Create app.py

app.py

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+import streamlit as st
+
+# Set dark mode
+st.set_page_config(page_title="Speech Detection System", page_icon="🎙", layout="wide")
+
+# Custom CSS for dark theme and styling
+st.markdown(
+    """
+    <style>
+        body {
+            color: white;
+            background-color: #0e1117;
+        }
+        .stApp {
+            background-color: #0e1117;
+        }
+        .title {
+            text-align: center;
+            font-size: 2.5rem;
+            font-weight: bold;
+            color: #1db954;
+        }
+        .subheading {
+            font-size: 1.5rem;
+            font-weight: bold;
+            color: #f4f4f4;
+            text-align: center;
+        }
+        .description {
+            font-size: 1.1rem;
+            text-align: center;
+            color: #d1d1d1;
+            margin-bottom: 20px;
+        }
+        .feature-card {
+            background-color: #22272e;
+            padding: 15px;
+            border-radius: 10px;
+            box-shadow: 2px 2px 10px rgba(255, 255, 255, 0.1);
+            margin: 10px;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Title
+st.markdown("<div class='title'>🎙 Speech Detection System</div>", unsafe_allow_html=True)
+
+# Description
+st.markdown(
+    """
+    <div class='description'>
+        Speech detection systems utilize various datasets to analyze and interpret spoken language. 
+        These systems perform **acoustic analysis** to identify pitch, tone, and volume, while **speech recognition** converts audio into text. 
+        **Noise filtering** enhances clarity by removing background sounds, and **emotional detection** determines the speaker's mood based on vocal tone. 
+        **Real-time processing** ensures live detection with minimal delay. The use of **multilingual** and **diverse environmental datasets** 
+        improves adaptability and accuracy, making these systems ideal for applications like **virtual assistants, sentiment analysis, and voice-controlled systems**.
+    </div>
+    """,
+    unsafe_allow_html=True
+)
+
+# Features
+st.markdown("<div class='subheading'>🔍 Key Features</div>", unsafe_allow_html=True)
+
+features = [
+    ("🎵 Acoustic Analysis", "Identifies pitch, tone, and volume. Processes sound waveforms to extract unique speech characteristics."),
+    ("😊 Emotional Detection", "Detects emotions such as happiness, anger, or neutrality from vocal tone."),
+    ("🗣 Speech Recognition", "Converts spoken words into text using advanced algorithms. Detects languages and keywords."),
+    ("⚡ Real-Time Processing", "Enables live speech detection with minimal latency for fast, accurate responses."),
+    ("🔇 Noise Filtering", "Removes background noise, ensuring clearer speech recognition and analysis."),
+    ("🌍 Dataset Diversity", "Utilizes multilingual and environmental datasets for robust, adaptable speech detection."),
+]
+
+# Display features in two columns
+cols = st.columns(2)
+for i, (title, desc) in enumerate(features):
+    with cols[i % 2]:  # Distribute features evenly
+        st.markdown(f"<div class='feature-card'><b>{title}</b><br>{desc}</div>", unsafe_allow_html=True)
+
+# Footer
+st.markdown("---")
+st.markdown("<div style='text-align: center; font-size: 0.9rem;'>Built with ❤️ using Streamlit</div>", unsafe_allow_html=True)