Update app.py

app.py

@@ -239,43 +239,43 @@ model = from_pretrained_keras("keras-io/timeseries_forecasting_for_weather")
 #%% 
 st.set_option('deprecation.showPyplotGlobalUse', False)
 def plot():
-    n = st.sidebar.slider("Step", min_value = 1, max_value=5, value = 1)
-    def show_plot(plot_data, delta, title):
-        labels = ["History", "True Future", "Model Prediction"]
-        marker = [".-", "rx", "go"]
-        time_steps = list(range(-(plot_data[0].shape[0]), 0))
-        if delta:
-            future = delta
-        else:
-            future = 0
-
-        plt.title(title)
-        for i, val in enumerate(plot_data):
-            if i:
-                plt.plot(future, plot_data[i], marker[i], markersize=10, label=labels[i])
-            else:
-                plt.plot(time_steps, plot_data[i].flatten(), marker[i], label=labels[i])
-        plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05),
-          ncol=3, fancybox=True, shadow=True)
-        plt.xlim([time_steps[0], (future + 5) * 2])
-        plt.xlabel("Time-Step")
-        plt.show()
-        return
-
-
-for x, y in dataset_val.take(n):
-	if ("0" == "mycustom"):
-		show_plot(
-			[x[0][:, 1].numpy(), y[0].numpy(), model.predict(x)[0]],
-			12,
-			f"{n} Step Prediction",
-		)
-	if ("0" != "mycustom"):
-		show_plot(
-			[x[0][:, 0].numpy(), y[0].numpy(), model.predict(x)[0]],
-			12,
-			f"{n} Step Prediction",
-		)
+	n = st.sidebar.slider("Step", min_value = 1, max_value=5, value = 1)
+	def show_plot(plot_data, delta, title):
+		labels = ["History", "True Future", "Model Prediction"]
+		marker = [".-", "rx", "go"]
+		time_steps = list(range(-(plot_data[0].shape[0]), 0))
+		if delta:
+			future = delta
+		else:
+			future = 0
+
+		plt.title(title)
+		for i, val in enumerate(plot_data):
+			if i:
+				plt.plot(future, plot_data[i], marker[i], markersize=10, label=labels[i])
+			else:
+				plt.plot(time_steps, plot_data[i].flatten(), marker[i], label=labels[i])
+		plt.legend(loc='lower center', bbox_to_anchor=(0.5, 1.05),
+			ncol=3, fancybox=True, shadow=True)
+		plt.xlim([time_steps[0], (future + 5) * 2])
+		plt.xlabel("Time-Step")
+		plt.show()
+		return
+
+
+	for x, y in dataset_val.take(n):
+		if ("0" == "mycustom"):
+			show_plot(
+				[x[0][:, 1].numpy(), y[0].numpy(), model.predict(x)[0]],
+				12,
+				f"{n} Step Prediction",
+			)
+		if ("0" != "mycustom"):
+			show_plot(
+				[x[0][:, 0].numpy(), y[0].numpy(), model.predict(x)[0]],
+				12,
+				f"{n} Step Prediction",
+			)
 
 
 fig = plot()