Updated the documentation with instructions for running the application locally.

README.md

@@ -11,6 +11,24 @@ license: cc-by-4.0
 short_description: Fake ECG Generator
 ---
 
+# Deepfake ECG Generator GUI
+
 Allows to generate ECGs. Based on the following paper:
 
-https://www.nature.com/articles/s41598-021-01295-2
+https://www.nature.com/articles/s41598-021-01295-2
+
+# Run locally
+
+## Prepare venv and install dependencies
+```bash
+mkdir -p ~/python-environments/deepfake-ecg
+python3 -m venv ~/python-environments/deepfake-ecg
+. ~/python-environments/deepfake-ecg/bin/activate
+pip install -r requirements.txt
+```
+
+## Run the application
+```bash
+./app.py
+```
+Then, connect a web browser to http://127.0.0.1:7860/ to use the application

app.py

@@ -1,6 +1,8 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
 import io
 import gradio as gr
-#from transformers import pipeline
 from transformers import AutoModel
 import ecg_plot
 import matplotlib.pyplot as plt
@@ -11,17 +13,16 @@ model = AutoModel.from_pretrained("deepsynthbody/deepfake_ecg", trust_remote_cod
 
 def predict():
     prediction = (model(1)[0].t()/1000) # to micro volte
-    
-    
+
     lead_III = (prediction[1] - prediction[0]).unsqueeze(dim=0)
     lead_aVR = ((prediction[0] + prediction[1])*(-0.5)).unsqueeze(dim=0)
     lead_aVL = (prediction[0] -  prediction[1]* 0.5).unsqueeze(dim=0)
     lead_aVF = (prediction[1] -  prediction[0]* 0.5).unsqueeze(dim=0)
     all = torch.cat((prediction, lead_III, lead_aVR, lead_aVL, lead_aVF), dim=0)
     all_corrected = all[torch.tensor([0,1,8, 9, 10, 11, 2,3,4,5,6,7])]
-    
+
     ecg_plot.plot(all_corrected, sample_rate = 500, title = 'ECG 12')
-    
+
     #ecg_plot.show()
     buf = io.BytesIO()
     plt.savefig(buf, format="png")
@@ -33,4 +34,4 @@ gr.Interface(
     inputs=None,
     outputs="image",
     title="Generating Fake ECGs",
-).launch() 
+).launch()

requirements.txt

@@ -1,5 +1,6 @@
+gradio
 transformers
 torch
 ecg-plot
 matplotlib
-Pillow
+Pillow