sentiment-analysis/app.py at main · Saikishorep15/sentiment-analysis · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
import streamlit as st
import pickle

# ------------------------------
# Load model
# ------------------------------
model = pickle.load(open("model.pkl", "rb"))
vectorizer = pickle.load(open("vectorizer.pkl", "rb"))

# ------------------------------
# Page Config
# ------------------------------
st.set_page_config(page_title="Sentiment Analyzer", page_icon="💬", layout="centered")

# ------------------------------
# Custom CSS (Premium UI)
# ------------------------------
st.markdown("""
<style>
.main {
    background-color: #0e1117;
    color: white;
}
.title {
    text-align: center;
    font-size: 42px;
    font-weight: bold;
    color: #00c3ff;
}
.subtitle {
    text-align: center;
    font-size: 18px;
    color: #bbbbbb;
}
.box {
    padding: 20px;
    border-radius: 15px;
    background-color: #1c1f26;
    margin-top: 20px;
}
.footer {
    text-align: center;
    color: gray;
    font-size: 14px;
}
</style>
""", unsafe_allow_html=True)

# ------------------------------
# Title
# ------------------------------
st.markdown('<div class="title">💬 Sentiment Analysis App</div>', unsafe_allow_html=True)
st.markdown('<div class="subtitle">Analyze text as Positive or Negative using NLP</div>', unsafe_allow_html=True)

st.markdown("---")

# ------------------------------
# Input Box
# ------------------------------
st.markdown('<div class="box">', unsafe_allow_html=True)
text = st.text_area("✍️ Enter your text here", height=150)
st.markdown('</div>', unsafe_allow_html=True)

# ------------------------------
# Analyze Button
# ------------------------------
col1, col2, col3 = st.columns([1,2,1])
with col2:
    analyze = st.button("🔍 Analyze Sentiment", use_container_width=True)

# ------------------------------
# Prediction
# ------------------------------
if analyze:
    if text.strip() == "":
        st.warning("⚠️ Please enter some text")
    else:
        vec = vectorizer.transform([text])
        prediction = model.predict(vec)[0]
        confidence = model.predict_proba(vec).max()

        st.markdown("---")

        # Result Box
        if prediction == 1:
            st.markdown(f"""
            <div style="background-color:#1e4620;padding:20px;border-radius:10px">
                <h2 style="color:#00ff88;">😊 Positive Sentiment</h2>
                <p>Confidence: {confidence:.2f}</p>
            </div>
            """, unsafe_allow_html=True)
        else:
            st.markdown(f"""
            <div style="background-color:#5c1e1e;padding:20px;border-radius:10px">
                <h2 style="color:#ff4b4b;">😡 Negative Sentiment</h2>
                <p>Confidence: {confidence:.2f}</p>
            </div>
            """, unsafe_allow_html=True)

        # Confidence Bar
        st.subheader("📊 Confidence Score")
        st.progress(float(confidence))

        # Emoji meter
        st.subheader("🔥 Sentiment Meter")
        if prediction == 1:
            st.write("😊 😊 😊 😊 😊")
        else:
            st.write("😡 😡 😡 😡 😡")

# ------------------------------
# Footer
# ------------------------------
st.markdown("---")
st.markdown('<div class="footer">🚀 Built with Streamlit | CodeC Internship Task 2</div>', unsafe_allow_html=True)