Added the application file

app.py

@@ -0,0 +1,121 @@
+import streamlit as st
+import joblib
+import numpy as np
+import xgboost
+# from sklearn.ensemble import GradientBoostingClassifier
+# from tensorflow.keras.models import load_model
+import tensorflow
+from keras.losses import binary_crossentropy
+from keras.optimizers import Adam
+from tensorflow import keras
+from keras.models import load_model
+# Local Imports
+import feature_extraction
+import audio_splitting
+
+# Create a Streamlit web app
+st.title("Music Genre Classifier")
+
+# Upload music file
+uploaded_file = st.file_uploader("Upload a music file", type=["mp3", "wav"])
+
+if uploaded_file is not None:
+    # User selects a model
+    all_models = ["K-Nearest Neighbors - (Single Label)", "Logistic Regression - (Single Label)", "Support Vector Machines - (Single Label)",
+                  "Neural Network - (Single Label)",
+                  "XGB Classifier - (Single Label)", "Convolutional Recurrent Neural Network - (Multi Label)", "XGB - (Multi Label)",
+                  "Neural Network - (Multi Label)","Batch Normalization - (Multi Label)"]
+    model_name = st.selectbox("Select a model", all_models)
+    st.write(f"Predicition of following genres")
+    multi_class_names = ["Metal", "Jazz", "Blues", "R&B", "Classical", "Reggae", "Rap & Hip-Hop", "Punk", "Rock",
+                         "Country", "Bebop", "Pop", "Soul", "Dance & Electronic", "Folk"]
+
+    st.write(multi_class_names)
+
+    # Load the selected model
+    if model_name == "K-Nearest Neighbors - (Single Label)":
+        model = joblib.load("./models/knn.pkl")
+    elif model_name == "Logistic Regression - (Single Label)":
+        model = joblib.load("./models/logistic.pkl")
+    elif model_name == "Support Vector Machines - (Single Label)":
+        model = joblib.load("./models/svm.pkl")
+    elif model_name == "Neural Network - (Single Label)":
+        model = joblib.load("./models/nn.pkl")
+    elif model_name == "XGB Classifier - (Single Label)":
+        model = joblib.load("./models/xgb.pkl")
+    elif model_name == "XGB - (Multi Label)":
+        model = joblib.load("./models/xgb_mlb.pkl")
+    elif model_name == "Convolutional Recurrent Neural Network - (Multi Label)":
+        model = tensorflow.keras.models.load_model("./models/model_crnn1.h5", compile=False)
+        model.compile(loss=binary_crossentropy,
+                      optimizer=Adam(),
+                      metrics=['accuracy'])
+    elif model_name == "Neural Network - (Multi Label)":
+        model = tensorflow.keras.models.load_model("./models/model_nn.h5", compile=False)
+        model.compile(loss=binary_crossentropy,
+                      optimizer=Adam(),
+                      metrics=['accuracy'])
+    elif model_name == "Batch Normalization - (Multi Label)":
+        model = tensorflow.keras.models.load_model("./models/model_bn.h5", compile=False)
+        model.compile(loss=binary_crossentropy,
+                      optimizer=Adam(),
+                      metrics=['accuracy'])
+    class_names = ["blues", "classical", "country", "disco", "hiphop", "jazz", "metal", "pop", "reggae", "rock"]
+
+    xgb_multi_class_names = ["Rock", "Rap & Hip-Hop", "Soul", "Classical", "Dance & Electronic", "Blues","Jazz",
+                             "Country","Bebop","Folk","Reggae","R&B","Punk","Metal","Pop"]
+
+    xmulti_class_names = ["Metal", "Blues", "Reggae", "Jazz", "Rock", "Folk", "Classical", "Dance & Electronic",
+                         "Punk","Bebop", "Pop", "R&B", "Country", "Rap & Hip-Hop", "Soul"]
+    class_indices = {i: class_name for i, class_name in enumerate(class_names)}
+
+    features_list = audio_splitting.split_audio(uploaded_file)
+    features = feature_extraction.scale(features_list)
+
+    st.write(features)
+    # Reshape the features to match the expected shape for prediction
+    reshaped_features = features.reshape(1, -1)
+    if model_name == "XGB - (Multi Label)":
+        # Predict labels for the input features
+        predicted_indices = model.predict(reshaped_features)
+        print(predicted_indices)
+        predicted_labels = []
+        for i in range(0,len(predicted_indices[0])):
+            if predicted_indices[0][i]==1.0:
+                predicted_labels.append(xgb_multi_class_names[i])
+        if predicted_labels:
+            st.write(f"Predicted Genres: {', '.join(predicted_labels)}")
+        else:
+            st.write("No genres predicted for this input.")
+    if model_name == "XGB Classifier - (Single Label)":
+        predicted_indices = model.predict(reshaped_features)
+        predicted_labels = [class_indices[i] for i in predicted_indices]
+        st.write(f"Predicted Genre: {predicted_labels[0]}")
+    elif model_name == "Convolutional Recurrent Neural Network - (Multi Label)"\
+            or model_name == "Neural Network - (Multi Label)"\
+            or model_name == "Batch Normalization - (Multi Label)":
+        predicted_probabilities = model.predict(reshaped_features)
+
+        # Set a threshold for class prediction (e.g., 0.5)
+        threshold = 0.3
+        print(predicted_probabilities)
+        probabilities = []
+        if model_name == "Convolutional Recurrent Neural Network - (Multi Label)":
+            predicted_labels = [class_name for i, class_name in enumerate(multi_class_names) if
+                                predicted_probabilities[0][i] >= threshold]
+            probabilities = [(class_name,predicted_probabilities[0][i]*100) for i, class_name in enumerate(multi_class_names)]
+
+        else:
+            predicted_labels = [class_name for i,class_name in enumerate(xmulti_class_names) if
+                                predicted_probabilities[0][i] >= threshold]
+            probabilities = [(class_name,predicted_probabilities[0][i]*100) for i, class_name in enumerate(xmulti_class_names)]
+
+        if predicted_labels:
+            st.write(f"All probabilities are:")
+            st.write(probabilities)
+            st.write(f"Predicted Genres: {', '.join(predicted_labels)}")
+        else:
+            st.write("No genre predicted above the threshold.")
+    else:
+        predicted_label = model.predict(reshaped_features)[0]
+        st.write(f"Predicted Genre: {predicted_label}")