testing externalizing texts

app.py

@@ -1,6 +1,8 @@
 import os
 import sys
 import subprocess
+import toml
+from argparse import Namespace
 import numpy as np
 import logging
 import gradio as gr
@@ -34,6 +36,9 @@ DATA = np.empty((len(SNRS), N, *DATA_SHAPE), dtype=np.complex128)
 TRUTH = np.empty((len(SNRS), N, *ETA_SHAPE))
 ESTIM = np.empty((len(SNRS), N, *ETA_SHAPE))
 
+# load texts
+TEXTS = Namespace(**toml.load("texts.toml"))
+
 def install_deepest():
     git_token = os.environ["GIT_TOKEN"]
     git_url = os.environ["GIT_URL"]
@@ -174,6 +179,10 @@ def demo():
             """
         )
         
+        gr.Markdown(
+            TEXTS.contact
+        )
+        
     demo.launch()
     
 def main():

requirements.txt

@@ -1,3 +1,4 @@
+argparse
 datetime
 requests
 toml

texts.toml

@@ -0,0 +1,41 @@
+introduction = """
+    # deepest
+    **deepest** (short for **deep** learning parameter **est**imator) is a CNN trained to perform signal parameter estimation.
+    The corresponding paper can be found on [arxiv](https://arxiv.org/abs/2211.04846).
+                
+    This applet lets you explore the `deepest` with data from the validationset.
+    You can also upload your own data and see how it works for your signals.
+    """
+
+try_your_own = """
+    ## Try with your own data.
+    Good news everyone! If you want to try `deepest` with your own data, here is your chance. 
+    Afterall there is no need to believe a paper making vague claims about an algorithms performance.
+    But keep in mind that slight deviations from the training-data distribution might throw off `deepest`.
+    Its a Neural Network after all.  
+    You can upload a `numpy` file (both `*.npy` and `*.npz` work) with your test data.
+    Ensure the data meets the requirements, such that you get good results. 
+    
+    ### Requirements
+    - complex-valued baseband data for the time-variant Channel transfer function $H(f,t)$ (e.g. from a channel-sounding campaign)
+    - array shape must be `batch_size x f_bins x t_bins`
+    - ideally `f_bins`=64 and `t_bins`=64, otherwise the data will be downsampled by the 2D-DFT, which might not be ideal in all scenarios.
+    
+    **Important** This demo runs on Huggingface. You are responsible for the data you upload. Do not upload any data that is confidential or unsuitable in this context.
+    """
+
+acknowledgements = """
+    ## Acknowledgements
+    The authors acknowledge the financial support by the Federal Ministry of Education and Research of Germany in the project “Open6GHub” (grant number: 16KISK015).
+        
+    The authors give special thanks to Henning Schwanbeck (HPC team leader) of the TU Ilmenau Computer Center for his valuable support.
+    """
+
+contact = """
+    ## Contact
+    If you have technical or scientific questions or encounter any issues in the use of this applet, please let me know.
+    You can either 
+        - write a me an email to [steffen.schieler@tu-ilmenau.de](mailto:steffen.schieler@tu-ilmenau.de)
+        - or start a new discussion in the [Community Tab](https://huggingface.co/spaces/EMS-TU-Ilmenau/deepest-demo/discussions)
+
+    """