Upload app.py

app.py

@@ -1,13 +1,56 @@
 import gradio as gr
-import random as random
-
+import random
+import pandas as pd
 
 opo = pd.read_csv('oportunidades.csv')
 simulation = pd.read_csv('simulation1.csv')
+userID = max(simulation['userID']) + 1
 
 opo_n = random.randrange(len(opo))
+evaluated = [opo_n]
+
+def predict_next(inp):
+    global userID
+    global opo_n
+    global evaluated
+    global opo
+    global simulation
+
+    simulation = simulation.append({'userID': userID, 'itemID': opo_n, 'rating': inp}, ignore_index=True)
+    evaluated.append(opo_n)
+    
+    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 = items[est.index(max(est))]
+    return opo.loc[opo_n]['opo_texto']
 
 with gr.Blocks() as demo:
-    nota = gr.Slider(1,5, label = opo.loc[opo_n])
+    gr.Markdown("# MCTI Recommender System")
+    
+    current_opo = gr.Textbox(opo.loc[opo_n]['opo_texto'], label='Oportunidade')
+    nota = gr.Slider(1,5,step=1,label="Nota")
+    confirm = gr.Button("Confirmar")
+
+    confirm.click(fn=predict_next,
+               inputs=nota,
+               outputs=current_opo)
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()