Create app.py

app.py

@@ -0,0 +1,65 @@
+# app.py
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+def load_data(file):
+    try:
+        df = pd.read_csv(file)
+        return df
+    except Exception as e:
+        st.error(f"Error loading file: {e}")
+        return None
+
+def generate_summary(df):
+    summary = {
+        'Column': df.columns,
+        'Data Type': [str(df[col].dtype) for col in df.columns],
+        'Non-Null Count': df.notnull().sum().values,
+        'Unique Values': [df[col].nunique() for col in df.columns],
+        'Sample Value': [df[col].iloc[0] if len(df[col]) > 0 else None for col in df.columns]
+    }
+    return pd.DataFrame(summary)
+
+def generate_insights(df):
+    insights = []
+
+    # Example insights
+    if 'avg_training_score' in df.columns:
+        avg_score = df['avg_training_score'].mean()
+        insights.append(f"The average training score is {avg_score:.2f}. Consider additional training for employees below this score.")
+
+    if 'length_of_service' in df.columns:
+        experienced_employees = len(df[df['length_of_service'] > 5])
+        insights.append(f"{experienced_employees} employees have more than 5 years of service. Consider them for leadership roles.")
+
+    if 'awards_won' in df.columns:
+        award_winners = df['awards_won'].sum()
+        insights.append(f"A total of {award_winners} awards have been won by employees.")
+
+    return insights
+
+# Streamlit app
+st.title("Employee Performance Dashboard")
+st.markdown("Upload your cleaned dataset to generate insights and suggestions.")
+
+# File upload
+uploaded_file = st.file_uploader("Upload CSV File", type="csv")
+
+if uploaded_file is not None:
+    df = load_data(uploaded_file)
+
+    if df is not None:
+        st.markdown("### Dataset Preview")
+        st.dataframe(df.head())
+
+        st.markdown("### Dataset Summary")
+        summary = generate_summary(df)
+        st.dataframe(summary)
+
+        st.markdown("### Insights and Suggestions")
+        insights = generate_insights(df)
+        for insight in insights:
+            st.write(f"- {insight}")
+else:
+    st.info("Please upload a CSV file.")