First commit

Dockerfile

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+# Use a lightweight Python image as the base
+FROM python:3.9-slim
+
+# Set the working directory
+WORKDIR /app
+
+# Copy the project files to the container
+COPY . /app
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose Streamlit's default port
+EXPOSE 8501
+
+# Command to run the Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

app.py

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+import streamlit as st
+import requests
+import pandas as pd
+
+# Function to get data from FastAPI
+def get_continent_population(continent_name):
+    url = f"https://kabila22-backend.hf.space"
+    response = requests.get(url)
+    if response.status_code == 200:
+        return response.json()
+    else:
+        st.error("Failed to retrieve data")
+        return None
+
+# List of continents
+continents = ["Africa", "Asia", "Europe", "North America", "Oceania", "South America"]
+
+# Create Streamlit dropdown box
+st.title("World Population by Continent")
+selected_continent = st.selectbox("Select a continent:", continents)
+
+# Get data for selected continent
+continent_data = get_continent_population(selected_continent)
+
+if continent_data:
+    # Display population data in a table
+    st.subheader(f"Population Data for {selected_continent}")
+
+    # Ensure all values are present and convert them to numeric if necessary
+    population_data = {
+        "Data": ["Total Population", "Total Area", "Population Density", "Max Population", "Min Population", "Avg Population"],
+        "Result": [
+            continent_data.get("total_population", 0),
+            continent_data.get("total_area_km2", 0),
+            continent_data.get("continent_population_density", 0),
+            continent_data["max_population"].get("population", 0),
+            continent_data["min_population"].get("population", 0),
+            continent_data.get("avg_population", 0)
+        ],
+        "Country": [
+            "",
+            "",
+            "",
+            continent_data["max_population"]["country"],
+            continent_data["min_population"]["country"],
+            ""
+        ]
+    }
+
+    df = pd.DataFrame(population_data)
+
+    # Display the data in a table
+    st.table(df)

requirements.txt

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+streamlit
+requests
+pandas