working prototype added

Author: mijanr

main.py

@@ -0,0 +1,18 @@
+# main.py
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+
+app = FastAPI()
+
+# Allow CORS for all origins for development purposes
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Mount the streamlit app as a static file
+app.mount("/streamlit", StaticFiles(directory="src", html=True), name="streamlit")

src/app.py

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+# src/app.py
+import streamlit as st
+import pandas as pd
+from fastapi import HTTPException
+
+# Load the Iris dataset
+@st.cache_data
+def load_data():
+    try:
+        df = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data", header=None)
+        df.columns = ["sepal_length", "sepal_width", "petal_length", "petal_width", "class"]
+        return df
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error loading data: {str(e)}")
+
+df = load_data()
+
+# Streamlit app
+def main():
+    st.title("Iris Dataset EDA")
+
+    # Create buttons in the sidebar for each page
+    raw_data_button = st.sidebar.button("Raw Data")
+    summary_stats_button = st.sidebar.button("Summary Statistics")
+    class_distribution_button = st.sidebar.button("Class Distribution")
+
+    # Display content based on clicked button
+    if raw_data_button:
+        st.subheader("Raw Dataset")
+        st.write(df)
+
+    if summary_stats_button:
+        st.subheader("Summary Statistics")
+        st.write(df.describe())
+
+    if class_distribution_button:
+        st.subheader("Class Distribution")
+        class_counts = df["class"].value_counts()
+        st.bar_chart(class_counts)
+
+if __name__ == "__main__":
+    main()