Upload folder using huggingface_hub

.amlignore

@@ -0,0 +1,6 @@
+## This file was auto generated by the Azure Machine Learning Studio. Please do not remove. 
+## Read more about the .amlignore file here: https://docs.microsoft.com/azure/machine-learning/how-to-save-write-experiment-files#storage-limits-of-experiment-snapshots
+        
+.ipynb_aml_checkpoints/ 
+*.amltmp 
+*.amltemp

Dockerfile

@@ -1,15 +1,41 @@
-FROM python:3.13.5-slim
+FROM python:3.9-slim
 
-WORKDIR /app
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+
+
+# Run apt-get update and install as root
+USER root
 
 RUN apt-get update && apt-get install -y \
     build-essential \
+
     curl \
+
+    software-properties-common \
+
     git \
+
     && rm -rf /var/lib/apt/lists/*
 
-COPY requirements.txt ./
-COPY src/ ./src/
+USER user
+
+
+# COPY requirements.txt ./
+
+# COPY src/ ./src/
+
+COPY . .
 
 RUN pip3 install -r requirements.txt
 
@@ -17,4 +43,4 @@ EXPOSE 8501
 
 HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

app.py

@@ -0,0 +1,68 @@
+import streamlit as st
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Load data
+def load_data():
+    df = pd.read_csv("processed_data.csv")  # replace with your dataset
+    return df
+
+# Create Streamlit app
+def app():
+    # Title for the app
+    st.title("Pizza Sales Data Analysis Dashboard")
+    df = load_data()
+
+    df = pd.DataFrame(df)
+
+    # Calculate key metrics
+    total_orders = df['order_id'].nunique()
+    total_revenue = df['total_price'].sum()
+    most_popular_size = df['pizza_size'].value_counts().idxmax()
+    most_frequent_category = df['pizza_category'].value_counts().idxmax()
+    total_pizzas_sold = df['quantity'].sum()
+    repeat_customers = df.groupby('order_id').size().value_counts().get(2, 0)
+
+    # Sidebar with key metrics
+    st.sidebar.header("Key Metrics")
+    st.sidebar.metric("Total Orders", total_orders)
+    st.sidebar.metric("Total Revenue", f"${total_revenue:,.2f}")
+    st.sidebar.metric("Most Popular Size", most_popular_size)
+    st.sidebar.metric("Most Popular Category", most_frequent_category)
+    st.sidebar.metric("Total Pizzas Sold", total_pizzas_sold)
+
+    plots = [
+        {"title": "Top Selling Pizzas (by Quantity)", "x": "pizza_name", "y": "quantity", "top": 5},
+        {"title": "Quantity of Pizzas Sold by Category and Time of the Day", "x": "time_of_day", "hue": "pizza_category"},
+        {"title": "Quantity of Pizzas Sold by Size and Time of the Day", "x": "time_of_day", "hue": "pizza_size"},
+        {"title": "Monthly Revenue Trends by Pizza Category", "x": "order_month", "y": "total_price", "hue": "pizza_category", "estimator": "sum", "marker": "o"},
+    ]
+
+    for plot in plots:
+      st.header(plot["title"])
+      
+      fig, ax = plt.subplots()
+      
+      if "Top Selling Pizzas" in plot["title"]:
+        data_aux = df.groupby(plot["x"])[plot["y"]].sum().reset_index().sort_values(by=plot["y"], ascending=False).head(plot["top"])
+        ax.bar(data_aux[plot["x"]].values.tolist(), data_aux[plot["y"]].values.tolist())
+      
+      if "Quantity of Pizzas" in plot["title"]:
+        sns.countplot(data=df, x=plot["x"], hue=plot["hue"], ax=ax)
+      
+      if "Monthly Revenue" in plot["title"]:
+        sns.lineplot(data=df, x=plot["x"], y=plot["y"], hue=plot["hue"], estimator=plot["estimator"], errorbar=None, marker=plot["marker"], ax=ax)
+
+      ax.set_xlabel(" ".join(plot["x"].split("_")).capitalize())
+      if "y" in plot.keys():
+        ax.set_ylabel(" ".join(plot["y"].split("_")).capitalize())
+      else:
+        ax.set_ylabel("Quantity")
+      ax.legend(bbox_to_anchor=(1,1))
+
+      st.pyplot(fig)
+    
+
+if __name__ == "__main__":
+    app()

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+pandas==1.5.2
+matplotlib==3.6.2
+seaborn==0.12.1
+scipy==1.10.0
+numpy==1.23.5
+streamlit==1.46.0