feat: implemented emotion based music recommendation system

app.py

+# Importing necessary libraries
+import numpy as np
+import streamlit as st
+import sqlite3
+from textblob import TextBlob
+conn = sqlite3.connect('sentiment.db')
+cursor = conn.cursor()
+import pandas as pd
+import cv2
+from collections import Counter
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Flatten
+from tensorflow.keras.layers import Conv2D
+from tensorflow.keras.layers import MaxPooling2D
+
+# Connect to the SQLite database
+conn = sqlite3.connect('sentiment.db')
+cursor = conn.cursor()
+
+# Create a table for user inputs if it doesn't exist
+cursor.execute('''
+    CREATE TABLE IF NOT EXISTS user_inputs (
+        id INTEGER PRIMARY KEY AUTOINCREMENT,
+        user_input TEXT,
+        sentiment TEXT
+    )
+''')
+conn.commit()
+
+# Configure Streamlit page layout
+st.set_page_config(layout="wide")
+
+# Dictionary mapping emotion index to emotion labels
+emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+
+# Load music data from CSV file 
+df = pd.read_csv('muse_v3.csv')
+df['link'] = df['lastfm_url']
+df['name'] = df['track']
+df['emotional'] = df['number_of_emotion_tags']
+df['pleasant'] = df['valence_tags']
+df = df[['name','emotional','pleasant','link','artist']]
+df = df.sort_values(by=["emotional", "pleasant"])
+df.reset_index()
+
+# Rename columns for better readability
+df_sadp = df[:9000]
+df_sadn = df[9000:18000]
+df_fearp = df[18000:27000]
+df_fearn = df[27000:36000]
+df_angryp = df[36000:45000]
+df_angryn = df[45000:54000]
+df_neutralp = df[54000:63000]
+df_neutraln = df[63000:72000]
+df_happyp = df[72000:81000]
+df_happyn = df[81000:]
+
+# Split the dataframe into different emotion categories
+# (e.g., df_sadp, df_sadn, df_fearp, df_fearn, etc.)
+
+# Function to generate music recommendations based on selected emotions
+def fun(emotions):
+    data = pd.DataFrame()
+    # Logic to select music based on the combination of emotions
+    if len(emotions) == 1:
+        v = emotions[0]
+        t = 30
+
+        if v == 'neutralp':
+            temp = df_neutralp.sample(n=t)
+            data = pd.concat([data, temp[:]])
+        
+        elif v == 'neutraln':
+            temp = df_neutraln.sample(n=t)
+            data = pd.concat([data, temp[:]])
+
+        elif v == 'angryp':
+            temp = df_angryp.sample(n=t)
+            data = pd.concat([data, temp[:]])
+
+        elif v == 'angryn':
+            temp = df_angryn.sample(n=t)
+            data = pd.concat([data, temp[:]])
+
+        elif v == 'fearfulp':
+            temp = df_fearp.sample(n=t)
+            data = pd.concat([data, temp[:]])
+
+        elif v == 'fearfuln':
+            temp = df_fearn.sample(n=t)
+            data = pd.concat([data, temp[:]])
+
+        elif v == 'happyp':
+            temp = df_happyp.sample(n=t)
+            data = pd.concat([data, temp[:]])
+
+        elif v == 'happyn':
+            temp = df_happypn.sample(n=t)
+            data = pd.concat([data, temp[:]])
+
+        elif v == 'sadp':
+            temp = df_sadp.sample(n=t)
+            data = pd.concat([data, temp[:]])
+
+        else:
+            temp = df_sadn.sample(n=t)
+            data = pd.concat([data, temp[:]])
+
+    elif len(emotions) == 2:
+        times = [20,10]
+
+        for i in range(len(emotions)):
+            v = emotions[i]
+            t = times[i]
+
+            if v == 'neutralp':
+                temp = df_neutralp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+        
+            elif v == 'neutraln':
+                temp = df_neutraln.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'angryp':
+                temp = df_angryp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'angryn':
+                temp = df_angryn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'fearfulp':
+                temp = df_fearp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'fearfuln':
+                temp = df_fearn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'happyp':
+                temp = df_happyp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'happyn':
+                temp = df_happypn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'sadp':
+                temp = df_sadp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            else:
+                temp = df_sadn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+    elif len(emotions) == 3:
+        times = [15,10,5]
+
+        for i in range(len(emotions)):
+            v = emotions[i]
+            t = times[i]
+
+            if v == 'neutralp':
+                temp = df_neutralp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+        
+            elif v == 'neutraln':
+                temp = df_neutraln.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'angryp':
+                temp = df_angryp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'angryn':
+                temp = df_angryn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'fearfulp':
+                temp = df_fearp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'fearfuln':
+                temp = df_fearn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'happyp':
+                temp = df_happyp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'happyn':
+                temp = df_happypn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'sadp':
+                temp = df_sadp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            else:
+                temp = df_sadn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+    elif len(emotions) == 4:
+        times = [10,9,8,3]
+
+        for i in range(len(emotions)):
+            v = emotions[i]
+            t = times[i]
+
+            if v == 'neutralp':
+                temp = df_neutralp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+        
+            elif v == 'neutraln':
+                temp = df_neutraln.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'angryp':
+                temp = df_angryp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'angryn':
+                temp = df_angryn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'fearfulp':
+                temp = df_fearp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'fearfuln':
+                temp = df_fearn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'happyp':
+                temp = df_happyp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'happyn':
+                temp = df_happypn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'sadp':
+                temp = df_sadp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            else:
+                temp = df_sadn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+    else:
+        times = [10,7,6,5,2]
+
+        for i in range(len(emotions)):
+            v = emotions[i]
+            t = times[i]
+
+            if v == 'neutralp':
+                temp = df_neutralp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+        
+            elif v == 'neutraln':
+                temp = df_neutraln.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'angryp':
+                temp = df_angryp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'angryn':
+                temp = df_angryn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'fearfulp':
+                temp = df_fearp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'fearfuln':
+                temp = df_fearn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'happyp':
+                temp = df_happyp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'happyn':
+                temp = df_happypn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            elif v == 'sadp':
+                temp = df_sadp.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+            else:
+                temp = df_sadn.sample(n=t)
+                data = pd.concat([data, temp[:]])
+
+    return data
+
+# Function to preprocess a list and return unique elements in the order of occurrence
+def pre(l):
+    result = [item for items, c in Counter(l).most_common() for item in [items] * c]
+    ul = []
+    for x in result:
+        if x not in ul:
+            ul.append(x)
+    return ul
+
+# Function to create a convolutional neural network (CNN) model
+def create_model():
+    # Define the model architecture
+    model = Sequential()
+    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))
+    model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.25))
+    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.25))
+    model.add(Flatten())
+    model.add(Dense(1024, activation='relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(7, activation='softmax'))
+
+    model.load_weights('model.h5')
+    return model
+
+# Function to insert user input into the SQLite database
+def insert_user_input(user_input,sentiment):
+    sentiment = 'Positive' if sentiment == 'P' else 'Negative'
+    cursor.execute('INSERT INTO user_inputs (user_input,sentiment) VALUES (?,?)', (user_input,sentiment,))
+    conn.commit()
+
+# Function to retrieve user inputs from the database
+def get_user_inputs():
+    cursor.execute('SELECT user_input,sentiment FROM user_inputs')
+    data = cursor.fetchall()
+    return data
+
+# Function to perform sentiment analysis using TextBlob
+def sentiment_analysis(text):
+    blob = TextBlob(text)
+    polarity = blob.sentiment.polarity
+    if polarity >= 0:
+        return 'P'
+    else:
+        return 'N'
+
+# Main function to run the Streamlit app
+def main():
+    model = create_model()
+    cv2.ocl.setUseOpenCL(False)
+    cap = cv2.VideoCapture(0)
+    
+    st.markdown("<h2 style='text-align: center; color: grey;'><b>Emotion based music recommendation</b></h2>", unsafe_allow_html=True)
+    st.markdown("<h5 style='text-align: center; color: grey;'><b>Click on the name of recommended song to reach website</b></h5>", unsafe_allow_html=True)
+ 
+    col1,col2,col3 = st.columns(3)
+
+    with col1:
+        c1,c2 = st.columns(2)
+        c1.subheader("Text")
+        c2.subheader("Sentiment")
+        user_inputs = get_user_inputs()
+        user_inputs.reverse()
+        for input_text,sentiment in user_inputs:
+            c1.write(f"- {input_text[:15]}")
+            c2.write(f"- {sentiment}")
+    
+    submit = False
+    user_input=""
+    with col2:
+        user_input = st.text_area("Enter Text:", height=500)
+        sentiment = sentiment_analysis(user_input)
+        c1,c2,c3 = st.columns(3)
+        submit = col2.button("Submit")
+        if submit and user_input != "":
+            insert_user_input(user_input,sentiment)
+
+    new_emotions = []
+    with col3:
+        if submit and user_input != "":
+            count = 0
+            emotions = []
+            while True:
+                emotions.clear()
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                face = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+                gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+                faces = face.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+                count = count + 1
+
+                for (x, y, w, h) in faces:
+                    cv2.rectangle(frame, (x, y - 50), (x + w, y + h + 10), (255, 0, 0), 2)
+                    roi_gray = gray[y:y + h, x:x + w]
+                    cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)
+
+                    prediction = model.predict(cropped_img)
+                    max_index = int(np.argmax(prediction))
+                    emotions.append(emotion_dict[max_index])
+                    cv2.putText(frame, emotion_dict[max_index], (x + 20, y - 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
+                    cv2.imshow('Video', cv2.resize(frame, (1000, 700), interpolation=cv2.INTER_CUBIC))
+
+                if cv2.waitKey(1) & 0xFF == ord('x'):
+                    break
+
+                if count >= 20:
+                    break
+
+            cap.release()
+            cv2.destroyAllWindows()
+
+            emotions = pre(emotions)
+
+            for i in emotions:
+                new_emotions.append(i.lower()+sentiment)
+
+        elif submit and user_input == "":
+            st.error("Please enter text to get recommendations")
+
+    new_df = fun(new_emotions)
+
+    col3.markdown("<h5 style='text-align: center; color: grey;'><b>Recommended song's with artist names</b></h5>", unsafe_allow_html=True)
+    col3.write("---------------------------------------------------------------------------------------------------------------------")
+
+    try:
+        for l,a,n,i in zip(new_df["link"],new_df['artist'],new_df['name'],range(30)):
+            col3.markdown("""<h4 style='text-align: center;'><a href={}>{} - {}</a></h4>""".format(l,i+1,n),unsafe_allow_html=True)
+            col3.markdown("<h5 style='text-align: center; color: grey;'><i>{}</i></h5>".format(a), unsafe_allow_html=True)
+            col3.write("---------------------------------------------------------------------------------------------------------------------")
+    except:
+        pass
+
+# Run the Streamlit app if the script is executed directly
+if __name__ == '__main__':
+    main()