making the predict part working back

app.py

@@ -107,6 +107,7 @@ else:
 
     params, setup = user_input_features()
 
+    st.header("Work in progress - please be back in a few days")
     st.subheader("Instructions:")
     st.write("""
             1. Modify the dataset by using the sliders in the Dataset group on the left on the screen.
@@ -135,13 +136,14 @@ else:
     c1, c2, c3 = '#1e4a76', '#7dc0f7', '#ff7c0a'  # colors
     # sns.scatterplot(x=X, y=Y, color=c1)
     # st.pyplot()
+    fig, ax = plt.subplots()
     sns.lineplot(x=X, y=Y, color=c1)
     sns.lineplot(x=X_pred, y=Y_pred, color=c2, linestyle=':')
     plt.ylim(min(-2, min(Y), min(Y_pred)), max(2, max(Y), max(Y_pred)))
     plt.legend(['Train data', 'Test data'], loc=3)
     plt.xlabel('Sample number')
     plt.ylabel('Sample value')
-    st.pyplot()
+    st.pyplot(fig)
     st.write("The plot presents generated train and test data. Use the sliders on the left to modify the curve.")
 
 
@@ -163,21 +165,16 @@ else:
         waiters.append(st.image('wait.gif'))
         waiters.append(st.text("""The process should take around 20-60 seconds."""))
 
-        #  myUrl = 'http://localhost:7071/api/predict'
-        myUrl = 'https://rnn-background.azurewebsites.net/api/predict?code=a/X0yioXXY4CFVd9UFTw4MiyStNJ2qh3oae7FdFN7VBFMFhqe/qK7Q=='
-        # request = json.dumps(setup)
-        result = predict_series(data, steps=66, r1_nodes=14, r2_nodes=14, fc1_nodes=20)
-        # _ = [waiter.empty() for waiter in waiters]
-
-        result = np.array(range(5))
+        result = predict_series(**setup)
 
+        fig, ax = plt.subplots()
         sns.lineplot(x=X_pred, y=Y_pred, color=c2, linestyle=':')
         sns.lineplot(x=X, y=Y, color=c1)
         sns.lineplot(np.append(X[-1], np.arange(0, test_len, gran) + max(X) + gran), np.append(Y[-1], result['result']), color=c3)
         plt.legend(['Train data', 'Test data', 'Predicted data'], loc=3)
         plt.xlabel('Sample number')
         plt.ylabel('Sample value')
-        st.pyplot()
+        st.pyplot(fig)
 
         st.write("The prediction isn't good enough? Try to change settings in the model setup or increase the dataset length.")
         st.write('Training took {} epochs, Mean Squared Error: {:.2e}'.format(result['epochs'], result['loss']))

requirements.txt

@@ -1,6 +1,7 @@
 streamlit==1.17.0
 requests
-numpy
+numpy==1.19.5
 seaborn
 matplotlib
-tensorflow
+tensorflow
+sklearn