Update app.py

app.py

@@ -39,16 +39,31 @@ model = genai.GenerativeModel(
 )
 
 def calculate_kpis(df):
-    """Calculates KPIs relevant to the given dataset."""
-
-    total_interventions = len(df)
-    interventions_by_category = df['Intervention_Category'].value_counts().to_dict()
-    interventions_by_type = df['Intervention'].value_counts().to_dict()
-    male_participants = len(df[df['Gender'] == 'Male'])
-    female_participants = len(df[df['Gender'] == 'Female'])
-    youth_participants = len(df[df['Youth'] == 'Yes'])
-    non_youth_participants = len(df[df['Youth'] == 'No'])
-
+    total_interventions = len(df) if hasattr(df, '__len__') else 0 # Check if df is a dataframe
+    try:
+        interventions_by_category = df['Intervention_Category'].value_counts().to_dict() if 'Intervention_Category' in df.columns else {}
+    except AttributeError:
+        interventions_by_category = {}
+    try:
+        interventions_by_type = df['Intervention'].value_counts().to_dict() if 'Intervention' in df.columns else {}
+    except AttributeError:
+        interventions_by_type = {}
+    try:
+        male_participants = len(df[df['Gender'] == 'Male']) if 'Gender' in df.columns else 0
+    except TypeError:
+        male_participants = 0
+    try:
+        female_participants = len(df[df['Gender'] == 'Female']) if 'Gender' in df.columns else 0
+    except TypeError:
+        female_participants = 0
+    try:
+        youth_participants = len(df[df['Youth'] == 'Yes']) if 'Youth' in df.columns else 0
+    except TypeError:
+        youth_participants = 0
+    try:
+        non_youth_participants = len(df[df['Youth'] == 'No']) if 'Youth' in df.columns else 0
+    except TypeError:
+        non_youth_participants = 0
 
     kpis = {
         "total_interventions": total_interventions,
@@ -229,7 +244,13 @@ elif tabs == 'Reports':
 
                 with st.spinner("Generating Report, Please Wait...."):
                     try:
-                        prompt = f"""You are an expert business analyst. Analyze the following data and generate a comprehensive and insightful business report, including appropriate key performance indicators and recommendations.Data:{str(calculate_kpis(filtered_df))}{str(get_pandas_profile(filtered_df))}"""
+                        prompt = f"""
+                        You are an expert business analyst. Analyze the following data and generate a comprehensive and insightful business report, 
+                        including appropriate key performance indicators and recommendations.
+                        Data: dataset:{str(filtered_df.to_json(orient='records'))},
+                        kpi:{str(calculate_kpis(filtered_df))}, dataset profile:{str(get_pandas_profile(filtered_df))}
+                        """
+                        
                         response = model.generate_content(prompt)
                         report = response.text
                         st.markdown(report)