vocabulary, \
+        phraseology, grammar and conventions.
+        -   This solution is based on fine-tuned deberta-v3-large model.
+        """
+    )
+
+    st.markdown("")
+
+st.markdown("")
+st.markdown("## 📌 **Paste document**", unsafe_allow_html=True)
+with st.form(key="my_form"):
+    _, c2, _ = st.columns([0.07, 5, 0.07])
+
+    with c2:
+        doc = st.text_area(
+            "Paste your text below (max 500 words)",
+            height=510,
+        )
+
+        MAX_WORDS = 500
+
+        res = len(re.findall(r"\w+", doc))
+        doc = doc[:MAX_WORDS]
+
+        submit_button = st.form_submit_button(label="✨ Assess my text!")
+
+if not submit_button:
+    st.stop()
+
+st.markdown("## 🎈  **Check results**")
+
+st.header("")
+
+cs, c1, c2, c3, cLast = st.columns([2, 1.5, 1.5, 1.5, 2])
+
+st.header("")
+
+model = load_model()
+df = process_text(doc, model)
+
+df.index += 1
+
+# Add styling
+cmGreen = sns.light_palette("green", as_cmap=True)
+cmRed = sns.light_palette("red", as_cmap=True)
+df = df.style.background_gradient(
+    cmap=cmGreen,
+    subset=[
+        "Grade",
+    ],
+)
+
+
+format_dictionary = {
+    "Relevancy": "{:.1%}",
+}
+
+df = df.format(format_dictionary)
+
+st.table(df)