Update app.py

app.py

@@ -123,16 +123,17 @@ if file_upload is not None:
     column = data["S11"].values.reshape(1,-1)
     with open("automl4.pkl", "rb") as f:
         model = pickle.load(f)
-    predictions = model.predict(column).tolist()
+    predictions = model.predict(column)
     print(type(predictions))
     #st.write(predictions)
     #pred_clip = np.clip(pred_clip, [0.2,0.4,3.9,0.2,13.9,13.8,13.2],[1.01,1.21,4.71,0.8,14.701,14.201,14.001])
     #predictions = pd.DataFrame(pred_clip.tolist(), columns = ["w1","w2","w3","s1","l1","l2","l3"])
 
-    r2_score = r2_score(best_column, predictions)
+    r2_score = r2_score(best_column, predictions.squeeze())
     #st.write(r2_score)
     if r2_score < 0.98:
         predictions = perturb_array(best_column)
+        st.write(r2_score)
         st.write(predictions)
         st.write(type(predictions))
         predictions = pd.DataFrame([predictions], columns = ["w1","w2","w3","s1","l1","l2","l3"])