Update Readme.md

Author: ahmedbendebba1

README.md

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 # Weather-data-visualization
-Interactive SQL queries on BigQuery with Streamlit
+Interactive SQL queries on BigQuery with Streamlit. This project demonstrates running SQL queries on [BigQuery](https://cloud.google.com/bigquery/) public datasets and rendering results into an interactive [Streamlit](https://streamlit.io) app. 
+
+Live version: http://34.76.236.119/
+
+![alt text](record.gif)
+
+# Architecture
+![alt text](architecture.jpg)
+
+
+# Running on local machine 
+
+To run the application, build the docker image and run it using the following commands
+
+```
+docker build -t app .
+```
+
+```
+docker run --rm -p 8080:8080 app:latest
+```
+
+The application will be available on http://0.0.0.0:8080. 
+
+**Note:**
+In order to use BigQuery API in python, we need to create a credential file (JSON file) and declare its path as an environment variable `GOOGLE_APPLICATION_CREDENTIALS` (by default defined in `modules/data_query.py`). 
+To create the credential file follow steps explained here: https://cloud.google.com/bigquery/docs/quickstarts/quickstart-client-libraries
+
+
+# Running on cloud
+
+- Build image and push it to a PRIVATE  Container Registry (don't forget about your credentials) .
+
+From a GCP project, we will use Google Shell to build the image and push it to GCR (container registry).
+
+```
+# Get name of project 
+# For illustration we will call it PROJECT-ID
+
+gcloud config get-value project
+```
+
+Create the following Dockerfile in your CloudShell session .
+
+```
+FROM ahmedbendebba/weather-visualisation:latest
+
+# Google Run uses env variable PORT 
+
+CMD gunicorn --bind :$PORT wsgi:app
+```
+
+Finally, we can build and submit the image to GCR.
+
+```
+gcloud builds submit --tag gcr.io/PROJECT-ID/weather-visualisation
+```
+
+- Create an instance on Google Compute Engine
+- 
+- Deploy on Google Compute Engine by running this image gcr.io/PROJECT-ID/weather-visualisation
+
+```
+export GCR_TAG=gcr.io/PROJECT-ID/weather-visualization1 
+
+docker run -p 80:8080 -e PORT=8080 $GCR_TAG
+```
+
+# Code
+
+- `app.py` : Streamlit script to define the UI. 
+
+Modules: 
+
+- `modules/data_query.py`: Define Data fetcher from BigQuery (takes an SQL command and return results in Pandas DataFrame).
+
+- `modules/sql_queries.py`: Pre-defined parametrized SQL commands. 
+
+- `modules/utils.py`: Contains a  a function to compute size of data anluzed by a given query. 
+
+- `modules/plot.py`: Plot functions. 
+
+
+--- 
+By Ahmed
+
+