feat: add new recommend by gender movie or tv show (#17) closes #16

requirements.txt

@@ -1,4 +1,6 @@
 streamlit==1.26.0
 requests==2.31.0
 pandas==2.0.3
-numpy==1.24.0
+numpy==1.24.0
+joblib==1.3.2
+scikit-learn==1.3.0

src/components/query.py

@@ -1,5 +1,7 @@
+import os
 import pandas as pd
 from components.responses import response_markdown, response_recommends
+import joblib as jl
 
 def recommends(
                 dataset:pd.DataFrame,
@@ -30,4 +32,25 @@ def recommends(
     result = dataset[dataset['clusters_genre_type'] == k_id][cols_view][:int(top_n)]
     result.set_index('title')
 
+    return response_recommends(result)
+
+
+def recommender_by_gender(dataset, options: list, cols: list):
+
+    if options == 0:
+        return response_markdown('Empty')
+
+    input_group = {x: [0] for x in cols}
+    for x in options:
+        input_group[x] = [1]
+    y_input = pd.DataFrame(input_group)
+    path_model =  os.path.join('./src/data', 'models', 'model_kmeans_20231118')
+    model = jl.load(f'{path_model}.pkl')
+
+    pred_test = model.predict(y_input)
+    pred_test[0]
+
+    result = dataset[dataset['clusters_genre_type'] == pred_test[0] ][['title', 'gender_type', 'channel_streaming']]
+    result.set_index('title')
+
     return response_recommends(result)

src/components/responses.py

@@ -5,7 +5,7 @@
 def response_recommends(results: pd.DataFrame)-> st.dataframe:    
     st.dataframe(
         results,
-        use_container_width=False,        
+        use_container_width=True,
         )
 
 def response_markdown(text: str) -> st.markdown: