import pandas as pd
import numpy as np
import random
import sklearn
from sklearn.cluster import KMeans
from sklearn.preprocessing import scale, StandardScaler, normalize
df2=pd.read_csv('movies_metadata.csv')


data=df2[['revenue','runtime','vote_average','vote_count','title']]

data=data.fillna(0)

scaler = StandardScaler()
data_scaled = scaler.fit_transform(data.iloc[:, :-1])



data=data.fillna(0)

kmeans_mod = KMeans(n_clusters=5, n_init=10)
kmeans_mod.fit(data_scaled)
labels =kmeans_mod.fit_predict(data_scaled)
data['cluster']=labels
data=data[['revenue','runtime','vote_average','vote_count','cluster','title']]


cdata=data[['cluster','title']]



cluster0=[]
cluster1=[]
cluster2=[]
cluster3=[]
cluster4=[]

filmname=''
for i in cdata.iloc():
     if i[0]==0:
      filmname=''
      filmname=i[1]
      #print(filmname)
      cluster0.append(filmname)
     elif i[0]==1:
        filmname=''
        filmname=i[1]
        cluster1.append(filmname)
     elif i[0]==2:
        filmname=''
        filmname=i[1]
        cluster2.append(filmname)
     if i[0]==3:
        filmname=''
        filmname=i[1]
        cluster3.append(filmname)
     if i[0]==4:
        filmname=''
        filmname=i[1]
        cluster4.append(filmname)
data=data.iloc[:, :-1]

cluster=[cluster0,cluster1,cluster2,cluster3,cluster4]
def recomender(input1,input2,input3):

  encod_input1=[]
  encod_input2=[]
  encod_input3=[]


  encod_input1.append(cluster0.count(input1))
  encod_input1.append(cluster1.count(input1))
  encod_input1.append(cluster2.count(input1))
  encod_input1.append(cluster3.count(input1))
  encod_input1.append(cluster4.count(input1))



  encod_input2.append(cluster0.count(input2))
  encod_input2.append(cluster1.count(input2))
  encod_input2.append(cluster2.count(input2))
  encod_input2.append(cluster3.count(input2))
  encod_input2.append(cluster4.count(input2))



  encod_input3.append(cluster0.count(input3))
  encod_input3.append(cluster1.count(input3))
  encod_input3.append(cluster2.count(input3))
  encod_input3.append(cluster3.count(input3))
  encod_input3.append(cluster4.count(input3))


  return encod_input1,encod_input2,encod_input3


def recomender2(a):
  s1=0
  s2=0
  s3=0
  for i in range(0,5):
    if a[0][i]!=0:
        s1=i
        #print(f'The first film is in cluster {i+1}')
    if a[1][i]!=0:
       s2=i
       #print(f'The second film is in cluster {i+1}')
    if a[2][i]!=0:
       s3=i
       #print(f'The third film is in cluster {i+1}')
  return s1, s2, s3



def recom(b):
  if b[0]==b[1]==b[2]:
    return random.choice(cluster[b[1]])
  if b[0]==b[1]!=b[2]:
    return random.choice(cluster[b[1]])
  if b[0]!=b[1]==b[2]:
     return random.choice(cluster[b[1]])
  if b[0]==b[2]!=b[1]:
     return random.choice(cluster[b[2]])
  if  b[0]!=b[2]!=b[1]:
     return random.choice(cluster[b[0]])

def original_recom(input1,input2,input3):
  return recom(recomender2(recomender(input1,input2,input3)))
    
import gradio as gr


def original_recom(input1,input2,input3):
  return recom(recomender2(recomender(input1,input2,input3)))
with gr.Blocks() as demo:
    gr.Markdown("Flip text or image files using this demo.")

    input1 = gr.Textbox(placeholder='first film name...')

    input2 = gr.Textbox(placeholder='second film name...')

    input3 = gr.Textbox(placeholder='third film name...')

    text_output=gr.Textbox( )


    text_button = gr.Button("Submit")
    text_button.click(original_recom, inputs=[input1,input2,input3], outputs=text_output)
demo.launch()