Update README.md

README.md

@@ -384,6 +384,7 @@ def build_display_text(opo_n):
 ```
 
 Here it will be generate 4 random opportunities
+
 ```python
 opo_n_one = random.randrange(len(opo))
 opo_n_two = random.randrange(len(opo))
@@ -391,7 +392,10 @@ opo_n_three = random.randrange(len(opo))
 opo_n_four = random.randrange(len(opo))
 
 evaluated = []
+```
 
+```python
+This function predict the 
 def predict_next(option, nota):
     global userID
     global opo_n_one
@@ -401,9 +405,9 @@ def predict_next(option, nota):
     global evaluated
     global opo
     global simulation
-
+# the "selected" takes the number, on our database, of the rated opportunity
     selected = [opo_n_one, opo_n_two, opo_n_three, opo_n_four][int(option)-1]
-
+# Here is created a new database called simulation, that takes the previous simulation then adds a new line with te ID of the user, the rated item and the rate.  integrates the selected opportunity
     simulation = simulation.append({'userID': userID, 'itemID': selected, 'rating': nota}, ignore_index=True)
     evaluated.append(selected)
     
@@ -432,7 +436,11 @@ def predict_next(option, nota):
     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)
+```
+
+Here we have the interation of gradio, that allows the construction of the app
 
+```python
 
 with gr.Blocks() as demo:
     with gr.Row():
@@ -457,7 +465,9 @@ with gr.Blocks() as demo:
                outputs=[one_opo, two_opo, three_opo, four_opo])
 
 if __name__ == "__main__":
-    demo.launch() 
+    demo.launch()
+```
+
 ## Benchmarks
 
 ```python