Update app.py

app.py

@@ -134,7 +134,7 @@ def is_healthcare_scenario(text: str, uploaded_files_paths) -> bool:
     has_healthcare_keywords = any(keyword in t for keyword in HEALTHCARE_SETTINGS["healthcare_keywords"])
     
     # Check for healthcare facility types
-    has_facility_types = any(
+    has_facility_types = (
         any(ftype in t for ftype in ["hospital", "medical center", "health centre"]) or
         any(ftype in t for ftype in ["nursing", "residential", "care facility", "long-term care"]) or
         any(ftype in t for ftype in ["ambulatory", "clinic", "surgery center", "outpatient"])
@@ -196,6 +196,10 @@ def process_healthcare_data(uploaded_files_paths, data_registry):
 def analyze_facility_distribution(facilities_df):
     """Analyze healthcare facility distribution dynamically."""
     try:
+        # Validate input
+        if facilities_df is None or facilities_df.empty:
+            return {"error": "No facility data provided"}
+        
         # Find province column
         province_col = find_column(facilities_df, ['province', 'state', 'territory'])
         if province_col:
@@ -245,6 +249,10 @@ def analyze_facility_distribution(facilities_df):
 def analyze_bed_capacity(beds_df):
     """Analyze bed capacity dynamically."""
     try:
+        # Validate input
+        if beds_df is None or beds_df.empty:
+            return {"error": "No bed data provided"}
+        
         # Find required columns
         current_col = find_column(beds_df, ['current', '2023', '2024', 'beds_current', 'staffed_beds', 'capacity'])
         prev_col = find_column(beds_df, ['prev', 'previous', '2022', 'beds_prev', 'previous_beds'])
@@ -322,6 +330,10 @@ def analyze_bed_capacity(beds_df):
 def assess_long_term_capacity(facilities_df, beds_df, zone_name):
     """Assess long-term care capacity dynamically."""
     try:
+        # Validate inputs
+        if facilities_df is None or facilities_df.empty:
+            return {"error": "No facility data provided"}
+        
         # Find relevant columns
         zone_col = find_column(facilities_df, ['zone', 'region', 'area', 'district'])
         city_col = find_column(facilities_df, ['city', 'municipality', 'town'])
@@ -444,13 +456,13 @@ def format_healthcare_analysis_response(scenario_text, results, recommendations,
             response += "## 1. Data Preparation\n\n"
             response += f"Total healthcare facilities: {fd.get('total_facilities', 'N/A')}\n\n"
             
-            if 'type_distribution' in fd:
+            if 'type_distribution' in fd and isinstance(fd['type_distribution'], dict):
                 response += "### Facility Type Distribution\n\n"
                 for ftype, count in fd['type_distribution'].items():
                     response += f"- {ftype}: {count}\n"
                 response += "\n"
             
-            if 'city_breakdown' in fd:
+            if 'city_breakdown' in fd and isinstance(fd['city_breakdown'], dict):
                 response += "### Top Cities by Facility Count\n\n"
                 response += "| City | Hospitals | Nursing/Residential | Ambulatory | Total |\n"
                 response += "|------|-----------|-------------------|------------|-------|\n"
@@ -529,7 +541,7 @@ def format_healthcare_analysis_response(scenario_text, results, recommendations,
             response += f"In {zone} Zone, the major city is {city} with a nursing/residential to hospital ratio of {ratio:.2f}.\n\n"
             response += f"Long-term care capacity appears **{assessment}** in {city}.\n\n"
             
-            if 'facility_counts' in ltc:
+            if 'facility_counts' in ltc and isinstance(ltc['facility_counts'], dict):
                 response += "### Facility Counts\n\n"
                 for ftype, count in ltc['facility_counts'].items():
                     response += f"- {ftype}: {count}\n"