Merge pull request #25 from LaReserveTech/streamlit

Streamlit

requirements.txt

@@ -0,0 +1,8 @@
+streamlit
+google
+pandas
+numpy
+pybase64
+seaborn
+matplotlib
+plotly

streamlit.py

@@ -1,13 +1,69 @@
 import streamlit as st
+import pandas as pandas
+import numpy as np
+import pybase64 as base64
+import seaborn as sns 
+import matplotlib.pypot as plt
+import plotly.express as px 
+
+## Début du display
 st.title('Water Tracker')
 
+st.sidebar.title('Navigation')
 st.sidebar.radio('Indicateurs de niveaux d'eau', [":rainbow["Sècheresse"]", "Autre indicateur" , "Water Tracker:movie_camera:"])
-pages = ['page1', 'page2', 'page3']
+pages = ["Sècheresse", "Autre indicateur" , "Water Tracker"]
+
+
+# connection = connection to db / addresse de files dans S3 (dispo sous url)
+[gcp_service_account]
+type = "service_account"
+project_id = "xxx"
+private_key_id = "xxx"
+private_key = "xxx"
+client_email = "xxx"
+client_id = "xxx"
+auth_uri = "https://accounts.google.com/o/oauth2/auth"
+token_uri = "https://oauth2.googleapis.com/token"
+auth_provider_x509_cert_url = "https://www.googleapis.com/oauth2/v1/certs"
+client_x509_cert_url = "xxx"
+
+# streamlit_app.py
+
+import streamlit as st
+from google.oauth2 import service_account
+from google.cloud import bigquery
+
+# Create API client.
+credentials = service_account.Credentials.from_service_account_info(
+    st.secrets["gcp_service_account"]
+)
+client = bigquery.Client(credentials=credentials)
+
+# Perform query.
+# Uses st.cache_data to only rerun when the query changes or after 10 min.
+@st.cache_data(ttl=600)
+def run_query(query):
+    query_job = client.query(query)
+    rows_raw = query_job.result()
+    # Convert to list of dicts. Required for st.cache_data to hash the return value.
+    rows = [dict(row) for row in rows_raw]
+    return rows
+
+rows = run_query("SELECT word FROM `bigquery-public-data.samples.shakespeare` LIMIT 10")
+
+# Print results.
+st.write("Some wise words from Shakespeare:")
+for row in rows:
+    st.write("✍️ " + row['word'])
 
-connection = connection to db / addresse de files dans S3 (dispo sous url)
+#df = to_dataframe(data extracted from database) 
 
 if page == pages[0]:
   #things to put on the page
+  fig, ax = plt.subplots()
+  ax.plot(df.x, df.y)
+  st.title('title')
+  st.pyplot(fig)
 if page == pages[1]:
   #things to put on the second page
 if page == pages[2]: