Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,102 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
+ import streamlit as st
+from textblob import TextBlob
 
-"""
-# Welcome to Streamlit!
+def main():
+    # Page configuration
+    st.set_page_config(
+        page_title="Basic Sentiment Analysis",
+        page_icon="🔄",
+        layout="centered"
+    )
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+    # Custom CSS for styling
+    st.markdown("""
+    <style>
+        .main {
+            background-color: #f8f9fa;
+        }
+        .stTextInput > div > div > input {
+            border-radius: 15px;
+            padding: 10px;
+        }
+        .reportview-container .main .block-container {
+            padding: 2rem;
+        }
+        .result-box {
+            padding: 20px;
+            border-radius: 10px;
+            margin-top: 20px;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+        }
+        .positive {
+            background-color: #d4edda;
+            color: #155724;
+        }
+        .neutral {
+            background-color: #e2e3e5;
+            color: #383d41;
+        }
+        .negative {
+            background-color: #f8d7da;
+            color: #721c24;
+        }
+    </style>
+    """, unsafe_allow_html=True)
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+    # Header
+    st.title("Basic Sentiment Analysis")
+    st.markdown("Enter text below to analyze its sentiment (without using pre-trained models).")
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+    # Text input
+    user_input = st.text_area("Enter your text:", height=100, 
+                             placeholder="Type something like 'I love this!' or 'This is terrible.'")
+    
+    if st.button("Analyze Sentiment"):
+        if user_input:
+            # Analyze sentiment using TextBlob
+            analysis = TextBlob(user_input)
+            polarity = analysis.sentiment.polarity
+            
+            # Determine sentiment category
+            if polarity > 0.2:
+                sentiment = "Positive 😊"
+                emotion_class = "positive"
+            elif polarity < -0.2:
+                sentiment = "Negative 😞"
+                emotion_class = "negative"
+            else:
+                sentiment = "Neutral 😐"
+                emotion_class = "neutral"
+            
+            # Display results
+            st.markdown(f"<div class='result-box {emotion_class}'>", unsafe_allow_html=True)
+            
+            st.subheader("Sentiment Analysis Results:")
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                st.metric("Sentiment", sentiment)
+                
+            with col2:
+                st.metric("Polarity Score", round(polarity, 3))
+            
+            st.progress((polarity + 1) / 2)
+            
+            st.markdown("""
+                **Polarity Scale:**  
+                -1.0 (Very Negative) —— 0.0 (Neutral) —— +1.0 (Very Positive)
+            """)
+            
+            st.markdown("</div>", unsafe_allow_html=True)
+            
+            # Additional analysis
+            with st.expander("Detailed Analysis:"):
+                st.write(f"- **Subjectivity:** {'Subjective' if analysis.sentiment.subjectivity > 0.5 else 'Objective'} "
+                        f"(Score: {round(analysis.sentiment.subjectivity, 3)})")
+                st.write("- **Word Count:**", len(analysis.words))
+                
+        else:
+            st.warning("Please enter some text to analyze.")
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()