Update app.py

app.py

@@ -2,7 +2,73 @@ import streamlit as st
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
+import numpy as np
 from io import StringIO
+import openpyxl
+
+def load_data(file):
+    file_extension = file.name.split('.')[-1].lower()
+    if file_extension == 'csv':
+        data = pd.read_csv(file)
+    elif file_extension in ['xls', 'xlsx']:
+        data = pd.read_excel(file)
+    else:
+        st.error("Unsupported file format. Please upload a CSV, XLS, or XLSX file.")
+        return None
+    return data
+
+def manual_data_entry():
+    st.subheader("Manual Data Entry")
+    col_names = st.text_input("Enter column names separated by commas:").split(',')
+    col_names = [name.strip() for name in col_names if name.strip()]
+    
+    if col_names:
+        num_rows = st.number_input("Enter number of rows:", min_value=1, value=5)
+        data = []
+        for i in range(num_rows):
+            row = []
+            for col in col_names:
+                value = st.text_input(f"Enter value for {col} (Row {i+1}):")
+                row.append(value)
+            data.append(row)
+        
+        return pd.DataFrame(data, columns=col_names)
+    return None
+
+def perform_analysis(data):
+    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:")
+    numeric_data = data.select_dtypes(include=['float64', 'int64'])
+    if not numeric_data.empty:
+        fig, ax = plt.subplots(figsize=(10, 8))
+        sns.heatmap(numeric_data.corr(), annot=True, cmap='coolwarm', ax=ax)
+        st.pyplot(fig)
+    else:
+        st.write("No numeric columns available for correlation heatmap.")
+
+    # Pairplot
+    st.write("Pairplot:")
+    if not numeric_data.empty:
+        fig = sns.pairplot(numeric_data)
+        st.pyplot(fig)
+    else:
+        st.write("No numeric columns available for pairplot.")
+
+    # Histogram
+    st.write("Histograms:")
+    for column in numeric_data.columns:
+        fig, ax = plt.subplots()
+        sns.histplot(data[column], kde=True, ax=ax)
+        st.pyplot(fig)
 
 def main():
     st.title("PPDAC Data Analysis Toolkit")
@@ -17,39 +83,29 @@ def main():
 
     # Data
     st.header("3. Data")
-    uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+    data_input_method = st.radio("Choose data input method:", ("Upload File", "Manual Entry"))
     
-    if uploaded_file is not None:
-        data = pd.read_csv(uploaded_file)
+    if data_input_method == "Upload File":
+        uploaded_file = st.file_uploader("Choose a CSV, XLS, or XLSX file", type=["csv", "xls", "xlsx"])
+        if uploaded_file is not None:
+            data = load_data(uploaded_file)
+        else:
+            data = None
+    else:
+        data = manual_data_entry()
+    
+    if data is not None:
+        st.write("Data Preview:")
         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)
+        # Convert columns to numeric where possible
+        for col in data.columns:
+            try:
+                data[col] = pd.to_numeric(data[col])
+            except ValueError:
+                pass  # Keep as non-numeric if conversion fails
 
-        # 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)
+        perform_analysis(data)
 
         # Conclusion
         st.header("5. Conclusion")