Task-273 Implemented a basic chat-style UI for querying a model

Pipfile

@@ -7,6 +7,7 @@ name = "pypi"
 streamlit = "*"
 pandas = "*"
 numpy = "*"
+st-annotated-text = "*"
 
 [dev-packages]
 

Pipfile.lock

@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "c16e5f3fc76a1450c8e733093365a95a5f7d169ef54be7e83124c7fa917855da"
+            "sha256": "b114c11edb911ee8c918ef91522f2a4e6895019b452b37a052da3b4deb4c456e"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -186,6 +186,13 @@
             "markers": "python_version >= '3.7'",
             "version": "==3.1.44"
         },
+        "htbuilder": {
+            "hashes": [
+                "sha256:58c0bc5502c1a46b42ae9e074c43ec0f6fdc24ed334936cb17e1ed5a8938aee2"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==0.9.0"
+        },
         "idna": {
             "hashes": [
                 "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9",
@@ -750,6 +757,15 @@
             "markers": "python_version >= '3.7'",
             "version": "==5.0.2"
         },
+        "st-annotated-text": {
+            "hashes": [
+                "sha256:712c45821f020eccafad3a58c10b9d2d51d449f4db999a06308ecf39a753a4df",
+                "sha256:b0134dcf734697cc3dbdb11862c4174071e7fb6f365af55a37c710d357fd88a5"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.5'",
+            "version": "==4.0.2"
+        },
         "streamlit": {
             "hashes": [
                 "sha256:62026dbdcb482790933f658b096d7dd58fa70da89c1f06fbc3658b91dcd4dab2",

app.py

@@ -1,4 +1,93 @@
 import streamlit as st
+from annotated_text import annotated_text, annotation
+import time
+from random import randint, uniform
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+results_container = None
+
+def output_results(res):
+    label_dict = {
+        'Gender': '#4A90E2',
+        'Race': '#E67E22',
+        'Sexuality': '#3B9C5A',  
+        'Disability': '#8B5E3C',
+        'Religion': '#A347BA',  
+        'Unspecified': '#A0A0A0'
+
+    }
+    with rc:
+        with st.chat_message(name = 'human', avatar=None):
+            at_list = []
+            if res['numerical_sentiment'] == 1:
+                # st.markdown('##### Category Results:')
+                for entry in res['category_sentiments'].keys():
+                    if randint(0, 1) == 1:
+                        val = res['category_sentiments'][entry]
+                        perc = val * 100
+                        at_list.append((entry, f'{perc:.2f}%', label_dict[entry]))
+                        at_list.append(' ')
+
+            st.markdown(f"#### Text - *\"{res['raw_text']}\"*")
+            st.markdown(f"#### Classification - {':red' if res['numerical_sentiment'] == 1 else ':green'}[{res['text_sentiment']}]")
+
+            if len(at_list) > 0:
+                # st.html(annotated_text(at_list))
+                annotated_text(at_list)
+
+def test_results(text):
+    test_val = int(randint(0, 1))
+    res_obj = {
+            'raw_text': text,
+            'text_sentiment': 'Discriminatory' if test_val == 1 else 'Non-Discriminatory',
+            'numerical_sentiment': test_val,
+            'category_sentiments': {
+                'Gender': None if test_val == 0 else uniform(0.0, 1.0),
+                'Race': None if test_val == 0 else uniform(0.0, 1.0),
+                'Sexuality': None if test_val == 0 else uniform(0.0, 1.0),  
+                'Disability': None if test_val == 0 else uniform(0.0, 1.0),
+                'Religion': None if test_val == 0 else uniform(0.0, 1.0),  
+                'Unspecified': None if test_val == 0 else uniform(0.0, 1.0)
+            }
+        }
+    return res_obj
+
+
+def analyze_text(text):
+    res = None
+    with rc:
+        with st.spinner("Processing...", show_time=True) as spnr:
+            time.sleep(5)
+            res = test_results(text)
+            del spnr
+
+    if res is not None:
+        st.session_state.results.append(res)
+        output_results(res)
+
+pri_container = st.container()
+cols = pri_container.columns([1, 8, 1])
+cols[1].subheader('NLPinitiative - Discriminatory Text Classifier')
+
+pri_container.markdown(
+    """The NLPinitiative Discriminatory Text Classifier is an advanced 
+    natural language processing tool designed to detect and flag potentially 
+    discriminatory or harmful language. By analyzing text for biased, offensive, 
+    or exclusionary content, this classifier helps promote more inclusive and 
+    respectful communication. Simply enter your text below, and the model will 
+    assess it based on linguistic patterns and context. While the tool provides 
+    valuable insights, we encourage users to review flagged content thoughtfully 
+    and consider context when interpreting results."""
+)
+
+st.divider()
+rc = st.container()
+
+if "results" not in st.session_state:
+    st.session_state.results = []
+
+with rc:
+    for result in st.session_state.results:
+        output_results(result)
+
+if entry := st.chat_input('Enter text to classify'):
+    analyze_text(entry)