fix: update user in Dockerfile and add initial Streamlit app

Dockerfile

@@ -2,9 +2,9 @@ FROM python:3.9-slim
 
 
 USER root
-RUN useradd -ms /bin/bash jedha
+RUN useradd -ms /bin/bash user
 RUN mkdir /app 
-RUN chown -R jedha /app
+RUN chown -R user /app
 
 WORKDIR /app
 
@@ -18,16 +18,15 @@ RUN apt-get update && apt-get install -y \
 COPY requirements.txt ./
 RUN pip3 install -r requirements.txt
 
-USER jedha
+USER user
 
 WORKDIR /app
 
 COPY . .
 
-RUN git clone https://github.com/pradelf/getaround.git ./app
 
 EXPOSE 8501
 
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
 
-ENTRYPOINT ["streamlit", "run", "/app/app/app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+ENTRYPOINT ["streamlit", "run", "/app/src/app.py", "--server.port=8501", "--server.address=0.0.0.0"]

src/app.py

@@ -0,0 +1,116 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px 
+import plotly.graph_objects as go
+import numpy as np
+
+### CONFIG
+st.set_page_config(
+    page_title="E-commerce",
+    page_icon="💸",
+    layout="wide"
+  )
+
+### TITLE AND TEXT
+st.title("Build dashboards with Streamlit 🎨")
+
+st.markdown("""
+    Welcome to this awesome `streamlit` dashboard. This library is great to build very fast and
+    intuitive charts and application running on the web. Here is a showcase of what you can do with
+    it. Our data comes from an e-commerce website that simply displays samples of customer sales. Let's check it out.
+    Also, if you want to have a real quick overview of what streamlit is all about, feel free to watch the below video 👇
+""")
+
+### LOAD DATA
+DATA_PRICING = ('../Data/get_around_pricing_project.csv')
+
+DATA_ANALYSIS = ('../Data/get_around_delay_analysis.csv')
+
+# this lets the cache activated : usage d'un décorateur python pour ajouter des fonctionnalité 
+# : st.cache_data et st.cache_resource qui remplace st.cache qui va devenir obsolète.
+# https://docs.streamlit.io/develop/api-reference/caching-and-state/st.cache_data
+# https://docs.streamlit.io/develop/api-reference/caching-and-state/st.cache_resource
+@st.cache_data 
+def load_data(file, nrows, delimiter=","):
+    data = pd.read_csv(file, nrows=nrows,delimiter=delimiter)
+    #data["Date"] = data["Date"].apply(lambda x: pd.to_datetime(",".join(x.split(",")[-2:])))
+    #data["currency"] = data["currency"].apply(lambda x: pd.to_numeric(x[1:]))
+    return data
+
+data_load_state = st.text('Loading data...')
+data_pricing = load_data(DATA_PRICING,1000)
+data_analysis = load_data(DATA_ANALYSIS,1000)
+data_load_state.text("") # change text from "Loading data..." to "" once the the load_data function has run
+
+## Run the below code if the check is checked ✅
+if st.checkbox('Show raw data'):
+    st.subheader('Raw data')
+    st.write(data_pricing) 
+### SHOW GRAPH STREAMLIT
+
+price_per_model = data_pricing["price"]
+st.bar_chart(price_per_model)
+
+### SHOW GRAPH PLOTLY + STREAMLIT
+
+st.subheader("Simple bar chart built with Plotly")
+st.markdown("""
+    Now, the best thing about `streamlit` is its compatibility with other libraries. For example, you
+    don't need to actually use built-in charts to create your dashboard, you can use :
+    
+    * [`plotly`](https://docs.streamlit.io/library/api-reference/charts/st.plotly_chart) 
+    * [`matplotlib`](https://docs.streamlit.io/library/api-reference/charts/st.pyplot)
+    * [`bokeh`](https://docs.streamlit.io/library/api-reference/charts/st.bokeh_chart)
+    * ...
+    This way, you have all the flexibility you need to build awesome dashboards. 🥰
+""")
+fig = px.histogram(data.sort_values("country"), x="country", y="currency", barmode="group")
+st.plotly_chart(fig, use_container_width=True)
+
+
+### SIDEBAR
+st.sidebar.header("Build dashboards with Streamlit")
+st.sidebar.markdown("""
+    * [Load and showcase data](#load-and-showcase-data)
+    * [Charts directly built with Streamlit](#simple-bar-chart-built-directly-with-streamlit)
+    * [Charts built with Plotly](#simple-bar-chart-built-with-plotly)
+    * [Input Data](#input-data)
+""")
+e = st.sidebar.empty()
+e.write("")
+st.sidebar.write("GetAround Project")
+
+### EXPANDER
+
+with st.expander("⏯️ Watch this 15min tutorial"):
+    st.video("https://youtu.be/B2iAodr0fOo")
+
+st.markdown("---")
+
+#### CREATE TWO COLUMNS
+col1, col2 = st.columns(2)
+
+with col1:
+    # visu des widgets
+    st.markdown("First column")
+    car_id= st.selectbox("Select a country you want to see all time sales", data_analysis["car_id"].sort_values().unique())
+    # intelligence et contrôle du widget
+    rental_canceled = data_analysis[data_analysis["state"]=="canceled"]
+    fig = px.histogram(rental_canceled, x="time_delta_with_previous_rental_in_minutes", y="delay_at_checkout_in_minutes")
+    fig.update_layout(bargap=0.2)
+    st.plotly_chart(fig, use_container_width=True)
+
+with col2:
+    st.markdown("Second column")
+    with st.form("average_sales_per_country"):
+      model = st.selectbox("Select a model you want to see", data_pricing["model_key"].sort_values().unique())
+      power = st.selectbox("Select a start date you want to see your metric",data_pricing["engine_power"].sort_values().unique())
+      submit = st.form_submit_button("submit")
+      if submit:
+              model_select = data_pricing[data_pricing["model_key"]==model]
+              power_select = data_pricing[data_pricing["power"]==power]
+              avg_rental_price = data_pricing[model_select & power_select ]["rental_price_per_day"].mean()
+              st.metric("Average rental price (in $)", np.round(avg_rental_price, 2))
+
+
+