Update app.py

app.py

@@ -1,89 +1,65 @@
-import streamlit as st
 import pandas as pd
+import openai
+import streamlit as st
 import matplotlib.pyplot as plt
 
-def process_data(df):
-    # Clean and prepare data
-    df = df[['Project Category', 'Logged']].copy()
-    df = df.dropna(subset=['Project Category'])
-    
-    # Consolidate categories
-    df['Main Category'] = df['Project Category'].apply(
-        lambda x: 'Billable' if 'Billable' in str(x) else str(x).strip()
+# Analyze using OpenAI
+def get_openai_insights(api_key, prompt):
+    openai.api_key = api_key
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=prompt,
+        max_tokens=500,
+        temperature=0.5
     )
-    
-    # Group by main categories
-    main_categories = df.groupby('Main Category')['Logged'].sum().reset_index()
-    total_hours = main_categories['Logged'].sum()
-    main_categories['Percentage'] = (main_categories['Logged'] / total_hours * 100).round(1)
-    
-    # Prepare non-billable breakdown
-    non_billable = df[df['Main Category'] == 'Non-Billable']
-    non_billable_breakdown = non_billable.groupby('Project Category')['Logged'].sum().reset_index()
-    
-    return main_categories, non_billable_breakdown
+    return response["choices"][0]["text"].strip()
 
-def create_pie_chart(data):
-    fig, ax = plt.subplots(figsize=(6, 6))
-    wedges, texts, autotexts = ax.pie(
-        data['Logged'],
-        labels=data['Main Category'],
-        autopct='%1.1f%%',
-        colors=['#4CAF50', '#FFC107', '#9E9E9E'],
-        startangle=90
-    )
-    plt.setp(autotexts, size=10, weight="bold", color='white')
-    ax.set_title('Overall Utilization Distribution', pad=20)
-    return fig
+# Streamlit app
+def main():
+    st.title("Excel Data Visualization with OpenAI Insights")
 
-def create_bar_chart(data):
-    fig, ax = plt.subplots(figsize=(10, 5))
-    data.plot(kind='bar', x='Project Category', y='Logged', ax=ax, legend=False)
-    ax.set_title('Non-Billable Time Breakdown')
-    ax.set_ylabel('Hours')
-    ax.set_xlabel('')
-    plt.xticks(rotation=45, ha='right')
-    plt.tight_layout()
-    return fig
+    # Input OpenAI API Key
+    api_key = st.text_input("Enter your OpenAI API Key", type="password")
+    if not api_key:
+        st.warning("Please enter your OpenAI API key to proceed.")
+        return
 
-def main():
-    st.title('QA Team Utilization Dashboard')
-    
-    uploaded_file = st.file_uploader("Upload Timesheet Excel File", type=['xls', 'xlsx'])
-    
-    if uploaded_file:
-        try:
-            df = pd.read_excel(uploaded_file, sheet_name='Report')
-            main_cats, non_billable = process_data(df)
-            
-            # Main utilization section
-            st.header("Overall Utilization")
-            col1, col2 = st.columns([2, 1])
-            
-            with col1:
-                st.pyplot(create_pie_chart(main_cats))
-            
-            with col2:
-                st.dataframe(
-                    main_cats[['Main Category', 'Logged', 'Percentage']],
-                    hide_index=True,
-                    column_config={
-                        'Main Category': 'Category',
-                        'Logged': st.column_config.NumberColumn('Hours', format="%.2f"),
-                        'Percentage': st.column_config.NumberColumn(format="%.1f%%")
-                    }
-                )
-            
-            # Non-billable breakdown
-            st.header("Non-Billable Detailed Breakdown")
-            st.pyplot(create_bar_chart(non_billable))
-            
-            # Raw data preview
-            st.subheader("Raw Data Preview")
-            st.dataframe(df.head(10), hide_index=True)
-            
-        except Exception as e:
-            st.error(f"Error processing file: {str(e)}")
+    # File upload
+    excel_file = st.file_uploader("Upload the Excel File", type=["xls", "xlsx"])
+
+    if excel_file:
+        # Load Excel data
+        excel_data = pd.ExcelFile(excel_file)
+        st.sidebar.header("Select a Sheet to Visualize")
+        sheet_name = st.sidebar.selectbox("Sheet Name", excel_data.sheet_names)
+
+        if sheet_name:
+            data = pd.read_excel(excel_data, sheet_name=sheet_name)
+            st.subheader(f"Data from Sheet: {sheet_name}")
+            st.dataframe(data)
+
+            # Option to generate insights using OpenAI
+            st.header("Generate AI Insights")
+            if st.button("Get Insights from OpenAI"):
+                with st.spinner("Generating insights..."):
+                    try:
+                        data_sample = data.head(5).to_csv(index=False)
+                        prompt = f"Analyze the following data and provide key insights:\n\n{data_sample}"
+                        insights = get_openai_insights(api_key, prompt)
+                        st.success("AI Insights Generated!")
+                        st.text_area("AI Insights:", insights, height=200)
+                    except openai.error.OpenAIError as e:
+                        st.error(f"Error with OpenAI API: {e}")
+
+            # Visualize numeric data
+            st.header("Visualize Data")
+            numeric_cols = data.select_dtypes(include="number").columns
+            if numeric_cols.any():
+                col_to_plot = st.selectbox("Select a Column to Plot", numeric_cols)
+                if col_to_plot:
+                    fig, ax = plt.subplots()
+                    data[col_to_plot].plot(kind="bar", ax=ax, title=f"{col_to_plot} Analysis")
+                    st.pyplot(fig)
 
 if __name__ == "__main__":
-    main()
+    main()