main+movie_recommendation.py

from keras.preprocessing.image import img_to_array
import imutils
import cv2
from keras.models import load_model
import numpy as np
import tkinter as tk
from tkinter import*
import playsound # to play saved mp3 file
from gtts import gTTS # google text to speech
import os
from matplotlib import pyplot as plt 


num=1
def speaks(output):
    global num
    num +=1

 #print("Safety Assistant : ", output)
    tospeak = gTTS(text=output, lang='en-US', slow=False)
    file = str(num)+".mp3"
    tospeak.save(file)
    playsound.playsound(file, True)
    os.remove(file)

def music():
    import music_recommendation
def movie():
    
    
    speaks("MOVIES WILL BE SUGGESTED BASED ON THE EMOTION DETECTED")

    # parameters for loading data and images
    detection_model_path = 'haarcascade_files/haarcascade_frontalface_default.xml'
    emotion_model_path = 'models/_mini_XCEPTION.102-0.66.hdf5'

    # hyper-parameters for bounding boxes shape
    # loading models
    face_detection = cv2.CascadeClassifier(detection_model_path)
    emotion_classifier = load_model(emotion_model_path, compile=False)
    EMOTIONS = ["angry" ,"disgust","scared", "happy", "sad", "surprised",
     "neutral"]


    #feelings_faces = []
    #for index, emotion in enumerate(EMOTIONS):
       # feelings_faces.append(cv2.imread('emojis/' + emotion + '.png', -1))

    # starting video streaming
    cv2.namedWindow('your_face')
    camera = cv2.VideoCapture(0)
    while True:
        frame = camera.read()[1]
        #reading the frame
        frame = imutils.resize(frame,width=300)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_detection.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=(30,30),flags=cv2.CASCADE_SCALE_IMAGE)
        
        canvas = np.zeros((250, 300, 3), dtype="uint8")
        frameClone = frame.copy()
        if len(faces) > 0:
            faces = sorted(faces, reverse=True,
            key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
            (fX, fY, fW, fH) = faces
                        # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
                # the ROI for classification via the CNN
            roi = gray[fY:fY + fH, fX:fX + fW]
            roi = cv2.resize(roi, (64, 64))
            roi = roi.astype("float") / 255.0
            roi = img_to_array(roi)
            roi = np.expand_dims(roi, axis=0)
            
            
            preds = emotion_classifier.predict(roi)[0]
            emotion_probability = np.max(preds)
            label = EMOTIONS[preds.argmax()]
        else:
            import tkinter as tk
            from tkinter import messagebox
            root123= tk.Tk()
            root123.withdraw()
            msgbox=tk.messagebox.showinfo('ERROR MESSAGE', "FACE NOT DETECTED,PLEASE TRY AGAIN!!")
            break

     
        for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)):
                    # construct the label text
                    text = "{}: {:.2f}%".format(emotion, prob * 100)

                    # draw the label + probability bar on the canvas
                   # emoji_face = feelings_faces[np.argmax(preds)]

                    
                    w = int(prob * 300)
                    cv2.rectangle(canvas, (7, (i * 35) + 5),
                    (w, (i * 35) + 35), (0, 0, 255), -1)
                    cv2.putText(canvas, text, (10, (i * 35) + 23),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.45,
                    (255, 255, 255), 2)
                    cv2.putText(frameClone, label, (fX, fY - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
                    cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
                                  (0, 0, 255), 2)
    #    for c in range(0, 3):
    #        frame[200:320, 10:130, c] = emoji_face[:, :, c] * \
    #        (emoji_face[:, :, 3] / 255.0) + frame[200:320,
    #        10:130, c] * (1.0 - emoji_face[:, :, 3] / 255.0)


        cv2.imshow('your_face', frameClone)
        cv2.imshow("Probabilities", canvas)
        if cv2.waitKey(1) & 0xFF == ord('s'):
            from bs4 import BeautifulSoup as SOUP 
            import re 
            import requests as HTTP 

            # Main Function for scraping 
            def main(emotion):
                em=emotion.lower()
                import tkinter as tk
                from tkinter import messagebox
                root= tk.Tk()
                root.withdraw()
                
                msgbox=tk.messagebox.showinfo('EMOTION', em.upper())
                
                # IMDb Url for Drama genre of 
                # movie against emotion Sad 
                if(em == "sad"):
                    speaks('GENRE ALLOCATED IS DRAMA')
                    urlhere = 'http://www.imdb.com/search/title?genres=drama&title_type=feature&sort=moviemeter, asc'
                    

                # IMDb Url for Musical genre of 
                # movie against emotion Disgust 
                elif(em == "disgust"):
                    speaks('GENRE ALLOCATED IS MUSICAL')
                    
                    urlhere = 'http://www.imdb.com/search/title?genres=musical&title_type=feature&sort=moviemeter, asc'

                # IMDb Url for Family genre of 
                # movie against emotion Anger 
                elif(em == "angry"):
                    speaks('GENRE ALLOCATED IS FAMILY')
                    
                    urlhere = 'http://www.imdb.com/search/title?genres=family&title_type=feature&sort=moviemeter, asc'

                # IMDb Url for Thriller genre of 
                # movie against emotion Anticipation 
                elif(em == "neutral"):
                    speaks('GENRE ALLOCATED IS THRILLER')
                    
                    urlhere = 'http://www.imdb.com/search/title?genres=thriller&title_type=feature&sort=moviemeter, asc'

                # IMDb Url for Sport genre of 
                # movie against emotion Fear 
                elif(em == "scared"):
                    speaks('GENRE ALLOCATED IS SPORTS')
                    
                    urlhere = 'http://www.imdb.com/search/title?genres=sport&title_type=feature&sort=moviemeter, asc'

                # IMDb Url for Thriller genre of 
                # movie against emotion Enjoyment 
                elif(em == "happy"):
                    speaks('GENRE ALLOCATED IS THRILLER')
                    
                    urlhere = 'http://www.imdb.com/search/title?genres=thriller&title_type=feature&sort=moviemeter, asc'

                
                

                # IMDb Url for Film_noir genre of 
                # movie against emotion Surprise 
                elif(em == "surprised"):
                    speaks('GENRE ALLOCATED IS FILM NOIR')
                    
                    urlhere = 'http://www.imdb.com/search/title?genres=film_noir&title_type=feature&sort=moviemeter, asc'

                # HTTP request to get the data of 
                # the whole page 
                response = HTTP.get(urlhere) 
                data = response.text 

                # Parsing the data using 
                # BeautifulSoup 
                soup = SOUP(data, "lxml") 

                # Extract movie titles from the 
                # data using regex 
                title = soup.find_all("a", attrs = {"href" : re.compile(r'\/title\/tt+\d*\/')})
                title1 = soup.find_all("h3",{"class":"lister-item-header"})
                rating = soup.find_all("div", {"class": "inline-block ratings-imdb-rating"})
                import tkinter
                from tkinter import ttk

                root12 = tkinter.Tk()
                root12.geometry("600x600")
                root12.title("MOVIES RECOMMENDED FOR DETECTED EMOTION")
                tree = ttk.Treeview(root12)
                tree["columns"]=("one","two")
                tree.column("one", width=200)
                tree.column("two", width=200)
                
                style = ttk.Style(root12)
                style.configure('Treeview', rowheight=45)
                        
                
                tree.heading("one", text="MOVIES")
                tree.heading("two", text="RATINGS")       
                for i in range(9,-1,-1):
                    tree.insert("" , 0,    text="", values=(title1[i].text,rating[i].text))
                tree.pack()
                
                return title1
                    

            # Driver Function 
            if __name__ == '__main__': 

                #emotion = input("Enter the emotion: ") 
                a = main(label) 
               
                cv2.destroyAllWindows()
                break

           

    camera.release()
    cv2.destroyAllWindows()



    


speaks("WELCOME TO ENTERTAINMENT RECOMMENDATION SYSTEM BASED ON EMOTION RECOGNITION")

speaks("In the next window,choose what you want us to suggest between MOVIES or MUSIC ")
root = Tk()
root.geometry("200x200")

var = IntVar()
R1 = Radiobutton(root, text="MOVIES", variable=var, value=1,
                  command=movie)
R1.pack( anchor = W )

R2 = Radiobutton(root, text="MUSIC", variable=var, value=2,command=music)
R2.pack( anchor = W )

label = Label(root)
label.pack()