Update app.py

app.py

@@ -8,14 +8,47 @@ from reportlab.pdfgen import canvas
 
 REQUIRED_COLUMNS = ['DeviceID', 'Lab', 'Type', 'Timestamp', 'Status', 'UsageCount']
 
+# ----------------------------
+# Helper to extract file content correctly for HF Spaces and local
+# ----------------------------
+def get_file_content(file_obj):
+    if file_obj is None:
+        return None
+
+    if hasattr(file_obj, "read"):
+        content = file_obj.read()
+        if isinstance(content, bytes):
+            return content
+        else:
+            return content.encode('utf-8')
+
+    if isinstance(file_obj, dict) and 'data' in file_obj:
+        data = file_obj['data']
+        if isinstance(data, str):
+            import base64
+            if data.startswith("data:"):
+                data = data.split(",")[1]
+            return base64.b64decode(data)
+        elif isinstance(data, bytes):
+            return data
+        else:
+            raise ValueError("Unexpected data format in file upload dict")
+
+    if isinstance(file_obj, str):
+        with open(file_obj, "rb") as f:
+            return f.read()
+
+    raise ValueError("Unsupported file object format")
+
 # ----------------------------
 # Load Logs Safely
 # ----------------------------
 def load_logs(file_obj):
     try:
         if file_obj is not None:
-            content = file_obj.read().decode('utf-8')
-            df = pd.read_csv(io.StringIO(content))
+            content_bytes = get_file_content(file_obj)
+            content_str = content_bytes.decode('utf-8')
+            df = pd.read_csv(io.StringIO(content_str))
             if not all(col in df.columns for col in REQUIRED_COLUMNS):
                 raise ValueError(f"CSV must contain columns: {', '.join(REQUIRED_COLUMNS)}")
             df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')
@@ -51,7 +84,6 @@ def filter_df(df, selected_lab, selected_type):
 # ----------------------------
 def summarize_logs(df):
     if df.empty:
-        # Return empty DataFrame with expected columns for plotting
         return pd.DataFrame(columns=['OK', 'DOWN'])
     summary = df.groupby(['Lab', 'Type'])['Status'].value_counts().unstack().fillna(0)
     return summary
@@ -122,7 +154,6 @@ def update_dropdown_options(file_obj):
         df = load_logs(file_obj)
         labs = sorted(df['Lab'].dropna().unique().tolist())
         types = sorted(df['Type'].dropna().unique().tolist())
-        # Add "All" option for no filtering
         labs = ["All"] + labs
         types = ["All"] + types
         return labs, types, gr.update(visible=True)
@@ -136,7 +167,7 @@ def dashboard(file_obj, selected_lab, selected_type):
     try:
         df = load_logs(file_obj)
         fig = generate_chart(df, selected_lab, selected_type)
-        return fig, gr.update(visible=False)  # Hide error
+        return fig, gr.update(visible=False)
     except Exception as e:
         return None, gr.update(value=f"❌ Error: {e}", visible=True)