diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/func_df_2.py b/apps/streamlit_ds_chat/cloud_coiled_io/func_df_2.py
new file mode 100644
index 0000000..3e4602e
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/func_df_2.py
@@ -0,0 +1,2 @@
+def run(df):
+    return df.min().compute()
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/func_global_local.py b/apps/streamlit_ds_chat/cloud_coiled_io/func_global_local.py
new file mode 100644
index 0000000..801bf5c
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/func_global_local.py
@@ -0,0 +1,2 @@
+def run(_):
+    return {"globals": globals(), "locals": locals()}
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/func_pdf.py b/apps/streamlit_ds_chat/cloud_coiled_io/func_pdf.py
new file mode 100644
index 0000000..14e1976
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/func_pdf.py
@@ -0,0 +1,2 @@
+def run(df):
+    return df.mean()
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/working_new_file_upload_cloud_execution.py b/apps/streamlit_ds_chat/cloud_coiled_io/working_new_file_upload_cloud_execution.py
new file mode 100644
index 0000000..772bb7f
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/working_new_file_upload_cloud_execution.py
@@ -0,0 +1,68 @@
+import coiled
+import importlib
+import os.path
+from dask.distributed import Client
+
+
+import numpy as np
+import dask.dataframe as dd
+import pandas as pd
+from rich.pretty import pprint
+
+pdf = pd.DataFrame(
+    {
+        "A": np.random.rand(10000),
+        "B": np.random.rand(10000),
+        "C": np.random.rand(10000),
+        "D": np.random.rand(10000),
+    }
+)
+df = dd.from_pandas(pdf, npartitions=4)
+
+print(f"Local result: {df.mean().compute()}")
+
+
+# Step 1: Create or connect to a Coiled cluster
+cluster = coiled.Cluster(name="my-cluster", n_workers=1, idle_timeout="20 minutes")
+# n_workers – Number of workers in this cluster.
+# Can either be an integer for a static number of workers,
+# or a list specifying the lower and upper bounds for adaptively scaling up/ down workers
+# depending on the amount of work submitted.
+# Defaults to n_workers=[4, 20] which adaptively scales between 4 and 20 workers.
+client = Client(cluster)
+
+for i in range(10):
+    file_name = input("file name?")
+    print("go")
+
+    if not file_name.endswith(".py"):
+        raise Exception("not a file")
+
+    if not os.path.exists(file_name):
+        raise Exception(f"no file name: {file_name}")
+
+    # Step 2: Upload the module file to all workers
+    client.upload_file(file_name)
+
+    # Step 3: Verify the file upload
+    def check_file(filename):
+        return os.path.exists(filename)
+
+    print(client.run(check_file, file_name))  # Should print True on all workers
+
+    # Step 4: Use the uploaded module in a distributed task
+    def use_uploaded_module():
+        try:
+            module = importlib.import_module(file_name[:-3])
+            print(f"Successfully imported {file_name}")
+        except ImportError as e:
+            print(f"Error importing {file_name}: {e}")
+            return None
+
+        return module.run(
+            pdf
+        )  # so, with proper function you can pass even Pandas Data Frames directly
+
+    result = client.run(use_uploaded_module)
+    print(type(result))
+    pprint(result)