| 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') |
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| data=df2[['revenue','runtime','vote_average','vote_count','title']] |
|
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| data=data.fillna(0) |
|
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| scaler = StandardScaler() |
| data_scaled = scaler.fit_transform(data.iloc[:, :-1]) |
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| data=data.fillna(0) |
|
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| 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']] |
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| cdata=data[['cluster','title']] |
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| cluster0=[] |
| cluster1=[] |
| cluster2=[] |
| cluster3=[] |
| cluster4=[] |
|
|
| filmname='' |
| for i in cdata.iloc(): |
| if i[0]==0: |
| filmname='' |
| filmname=i[1] |
| |
| 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): |
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| encod_input1=[] |
| encod_input2=[] |
| encod_input3=[] |
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| 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)) |
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| 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)) |
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| 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)) |
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| return encod_input1,encod_input2,encod_input3 |
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|
|
| def recomender2(a): |
| s1=0 |
| s2=0 |
| s3=0 |
| for i in range(0,5): |
| if a[0][i]!=0: |
| s1=i |
| |
| if a[1][i]!=0: |
| s2=i |
| |
| if a[2][i]!=0: |
| s3=i |
| |
| return s1, s2, s3 |
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|
| 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 |
|
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|
|
| 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.") |
|
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| input1 = gr.Textbox(placeholder='first film name...') |
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| input2 = gr.Textbox(placeholder='second film name...') |
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| input3 = gr.Textbox(placeholder='third film name...') |
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| text_output=gr.Textbox( ) |
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| text_button = gr.Button("Submit") |
| text_button.click(original_recom, inputs=[input1,input2,input3], outputs=text_output) |
| demo.launch() |
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