Update README.md

README.md

@@ -239,8 +239,7 @@ To train the Latent Dirichlet allocation (LDA) model, it was used a database of
 
 ## Checkpoints
 
-- Usage Example
-
+- Example
 ```python
 
 data=Data()
@@ -355,6 +354,104 @@ ranking = model_table('lda_rankings')
 sys.stdout = sys.__stdout__ # Codigo para reativar os prints
 
 ```
+
+- Usage Example
+
+In this section it will be explained how the recommendation is made for the user
+```python
+
+import gradio as gr
+import random
+import pandas as pd
+
+opo = pd.read_csv('oportunidades_results.csv', lineterminator='\n')
+# opo = opo.iloc[np.where(opo['opo_brazil']=='Y')]
+simulation = pd.read_csv('simulation2.csv')
+userID = max(simulation['userID']) + 1
+
+def build_display_text(opo_n):
+    
+    title = opo.loc[opo_n]['opo_titulo']
+    link = opo.loc[opo_n]['link']
+    summary = opo.loc[opo_n]['facebook-bart-large-cnn_results']
+
+    display_text = f"**{title}**\n\nURL:\n{link}\n\nSUMMARY:\n{summary}"
+
+    return display_text
+
+opo_n_one = random.randrange(len(opo))
+opo_n_two = random.randrange(len(opo))
+opo_n_three = random.randrange(len(opo))
+opo_n_four = random.randrange(len(opo))
+
+evaluated = []
+
+def predict_next(option, nota):
+    global userID
+    global opo_n_one
+    global opo_n_two
+    global opo_n_three
+    global opo_n_four
+    global evaluated
+    global opo
+    global simulation
+
+    selected = [opo_n_one, opo_n_two, opo_n_three, opo_n_four][int(option)-1]
+
+    simulation = simulation.append({'userID': userID, 'itemID': selected, 'rating': nota}, ignore_index=True)
+    evaluated.append(selected)
+    
+    from surprise import Reader
+    reader = Reader(rating_scale=(1, 5))
+
+    from surprise import Dataset
+    data = Dataset.load_from_df(simulation[['userID', 'itemID', 'rating']], reader)
+    trainset = data.build_full_trainset()
+
+    from surprise import SVDpp
+    svdpp = SVDpp()
+    svdpp.fit(trainset)
+
+    items = list()
+    est = list()
+
+    for i in range(len(opo)):
+        if i not in evaluated:
+            items.append(i)
+            est.append(svdpp.predict(userID, i).est)
+
+    opo_n_one = items[est.index(sorted(est)[-1])]
+    opo_n_two = items[est.index(sorted(est)[-2])]
+    opo_n_three = items[est.index(sorted(est)[-3])]
+    opo_n_four = items[est.index(sorted(est)[-4])]
+
+    return build_display_text(opo_n_one), build_display_text(opo_n_two), build_display_text(opo_n_three), build_display_text(opo_n_four)
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        one_opo = gr.Textbox(build_display_text(opo_n_one), label='Oportunidade 1')
+        two_opo = gr.Textbox(build_display_text(opo_n_two), label='Oportunidade 2')
+
+    with gr.Row():
+        three_opo = gr.Textbox(build_display_text(opo_n_three), label='Oportunidade 3')
+        four_opo = gr.Textbox(build_display_text(opo_n_four), label='Oportunidade 4')
+
+    with gr.Row():
+        option = gr.Radio(['1', '2', '3', '4'], label='Opção', value = '1')
+
+    with gr.Row():
+        nota = gr.Slider(1,5,step=1,label="Nota 1")
+
+    with gr.Row():
+        confirm = gr.Button("Confirmar")
+
+        confirm.click(fn=predict_next,
+               inputs=[option, nota],
+               outputs=[one_opo, two_opo, three_opo, four_opo])
+
+if __name__ == "__main__":
+    demo.launch() 
 ## Benchmarks
 
 ```python