Create app.py

app.py

@@ -0,0 +1,59 @@
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from io import StringIO
+
+def main():
+    st.title("PPDAC Data Analysis Toolkit")
+
+    # Problem
+    st.header("1. Problem")
+    problem = st.text_area("Define your problem:")
+
+    # Plan
+    st.header("2. Plan")
+    plan = st.text_area("Describe your plan:")
+
+    # Data
+    st.header("3. Data")
+    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    
+    if uploaded_file is not None:
+        data = pd.read_csv(uploaded_file)
+        st.write(data.head())
+
+        # Analysis
+        st.header("4. Analysis")
+        
+        # EDA
+        st.subheader("Exploratory Data Analysis")
+        
+        # Summary statistics
+        st.write("Summary Statistics:")
+        st.write(data.describe())
+
+        # Correlation heatmap
+        st.write("Correlation Heatmap:")
+        fig, ax = plt.subplots(figsize=(10, 8))
+        sns.heatmap(data.corr(), annot=True, cmap='coolwarm', ax=ax)
+        st.pyplot(fig)
+
+        # Pairplot
+        st.write("Pairplot:")
+        fig = sns.pairplot(data)
+        st.pyplot(fig)
+
+        # Histogram
+        st.write("Histograms:")
+        for column in data.select_dtypes(include=['float64', 'int64']).columns:
+            fig, ax = plt.subplots()
+            sns.histplot(data[column], kde=True, ax=ax)
+            st.pyplot(fig)
+
+        # Conclusion
+        st.header("5. Conclusion")
+        conclusion = st.text_area("Write your conclusion based on the analysis:")
+
+if __name__ == "__main__":
+    main()