Updated project

app.py

@@ -1,4 +1,40 @@
 import streamlit as st
+from src.constants import SETTINGS
+from src.utils import load_models, print_results
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+
+def main():
+    st.title(SETTINGS.title)
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.header('Model Selection')
+        model_selected = st.radio('Choose model', SETTINGS.models)
+
+    with col2:
+        st.header('Input text')
+        input_text = col2.text_area(
+            'Write or paste any text', 
+            value=SETTINGS.sample_text,
+            height=150
+        )
+
+    if st.button('Analyze', type='primary') and input_text != '':
+        pipes = load_models()
+        pipe = pipes[model_selected]
+
+        with st.spinner('Analyzing text...'):
+            output = pipe(input_text)
+        
+        st.subheader(f"{SETTINGS.task.replace('-', ' ').capitalize()} results")
+
+        if isinstance(output, dict):
+            output = [output]
+
+
+        for i, result in enumerate(output, 1):
+            print_results(result)
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -0,0 +1,6 @@
+streamlit>=1.40.2
+spacy>=3.8.2
+pydantic_settings>=2.6.1
+transformers>=4.46.3
+numpy<2
+torch>=2.2.2

src/config.py

@@ -0,0 +1,15 @@
+from pydantic_settings import BaseSettings
+from typing import List
+
+
+class Settings(BaseSettings):
+    task: str = 'text-classification'
+    models: List[str] = [
+        'ProsusAI/finbert',
+        'tabularisai/multilingual-sentiment-analysis',
+        'fhamborg/roberta-targeted-sentiment-classification-newsarticles'
+    ]
+    
+    sample_text: str = 'The quality of exchanged money in cache defines random person as a self-personed financial mentor if he absolutely successed at being thoughtful during the process of calculating the bills'
+    
+    title: str = 'Sentiment analyzer'

src/constants.py

@@ -0,0 +1,10 @@
+from src.config import Settings
+
+COLORS = {
+    'POSITIVE': '#4CAF50', # green
+    'NEGATIVE': '#F44336', # red
+    'NEUTRAL': '#FFC107',  # yellow
+    'DEFAULT': '#607D8B'   # grey
+}
+
+SETTINGS = Settings()

src/utils.py

@@ -0,0 +1,46 @@
+import streamlit as st
+from typing import Dict, Any
+from transformers import pipeline
+from src.constants import SETTINGS, COLORS
+
+
+@st.cache_resource
+def load_models() -> Dict[str, pipeline]:
+    return {
+        model_name: pipeline(task=SETTINGS.task, model=model_name) 
+        for model_name in SETTINGS.models
+    }
+
+
+def colorize_sentiment(label, score) -> str:
+    color = COLORS.get(label, COLORS['DEFAULT'])
+
+    return f"""
+    <div style="
+        display: inline-block;
+        background-color: {color};
+        color: white;
+        padding: 0.2em 0.5em;
+        border-radius: 0.3em;
+        margin: 0.1em;
+        font-size: 0.9em;
+        position: relative;
+    ">{label}</div>
+    <div style="
+        font-size: 1em;
+        color: {color};
+        margin-left: 0.5em
+    ">{score:.2f}</div>
+    """
+
+
+def print_results(result: Dict[str, Any]):
+    label = result['label']
+    score = result['score']
+    
+    st.markdown(f"""
+    <div style="display: flex; align-items: center; margin-bottom: 1em;">
+        <span style="font-size: 1.1em; margin-right: 1em;">Overall sentiment:</span>
+        {colorize_sentiment(label, score)}
+    </div>
+    """, unsafe_allow_html=True)