Done changes in ui

app.py

@@ -1,15 +1,77 @@
 import streamlit as st
-from preprocessing import predict_emotion
-from modelutils import  load_model
 import os
+import time
+from preprocessing import predict_emotion
+from modelutils import load_model
+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Set page configuration
+st.set_page_config(page_title="Audio Emotion Detector", layout="wide")
+
+# Load model
+@st.cache_resource
+def get_model():
+    return load_model()
 
+model = get_model()
+
+# Title and description
+st.title("🎭 Audio Emotion Detector")
+st.write("Upload an audio file to detect the emotion in the speech.")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an audio file", type=["wav", "mp3"])
 
-model = load_model()
-uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3"])
 if uploaded_file is not None:
-    st.write("Processing audio...")
-    with open("temp_audio.wav", "wb") as f:
-        f.write(uploaded_file.getbuffer())
-    predicted_emotion = predict_emotion(os.path.join("temp_audio.wav"), model)
-    st.write(f"Predicted Emotion: {predicted_emotion}")
+    # Create two columns
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.audio(uploaded_file, format='audio/wav')
+
+    with col2:
+        # Display a progress bar
+        progress_bar = st.progress(0)
+        status_text = st.empty()
+
+        for i in range(100):
+            # Update progress bar
+            progress_bar.progress(i + 1)
+            status_text.text(f"Processing: {i+1}%")
+            time.sleep(0.01)
+
+        # Save uploaded file temporarily
+        with open("temp_audio.wav", "wb") as f:
+            f.write(uploaded_file.getbuffer())
+
+        # Predict emotion
+        predicted_emotion = predict_emotion(os.path.join("temp_audio.wav"), model)
+
+        # Display result
+        st.success(f"Predicted Emotion: {predicted_emotion}")
+
+
+
+    # Display waveform and spectrogram
+    st.write("### Audio Visualization")
+    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))
+
+    y, sr = librosa.load(os.path.join("temp_audio.wav"))
+
+    # Waveform
+    librosa.display.waveshow(y, sr=sr, ax=ax1)
+    ax1.set_title('Waveform')
+
+    # Spectrogram
+    D = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max)
+    librosa.display.specshow(D, sr=sr, x_axis='time', y_axis='hz', ax=ax2)
+    ax2.set_title('Spectrogram')
+
+    plt.tight_layout()
+    st.pyplot(fig)
+    # Remove temporary file
     os.remove("temp_audio.wav")
+