add app from alex

.python-version

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+3.12

.streamlit/config.toml

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+[theme]
+base = "light"

app.py

@@ -1,4 +1,73 @@
+import matplotlib.pyplot as plt
+import pandas as pd
 import streamlit as st
 
-x = st.slider("Select a value")
-st.write(x, "squared is", x * x)
+
+def load_data(year):
+    """Load data from a CSV file for the given year."""
+    try:
+        data = pd.read_csv(f"validation/{year}.csv")
+        return data
+    except FileNotFoundError:
+        st.error(f"No data found for year {year}. Please ensure the file exists.")
+        return None
+
+def filter_data(data, country, brand):
+    """Filter the data for the selected country and brand."""
+    return data[(data['country'] == country) & (data['brand'] == brand)]
+
+def plot_data(filtered_data):
+    """Plot target vs. date with a confidence interval."""
+    if filtered_data.empty:
+        st.warning("No data available for the selected criteria.")
+        return
+    
+    st.write("Plotting target vs date with confidence intervals.")
+    dates = pd.to_datetime(filtered_data['date'])
+    target = filtered_data['target']
+    prediction = filtered_data['prediction']
+    prediction_10 = filtered_data['prediction_10']
+    prediction_90 = filtered_data['prediction_90']
+
+    plt.figure(figsize=(12, 6))
+    
+    # Plot the target
+    plt.plot(dates, target, label='Target', color='blue')
+
+    # Plot the prediction with confidence interval
+    plt.plot(dates, prediction, label='Prediction', color='orange')
+    plt.fill_between(dates, prediction_10, prediction_90, color='orange', alpha=0.2, label='Confidence Interval (10th to 90th percentile)')
+    
+    plt.xlabel('Date')
+    plt.ylabel('Target')
+    plt.title('Target vs Date with Confidence Interval')
+    plt.legend()
+    plt.grid(True)
+    st.pyplot(plt)
+
+def main():
+    st.title("Data Visualization App")
+
+    # Step 1: Select Year
+    year = st.sidebar.selectbox("Select Year", range(2017, 2022))
+    
+    # Load data based on year selection
+    data = load_data(year)
+    
+    if data is not None:
+        # Step 2: Select Country based on available options for the year
+        available_countries = data['country'].unique()
+        country = st.sidebar.selectbox("Select Country", available_countries)
+        
+        # Step 3: Select Brand based on available options for the year and country
+        available_brands = data[data['country'] == country]['brand'].unique()
+        brand = st.sidebar.selectbox("Select Brand", available_brands)
+        
+        # Filter data based on inputs
+        filtered_data = filter_data(data, country, brand)
+
+        # Plot the data
+        plot_data(filtered_data)
+
+if __name__ == "__main__":
+    main()

hello.py

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+def main():
+    print("Hello from novartis!")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

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+[project]
+name = "novartis"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = []

requirements.txt

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+altair==5.5.0
+attrs==24.2.0
+blinker==1.9.0
+cachetools==5.5.0
+certifi==2024.8.30
+charset-normalizer==3.4.0
+click==8.1.7
+contourpy==1.3.1
+cycler==0.12.1
+fonttools==4.55.0
+gitdb==4.0.11
+gitpython==3.1.43
+idna==3.10
+jinja2==3.1.4
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+kiwisolver==1.4.7
+markdown-it-py==3.0.0
+markupsafe==3.0.2
+matplotlib==3.9.3
+mdurl==0.1.2
+narwhals==1.15.0
+numpy==2.1.3
+packaging==24.2
+pandas==2.2.3
+pillow==11.0.0
+protobuf==5.29.0
+pyarrow==18.1.0
+pydeck==0.9.1
+pygments==2.18.0
+pyparsing==3.2.0
+python-dateutil==2.9.0.post0
+pytz==2024.2
+referencing==0.35.1
+requests==2.32.3
+rich==13.9.4
+rpds-py==0.21.0
+six==1.16.0
+smmap==5.0.1
+streamlit==1.40.2
+tenacity==9.0.0
+toml==0.10.2
+tornado==6.4.2
+typing-extensions==4.12.2
+tzdata==2024.2
+urllib3==2.2.3