diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..25760b3
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
index 32d5d7a..d1114d8 100644
--- a/.idea/modules.xml
+++ b/.idea/modules.xml
@@ -2,7 +2,10 @@
+
+
+
\ No newline at end of file
diff --git a/.idea/pet_project.iml b/.idea/pet_project.iml
index 8794792..7c0c2d7 100644
--- a/.idea/pet_project.iml
+++ b/.idea/pet_project.iml
@@ -1,12 +1,21 @@
-
+
-
-
-
+
+
-
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
index 35eb1dd..856008c 100644
--- a/.idea/vcs.xml
+++ b/.idea/vcs.xml
@@ -2,5 +2,7 @@
+
+
\ No newline at end of file
diff --git a/apps/__init__.py b/apps/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/apps/ds_chat_backend/__init__.py b/apps/ds_chat_backend/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/apps/ds_chat_backend/cli.py b/apps/ds_chat_backend/cli.py
new file mode 100644
index 0000000..6c8a860
--- /dev/null
+++ b/apps/ds_chat_backend/cli.py
@@ -0,0 +1,62 @@
+# Copyright (c) 2024. Sergii Nechuiviter
+import requests
+
+
+def print_help():
+ print("You can type your message and press Enter to send it.")
+ print("")
+ print("If you start your message with '`' character, you can use some commands.")
+ print("You can type '`h' and press Enter to see the help.")
+ print("You can type '`q' and press Enter to quit the application.")
+ print("You can try to enter '`' and your command in natural language for other system commands.")
+ print("")
+
+
+def process_user_message(message: str):
+ return (f'You said: {message}\n'
+ f' Ok...')
+
+
+def process_command(message: str):
+ return (f'Command: {message}\n'
+ f' Command is not supported yet.')
+
+
+if __name__ == "__main__":
+ # Print invitation message
+ print("Hello, this is a simple chat application.")
+ print("")
+ print_help()
+
+ # Main processing loop
+ while True:
+ # Get user input
+ user_input = input("You: ")
+
+ # Check if user wants to quit
+ if user_input == '`q':
+ break
+
+ # Check if user wants to see help
+ if user_input == "`h":
+ print_help()
+ continue
+
+ try:
+ # Check if user wants to send a command
+ if user_input.startswith("`"):
+ # Send the command to the server
+ response = process_command(user_input)
+ else:
+ # send message to the server
+ response = process_user_message(user_input)
+ except APIException as e:
+ print(e)
+
+ print(response)
+
+ continue
+
+ # Print a final message
+ print("Thank you for using the chat application.")
+ print("Goodbye!")
diff --git a/apps/streamlit_ds_chat/.gitignore b/apps/streamlit_ds_chat/.gitignore
new file mode 100644
index 0000000..cc82e12
--- /dev/null
+++ b/apps/streamlit_ds_chat/.gitignore
@@ -0,0 +1,3 @@
+.streamlit/secrets.toml
+env/
+env12/
diff --git a/apps/streamlit_ds_chat/.idea/inspectionProfiles/profiles_settings.xml b/apps/streamlit_ds_chat/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/apps/streamlit_ds_chat/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/apps/streamlit_ds_chat/.idea/modules.xml b/apps/streamlit_ds_chat/.idea/modules.xml
new file mode 100644
index 0000000..aaa8f08
--- /dev/null
+++ b/apps/streamlit_ds_chat/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/apps/streamlit_ds_chat/.idea/streamlit_ds_chat.iml b/apps/streamlit_ds_chat/.idea/streamlit_ds_chat.iml
new file mode 100644
index 0000000..530c62e
--- /dev/null
+++ b/apps/streamlit_ds_chat/.idea/streamlit_ds_chat.iml
@@ -0,0 +1,11 @@
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/apps/streamlit_ds_chat/.streamlit/secrets.toml.example b/apps/streamlit_ds_chat/.streamlit/secrets.toml.example
new file mode 100644
index 0000000..a8d559a
--- /dev/null
+++ b/apps/streamlit_ds_chat/.streamlit/secrets.toml.example
@@ -0,0 +1,2 @@
+# here is an example file with secrets
+# the real file must not be commited to git
\ No newline at end of file
diff --git a/apps/streamlit_ds_chat/README_00_ds_chat_app.md b/apps/streamlit_ds_chat/README_00_ds_chat_app.md
new file mode 100644
index 0000000..e69de29
diff --git a/apps/streamlit_ds_chat/README_01_Streamlit_app.md b/apps/streamlit_ds_chat/README_01_Streamlit_app.md
new file mode 100644
index 0000000..e69de29
diff --git a/apps/streamlit_ds_chat/README_02_Vertex_AI.md b/apps/streamlit_ds_chat/README_02_Vertex_AI.md
new file mode 100644
index 0000000..e69de29
diff --git a/apps/streamlit_ds_chat/README_03_Remote_cloud_execution.md b/apps/streamlit_ds_chat/README_03_Remote_cloud_execution.md
new file mode 100644
index 0000000..e69de29
diff --git a/apps/streamlit_ds_chat/README_04_vertex_ai_codegen_streamlit.md b/apps/streamlit_ds_chat/README_04_vertex_ai_codegen_streamlit.md
new file mode 100644
index 0000000..16310fb
--- /dev/null
+++ b/apps/streamlit_ds_chat/README_04_vertex_ai_codegen_streamlit.md
@@ -0,0 +1,68 @@
+
+
+For testing manager, the following questions are used:
+
+How many unique fruits are there in the dataset?
+How many fruits likes each person?
+Make a chart which shows, how many fruits likes each person?
+How many different kinds of fruit each person likes?
+
+### Options from VertexAI itself
+What is the average number of fruits liked?
+Who likes the most number of fruits?
+Show the distribution of fruits liked by people.
+
+## Example output to the last question:
+
+### Attention! The response includes \```python phrase in the beginning!
+
+```python
+import pandas as pd
+from bokeh.plotting import figure
+from bokeh.io import show
+
+def run(data):
+ """
+ This function analyzes the provided data and returns a descriptive summary.
+
+ Args:
+ data (dict): A dictionary containing the data to be analyzed.
+ It should have the same keys as and corresponding values.
+
+ Returns:
+ dict: A dictionary containing the results of the analysis.
+ 'results' key contains the descriptive summary of the dataset.
+ 'chart' key contains the plot of the distribution of fruit likes per person.
+ """
+
+ df = data['df_table'] # Access the DataFrame from the data dictionary
+
+ # Group by person and count likes
+ person_likes = df.groupby('person')['likes'].sum()
+
+ # Create a Bokeh figure
+ p = figure(title="Distribution of Fruit Likes per Person",
+ x_axis_label="Person", y_axis_label="Number of Likes")
+
+ # Add a bar chart to the figure
+ p.vbar(x=person_likes.index, top=person_likes.values, width=0.5)
+
+ # Show the chart
+ show(p)
+
+ # Return the results and chart
+ return {
+ 'results': None, # No specific results in this case
+ 'chart': {
+ 'chart_type': 'vbar',
+ 'kwargs': {
+ 'x': person_likes.index,
+ 'top': person_likes.values,
+ 'width': 0.5,
+ 'title': "Distribution of Fruit Likes per Person",
+ 'x_axis_label': "Person",
+ 'y_axis_label': "Number of Likes"
+ }
+ }
+ }
+```
diff --git a/apps/streamlit_ds_chat/README_05_putting_all_togather.md b/apps/streamlit_ds_chat/README_05_putting_all_togather.md
new file mode 100644
index 0000000..0f038ae
--- /dev/null
+++ b/apps/streamlit_ds_chat/README_05_putting_all_togather.md
@@ -0,0 +1,28 @@
+# Step 5. Put all components together
+
+Now I have code for:
+- running Streamlit.app
+- uploading sample dataset
+- basic chat-like streamlit application
+- generating code in response to the user request
+- means for remote code execution
+- preformatted params to draw charts with Bokeh
+ (Bokeh exists for >10 years, Streamlit is quite young.
+ Probably, this is the reason why Gemini may generate Bokeh, but not Streamlit.chart code)
+
+It is time now to put all components togather.
+
+Example requests:
+- Draw number of likes for people and fruits
+
+conda create -p ./env12 -c conda-forge coiled python=3.12 "numpy<2.0.0" streamlit google-cloud-aiplatform dask
+
+
+Known issues:
+- Bokeh is not needed for workers but it is still provided.
+- StreamlitAPIException: Streamlit only supports Bokeh version 2.4.3, but you have version 3.5.0 installed. Please run `pip install --force-reinstall --no-deps bokeh==2.4.3` to install the correct version.
+- start of coiled cluster takes 1-2 minutes, but this will be the same for any cluster start on any tool
+- hard to acquire good code from code generation; it usually works, but the return value is not exactly what was requested
+ This complicates diagram generation on the client side.
+- cluster disconnects and killed after set period of time, no reconnection implemented
+- TODO needed some way to clear all interface
\ No newline at end of file
diff --git a/apps/streamlit_ds_chat/__init__.py b/apps/streamlit_ds_chat/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/apps/streamlit_ds_chat/chat_with_vertex_ai.py b/apps/streamlit_ds_chat/chat_with_vertex_ai.py
new file mode 100644
index 0000000..0fac983
--- /dev/null
+++ b/apps/streamlit_ds_chat/chat_with_vertex_ai.py
@@ -0,0 +1,100 @@
+import streamlit as st
+
+import vertexai
+from vertexai.generative_models import GenerativeModel
+import vertexai.preview.generative_models as generative_models
+from google.oauth2.service_account import Credentials
+
+from datetime import datetime
+
+
+st.title("Stub of DS chat with Google Vertex AI")
+
+########################################################################
+# init chat model
+
+chat = None
+
+if "vertex_ai_model" not in st.session_state or chat is None:
+ st.session_state["vertex_ai_model"] = "gemini-1.5-flash-001"
+
+ credentials = Credentials.from_service_account_info(st.secrets["gcs_connections"])
+ vertexai.init(
+ project="pivotal-cable-428219-c5",
+ location="us-central1",
+ credentials=credentials,
+ )
+ model = GenerativeModel(
+ st.session_state[
+ "vertex_ai_model"
+ ], # by default it will be "gemini-1.5-flash-001",
+ system_instruction=[
+ "You are an expert python engineer with data scientist background."
+ "If the user's question needs code generation, respond with: "
+ "If the user's question needs drawing a chart, respond with: "
+ ],
+ )
+ chat = model.start_chat()
+
+generation_config = {
+ "max_output_tokens": 8192,
+ "temperature": 1,
+ "top_p": 0.95,
+}
+
+safety_settings = {
+ generative_models.HarmCategory.HARM_CATEGORY_HATE_SPEECH: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+ generative_models.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+ generative_models.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+ generative_models.HarmCategory.HARM_CATEGORY_HARASSMENT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+}
+
+#####################################
+
+
+if "messages" not in st.session_state:
+ st.session_state.messages = []
+
+for message in st.session_state.messages:
+ with st.chat_message(message["role"]):
+ st.markdown(message["content"])
+
+# streaming is not working with streamlit, exceptions inside `vertexai.generative_models import GenerativeModel`
+USE_STREAMING = False
+
+if prompt := st.chat_input("What is up?"):
+ st.session_state.messages.append({"role": "user", "content": prompt})
+ with st.chat_message("user"):
+ st.markdown(prompt)
+
+ with st.chat_message("assistant"):
+ if USE_STREAMING:
+ api_response_stream = chat.send_message(
+ [prompt],
+ generation_config=generation_config,
+ safety_settings=safety_settings,
+ stream=True,
+ )
+
+ def stream_data():
+ for api_response in api_response_stream:
+ chunk = api_response.candidates[0].content.parts[0]._raw_part.text
+ print(f"{datetime.now()}: {chunk}")
+ yield chunk
+
+ response = st.write_stream(stream_data)
+ else:
+ with st.spinner(
+ "Wait for the whole response (streaming not working with Streamlit)..."
+ ):
+ api_response = chat.send_message(
+ [prompt],
+ generation_config=generation_config,
+ safety_settings=safety_settings,
+ stream=False,
+ )
+ response = api_response.candidates[0].content.parts[0]._raw_part.text
+ st.write(response)
+
+ print(("response:", response))
+ st.session_state.messages.append({"role": "assistant", "content": response})
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/README.md b/apps/streamlit_ds_chat/cloud_coiled_io/README.md
new file mode 100644
index 0000000..913033c
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/README.md
@@ -0,0 +1,114 @@
+
+https://www.coiled.io/
+
+Absolutely fascinating service and library which manage cloud for python computations.
+
+```
+Code locally, run at scale
+Coiled creates ephemeral VMs that match your local environment exactly, spinning up clones in about a minute, copying ...
+
+Your code
+Your software libraries
+Your working files
+Your cloud credentials (securely)
+```
+
+Works with AWS, GCloud, Azure, etc.
+Runs Dask and pure Python.
+
+
+# How to install Coiled
+
+Just register on `coiled.io` and follow instructions. Everything is very simple and works great.
+
+The only pitfall: all you installs should probably come from single distribution system.
+If you use conda, you should install everything with `conda install`.
+If you use `pip`, you should install all packages with `pip install`.
+
+
+```bash
+pip3 install coiled "dask[complete]"
+coiled login
+```
+
+> Authentication successful π
+> Credentials have been saved at /home/s-nechuiviter/.config/dask/coiled.yaml
+
+Use `coiled setup gcp` once to set up connection to GCP.
+
+Coiled requires that all modules should be `conda` compatible, at least if start from conda environment.
+Or use pip/venv everywhere.
+
+# pip and venv
+
+To create new virtual environment:
+
+```bash
+python -m venv ./env
+```
+
+Activate:
+
+```bash
+source .venv/bin/activate
+```
+
+Check
+
+```bash
+which python
+```
+
+Deactivate
+
+```bash
+deactivate
+```
+
+Prepare pip
+
+```bash
+python3 -m pip install --upgrade pip
+```
+
+
+
+What we need?
+
+```
+[//]: # (for streamlit)
+pip3 install streamlit
+
+[//]: # (For Google vertex AI)
+pip3 install --upgrade google-cloud-aiplatform numpy"<=2.0"
+```
+[//]: # (sudo snap install google-cloud-cli --classic) For google account access
+
+# Results
+
+https://docs.coiled.io/user_guide/functions.html
+`executor_1.py` successfully executes already existing code, but will not reload it.
+
+https://docs.coiled.io/user_guide/cli-jobs.html
+Single file can be executed from terminal with `coiled run python file_name.py`. Stdout --> stdout.
+
+## Notebooks
+
+### Files
+The --sync flag will optionally synchronize the files you have locally to the cloud machine, and copy any changes made on the cloud machine back to your local hard drive.
+
+This gives you the experience of just working on your local file system.
+
+To do this, you will need to install mutagen, which is available on brew :
+
+brew install mutagen-io/mutagen/mutagen
+And then use the --sync flag.
+
+coiled notebook start --sync
+Then you will get live synchronization between the remote server (/scratch/synced) and your local machine. This allows you to edit files on either machine and have the edits quickly show up on the other.
+
+Files larger than 1 GiB are not synced. If you would like to work in an entire directory that isnβt synced, you can create one in another directory, for example at /scratch/not_synced/
+
+
+
+
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/__init__.py b/apps/streamlit_ds_chat/cloud_coiled_io/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/executor_1.py b/apps/streamlit_ds_chat/cloud_coiled_io/executor_1.py
new file mode 100644
index 0000000..d36bc1d
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/executor_1.py
@@ -0,0 +1,26 @@
+import coiled
+
+# from test_func import estimate_pi_simple
+import test_func
+
+cluster = coiled.Cluster(n_workers=2) # Scale out to 100 machines
+client = cluster.get_client()
+
+
+for i in range(10):
+ input("go?")
+ print("go")
+
+ # importlib.reload(test_func) # reload doesn't send new code to cluster
+ client.upload_file("test_func.py")
+
+ futures = []
+ for filename in range(100): # Nested for loop
+ future = client.submit(test_func.estimate_pi_simple, 100_000)
+ futures.append(future)
+
+ results = client.gather(futures)
+
+ best = max(results)
+ avg = sum(results) / len(results)
+ print(f"{min(results)} : {avg} : {max(results)}")
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/func_df.py b/apps/streamlit_ds_chat/cloud_coiled_io/func_df.py
new file mode 100644
index 0000000..d57efc8
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/func_df.py
@@ -0,0 +1,2 @@
+def run(df):
+ return df.mean().compute()
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/serverless_example.py b/apps/streamlit_ds_chat/cloud_coiled_io/serverless_example.py
new file mode 100644
index 0000000..b271ed4
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/serverless_example.py
@@ -0,0 +1,17 @@
+import coiled
+import random
+
+
+@coiled.function()
+def estimate_pi(n: int) -> float:
+ total = 0
+ for _ in range(n):
+ x = random.random()
+ y = random.random()
+ if x**2 + y**2 < 1:
+ total += 1
+ return total / n * 4
+
+
+pi = estimate_pi(100_000)
+print(pi)
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/test_code.py b/apps/streamlit_ds_chat/cloud_coiled_io/test_code.py
new file mode 100644
index 0000000..477d2ee
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/test_code.py
@@ -0,0 +1,11 @@
+import random
+
+
+def estimate_pi_simple(n: int) -> float:
+ total = 0
+ for _ in range(n):
+ x = random.random()
+ y = random.random()
+ if x**2 + y**2 < 1:
+ total += 1
+ return total / n * 4
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/test_func.py b/apps/streamlit_ds_chat/cloud_coiled_io/test_func.py
new file mode 100644
index 0000000..3ae4847
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/test_func.py
@@ -0,0 +1,16 @@
+import random
+
+
+def estimate_pi_simple(n: int) -> float:
+ total = 0
+ # return -1
+ for _ in range(n):
+ x = random.random()
+ y = random.random()
+ if x**2 + y**2 < 1:
+ total += 1
+ return total / n * 4
+
+
+if __name__ == "__main__":
+ print(estimate_pi_simple(100_000))
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/working_dynamic_code_upload.py b/apps/streamlit_ds_chat/cloud_coiled_io/working_dynamic_code_upload.py
new file mode 100644
index 0000000..95d8035
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/working_dynamic_code_upload.py
@@ -0,0 +1,35 @@
+import coiled
+from dask.distributed import Client
+
+# Step 1: Create or connect to a Coiled cluster
+cluster = coiled.Cluster(name="my-cluster", n_workers=1)
+# n_workers β 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):
+ input("go?")
+ print("go")
+
+ # Step 2: Upload the module file to all workers
+ client.upload_file("test_func.py")
+
+ # Step 3: Verify the file upload
+ def check_file(filename):
+ import os
+
+ return os.path.exists(filename)
+
+ print(client.run(check_file, "test_func.py")) # Should print True on all workers
+
+ # Step 4: Use the uploaded module in a distributed task
+ def use_uploaded_module():
+ import test_func
+
+ return test_func.estimate_pi_simple(100_000)
+
+ result = client.run(use_uploaded_module)
+ print(result) # Should print "Hello, world!" from all workers
diff --git a/apps/streamlit_ds_chat/cloud_coiled_io/working_dynamic_code_upload_dataframe.py b/apps/streamlit_ds_chat/cloud_coiled_io/working_dynamic_code_upload_dataframe.py
new file mode 100644
index 0000000..345dd89
--- /dev/null
+++ b/apps/streamlit_ds_chat/cloud_coiled_io/working_dynamic_code_upload_dataframe.py
@@ -0,0 +1,54 @@
+import coiled
+from dask.distributed import Client
+
+
+import numpy as np
+import dask.dataframe as dd
+import pandas as pd
+
+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)
+# n_workers β 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):
+ input("go?")
+ print("go")
+
+ # Step 2: Upload the module file to all workers
+ client.upload_file("func_df.py")
+
+ # Step 3: Verify the file upload
+ def check_file(filename):
+ import os
+
+ return os.path.exists(filename)
+
+ print(client.run(check_file, "func_df.py")) # Should print True on all workers
+
+ # Step 4: Use the uploaded module in a distributed task
+ def use_uploaded_module():
+ import func_df
+
+ return func_df.run(df)
+
+ result = client.run(use_uploaded_module)
+ print(type(result))
+ print(result)
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 β 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)
diff --git a/apps/streamlit_ds_chat/data/Walmart.csv b/apps/streamlit_ds_chat/data/Walmart.csv
new file mode 100644
index 0000000..d58bba9
--- /dev/null
+++ b/apps/streamlit_ds_chat/data/Walmart.csv
@@ -0,0 +1,6436 @@
+Store,Date,Weekly_Sales,Holiday_Flag,Temperature,Fuel_Price,CPI,Unemployment
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+44,24-08-2012,337985.74,0,77.37,3.582,130.8381613,5.407
+44,31-08-2012,339490.69,0,79.18,3.624,130.8853548,5.407
+44,07-09-2012,338737.33,1,70.65,3.689,130.9325484,5.407
+44,14-09-2012,347726.67,0,68.55,3.749,130.9776667,5.407
+44,21-09-2012,336017.6,0,67.96,3.821,131.0103333,5.407
+44,28-09-2012,355307.94,0,64.8,3.821,131.043,5.407
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+44,12-10-2012,337796.13,0,55.1,3.797,131.1083333,5.217
+44,19-10-2012,323766.77,0,52.06,3.781,131.1499677,5.217
+44,26-10-2012,361067.07,0,46.97,3.755,131.1930968,5.217
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+45,19-02-2010,841264.04,0,31.27,2.745,182.0347816,8.992
+45,26-02-2010,741891.65,0,34.89,2.754,182.0774691,8.992
+45,05-03-2010,777951.22,0,37.13,2.777,182.1201566,8.992
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+45,19-03-2010,773819.49,0,48.79,2.844,182.0779857,8.992
+45,26-03-2010,782563.38,0,54.36,2.854,181.9718697,8.992
+45,02-04-2010,877235.96,0,47.74,2.85,181.8657537,8.899
+45,09-04-2010,833782.7,0,65.45,2.869,181.7596377,8.899
+45,16-04-2010,782221.96,0,54.28,2.899,181.6924769,8.899
+45,23-04-2010,749779.1,0,53.47,2.902,181.6772564,8.899
+45,30-04-2010,737265.57,0,53.15,2.921,181.6620359,8.899
+45,07-05-2010,812190.76,0,70.75,2.966,181.6468154,8.899
+45,14-05-2010,758182.2,0,54.26,2.982,181.6612792,8.899
+45,21-05-2010,747888.25,0,62.62,2.958,181.8538486,8.899
+45,28-05-2010,801098.43,0,69.27,2.899,182.0464181,8.899
+45,04-06-2010,837548.62,0,75.93,2.847,182.2389876,8.899
+45,11-06-2010,794698.77,0,69.71,2.809,182.4315571,8.899
+45,18-06-2010,815130.5,0,72.62,2.78,182.4424199,8.899
+45,25-06-2010,792299.15,0,79.32,2.808,182.3806,8.899
+45,02-07-2010,800147.84,0,76.61,2.815,182.3187801,8.743
+45,09-07-2010,787062,0,82.45,2.793,182.2569603,8.743
+45,16-07-2010,723708.99,0,77.84,2.783,182.2604411,8.743
+45,23-07-2010,714601.11,0,81.46,2.771,182.3509895,8.743
+45,30-07-2010,716859.27,0,79.78,2.781,182.4415378,8.743
+45,06-08-2010,746517.32,0,77.17,2.784,182.5320862,8.743
+45,13-08-2010,722262.21,0,78.44,2.805,182.6226346,8.743
+45,20-08-2010,708568.29,0,76.01,2.779,182.6165205,8.743
+45,27-08-2010,721744.33,0,71.36,2.755,182.6104063,8.743
+45,03-09-2010,790144.7,0,78.37,2.715,182.6042922,8.743
+45,10-09-2010,721460.22,1,70.87,2.699,182.598178,8.743
+45,17-09-2010,716987.58,0,66.55,2.706,182.622509,8.743
+45,24-09-2010,678228.58,0,68.59,2.713,182.6696737,8.743
+45,01-10-2010,690007.76,0,70.58,2.707,182.7168385,8.724
+45,08-10-2010,745782.1,0,56.49,2.764,182.7640032,8.724
+45,15-10-2010,715263.3,0,58.61,2.868,182.8106203,8.724
+45,22-10-2010,730899.37,0,53.15,2.917,182.8558685,8.724
+45,29-10-2010,732859.76,0,61.3,2.921,182.9011166,8.724
+45,05-11-2010,764014.06,0,45.65,2.917,182.9463648,8.724
+45,12-11-2010,765648.93,0,46.14,2.931,182.9916129,8.724
+45,19-11-2010,723987.85,0,50.02,3,182.8989385,8.724
+45,26-11-2010,1182500.16,1,46.15,3.039,182.7832769,8.724
+45,03-12-2010,879244.9,0,40.93,3.046,182.6676154,8.724
+45,10-12-2010,1002364.34,0,30.54,3.109,182.5519538,8.724
+45,17-12-2010,1123282.85,0,30.51,3.14,182.517732,8.724
+45,24-12-2010,1682862.03,0,30.59,3.141,182.54459,8.724
+45,31-12-2010,679156.2,1,29.67,3.179,182.5714479,8.724
+45,07-01-2011,680254.35,0,34.32,3.193,182.5983058,8.549
+45,14-01-2011,654018.95,0,24.78,3.205,182.6585782,8.549
+45,21-01-2011,644285.33,0,30.55,3.229,182.9193368,8.549
+45,28-01-2011,617207.58,0,24.05,3.237,183.1800955,8.549
+45,04-02-2011,759442.33,0,28.73,3.231,183.4408542,8.549
+45,11-02-2011,766456,1,30.3,3.239,183.7016129,8.549
+45,18-02-2011,802253.41,0,40.7,3.245,183.9371353,8.549
+45,25-02-2011,733716.78,0,35.78,3.274,184.1625632,8.549
+45,04-03-2011,761880.36,0,38.65,3.433,184.3879911,8.549
+45,11-03-2011,714014.73,0,45.01,3.582,184.613419,8.549
+45,18-03-2011,733564.77,0,46.66,3.631,184.809719,8.549
+45,25-03-2011,719737.25,0,41.76,3.625,184.9943679,8.549
+45,01-04-2011,712425.76,0,37.27,3.638,185.1790167,8.521
+45,08-04-2011,750182.71,0,48.71,3.72,185.3636656,8.521
+45,15-04-2011,765270.02,0,53.69,3.821,185.5339821,8.521
+45,22-04-2011,813630.44,0,53.04,3.892,185.6684673,8.521
+45,29-04-2011,786561.61,0,66.18,3.962,185.8029526,8.521
+45,06-05-2011,810150.64,0,58.21,4.046,185.9374378,8.521
+45,13-05-2011,793889.1,0,60.38,4.066,186.0719231,8.521
+45,20-05-2011,727163.67,0,62.28,4.062,185.9661154,8.521
+45,27-05-2011,817653.25,0,69.7,3.985,185.8603077,8.521
+45,03-06-2011,877423.45,0,76.38,3.922,185.7545,8.521
+45,10-06-2011,814395.17,0,73.88,3.881,185.6486923,8.521
+45,17-06-2011,811153.67,0,69.32,3.842,185.6719333,8.521
+45,24-06-2011,762861.78,0,74.85,3.804,185.7919609,8.521
+45,01-07-2011,791495.25,0,74.04,3.748,185.9119885,8.625
+45,08-07-2011,768718.11,0,77.49,3.711,186.032016,8.625
+45,15-07-2011,748435.2,0,78.47,3.76,186.1399808,8.625
+45,22-07-2011,741625.25,0,82.33,3.811,186.2177885,8.625
+45,29-07-2011,704680.97,0,81.31,3.829,186.2955962,8.625
+45,05-08-2011,765996.92,0,78.22,3.842,186.3734038,8.625
+45,12-08-2011,724180.89,0,77,3.812,186.4512115,8.625
+45,19-08-2011,712362.72,0,72.98,3.747,186.5093071,8.625
+45,26-08-2011,833979.01,0,72.55,3.704,186.5641172,8.625
+45,02-09-2011,726482.39,0,70.63,3.703,186.6189274,8.625
+45,09-09-2011,746129.56,1,71.48,3.738,186.6737376,8.625
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+45,23-09-2011,714106.42,0,63.75,3.711,187.0295321,8.625
+45,30-09-2011,698986.34,0,70.66,3.645,187.2566641,8.625
+45,07-10-2011,753447.05,0,55.82,3.583,187.4837962,8.523
+45,14-10-2011,720946.99,0,63.82,3.541,187.6917481,8.523
+45,21-10-2011,771686.4,0,59.6,3.57,187.7846197,8.523
+45,28-10-2011,781694.57,0,51.78,3.569,187.8774913,8.523
+45,04-11-2011,833429.22,0,43.92,3.551,187.9703629,8.523
+45,11-11-2011,808624.82,0,47.65,3.53,188.0632345,8.523
+45,18-11-2011,773603.77,0,51.34,3.53,188.1983654,8.523
+45,25-11-2011,1170672.94,1,48.71,3.492,188.3504,8.523
+45,02-12-2011,875699.81,0,50.19,3.452,188.5024346,8.523
+45,09-12-2011,957155.31,0,46.57,3.415,188.6544692,8.523
+45,16-12-2011,1078905.68,0,39.93,3.413,188.7979349,8.523
+45,23-12-2011,1521957.99,0,42.27,3.389,188.9299752,8.523
+45,30-12-2011,869403.63,1,37.79,3.389,189.0620155,8.523
+45,06-01-2012,714081.05,0,35.88,3.422,189.1940558,8.424
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+45,20-01-2012,700392.21,0,31.85,3.533,189.4214733,8.424
+45,27-01-2012,624081.64,0,37.93,3.567,189.5168505,8.424
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+45,08-06-2012,795133,0,64.3,3.689,191.0155329,8.567
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+45,20-07-2012,737613.65,0,78.89,3.567,191.1670428,8.684
+45,27-07-2012,711671.58,0,77.2,3.647,191.1655664,8.684
+45,03-08-2012,725729.51,0,76.58,3.654,191.16409,8.684
+45,10-08-2012,733037.32,0,78.65,3.722,191.1626135,8.684
+45,17-08-2012,722496.93,0,75.71,3.807,191.2284919,8.684
+45,24-08-2012,718232.26,0,72.62,3.834,191.3448865,8.684
+45,31-08-2012,734297.87,0,75.09,3.867,191.461281,8.684
+45,07-09-2012,766512.66,1,75.7,3.911,191.5776756,8.684
+45,14-09-2012,702238.27,0,67.87,3.948,191.69985,8.684
+45,21-09-2012,723086.2,0,65.32,4.038,191.8567038,8.684
+45,28-09-2012,713173.95,0,64.88,3.997,192.0135577,8.684
+45,05-10-2012,733455.07,0,64.89,3.985,192.1704115,8.667
+45,12-10-2012,734464.36,0,54.47,4,192.3272654,8.667
+45,19-10-2012,718125.53,0,56.47,3.969,192.3308542,8.667
+45,26-10-2012,760281.43,0,58.85,3.882,192.3088989,8.667
\ No newline at end of file
diff --git a/apps/streamlit_ds_chat/data/Walmart_small.csv b/apps/streamlit_ds_chat/data/Walmart_small.csv
new file mode 100644
index 0000000..8064f58
--- /dev/null
+++ b/apps/streamlit_ds_chat/data/Walmart_small.csv
@@ -0,0 +1,20 @@
+Store,Date,Weekly_Sales,Holiday_Flag,Temperature,Fuel_Price,CPI,Unemployment
+1,05-02-2010,1643690.9,0,42.31,2.572,211.0963582,8.106
+1,12-02-2010,1641957.44,1,38.51,2.548,211.2421698,8.106
+1,19-02-2010,1611968.17,0,39.93,2.514,211.2891429,8.106
+1,26-02-2010,1409727.59,0,46.63,2.561,211.3196429,8.106
+1,05-03-2010,1554806.68,0,46.5,2.625,211.3501429,8.106
+1,12-03-2010,1439541.59,0,57.79,2.667,211.3806429,8.106
+1,19-03-2010,1472515.79,0,54.58,2.72,211.215635,8.106
+1,26-03-2010,1404429.92,0,51.45,2.732,211.0180424,8.106
+1,02-04-2010,1594968.28,0,62.27,2.719,210.8204499,7.808
+1,09-04-2010,1545418.53,0,65.86,2.77,210.6228574,7.808
+1,16-04-2010,1466058.28,0,66.32,2.808,210.4887,7.808
+1,23-04-2010,1391256.12,0,64.84,2.795,210.4391228,7.808
+1,30-04-2010,1425100.71,0,67.41,2.78,210.3895456,7.808
+1,07-05-2010,1603955.12,0,72.55,2.835,210.3399684,7.808
+1,14-05-2010,1494251.5,0,74.78,2.854,210.3374261,7.808
+1,21-05-2010,1399662.07,0,76.44,2.826,210.6170934,7.808
+1,28-05-2010,1432069.95,0,80.44,2.759,210.8967606,7.808
+1,04-06-2010,1615524.71,0,80.69,2.705,211.1764278,7.808
+1,11-06-2010,1542561.09,0,80.43,2.668,211.4560951,7.808
\ No newline at end of file
diff --git a/apps/streamlit_ds_chat/data/fruits.csv b/apps/streamlit_ds_chat/data/fruits.csv
new file mode 100644
index 0000000..3453f96
--- /dev/null
+++ b/apps/streamlit_ds_chat/data/fruits.csv
@@ -0,0 +1,22 @@
+,person,fruit,likes
+0,anna,banana,True
+1,ryan,banana,True
+2,john,banana,False
+3,igor,banana,True
+4,igor,apple,False
+5,olaf,banana,True
+6,anna,pineapple,False
+7,mike,apple,False
+8,ryan,apple,False
+9,anna,peach,True
+10,joe,apple,True
+11,ryan,plum,True
+12,anna,plum,False
+13,ryan,peach,False
+14,anna,peach,True
+15,anna,peach,True
+16,ryan,plum,True
+17,anna,plum,False
+18,ryan,peach,False
+19,ryan,pineapple,True
+20,ryan,kiwi,True
diff --git a/apps/streamlit_ds_chat/ds_chat_streamlit_app.py b/apps/streamlit_ds_chat/ds_chat_streamlit_app.py
new file mode 100644
index 0000000..adc9e6a
--- /dev/null
+++ b/apps/streamlit_ds_chat/ds_chat_streamlit_app.py
@@ -0,0 +1,233 @@
+import streamlit as st
+import importlib
+
+from dask.distributed import Client
+
+import pandas as pd
+from rich.pretty import pprint
+
+from vertex_ai_codegen import (
+ start_code_generator_chat,
+ GENERATION_CONFIG,
+ SAFETY_SETTINGS,
+)
+
+st.title("DS chat with Google Vertex AI")
+
+########################################################################################################################
+# Initialization
+
+# init chat model
+
+if "vertex_ai_model" not in st.session_state:
+ st.session_state["vertex_ai_model"] = "gemini-1.5-flash-001"
+
+if "code_get_chat" not in st.session_state:
+ chat = start_code_generator_chat(
+ st.secrets["gcs_connections"], model_name=st.session_state["vertex_ai_model"]
+ )
+ st.session_state.code_get_chat = chat
+else:
+ chat = st.session_state.code_get_chat
+
+# Create or connect to a Coiled cluster
+with st.spinner("Creating or connecting to a Coiled cluster"):
+ if "cluster" not in st.session_state:
+ import coiled
+
+ cluster = coiled.Cluster(
+ name="my-cluster", n_workers=1, idle_timeout="20 minutes"
+ )
+ # n_workers β 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)
+ st.session_state.cluster = cluster
+ st.session_state.client = client
+ else:
+ cluster = st.session_state.cluster
+ client = st.session_state.client
+
+########################################################################################################################
+# Helpful sidebar
+
+with st.sidebar:
+ "[View the source code](https://github.com/Sklavit/pet_project/tree/20240629_ds_chat/apps/streamlit_ds_chat)"
+
+########################################################################################################################
+# Get data to analyze
+
+# upload some dataset to analyze
+uploaded_file = st.file_uploader("Upload a dataset to be analyzed", type=["csv"])
+
+# NOTE this upload ALWAYS reload the page, which is a bit of dangerous
+# TODO it is better to add buttons: 'reset and use new file', 'do all operations with new file'
+# TODO it looks like cool functionality to replay analysis for other data
+
+if not uploaded_file:
+ st.error("Upload dataset to work with")
+ st.stop()
+else:
+ # if uploaded_file: # and question: # and anthropic_api_key:
+ dataframe = pd.read_csv(uploaded_file)
+ st.dataframe(dataframe)
+
+ ####################################################################################################################
+ # Chat logic goes here
+
+ if "messages" not in st.session_state:
+ st.session_state.messages = []
+
+ # display all session history
+ for message in st.session_state.messages:
+ # set custom avatars for system messages (they are not saved in the history)
+ kwargs = {}
+ match message["role"]:
+ case "error":
+ kwargs["avatar"] = "π"
+ case "info":
+ kwargs["avatar"] = "π"
+
+ with st.chat_message(message["role"], **kwargs):
+ st.write(message["content"])
+
+ if prompt := st.chat_input("What do you what to ask about given dataset?"):
+ # save user prompt
+ st.session_state.messages.append({"role": "user", "content": prompt})
+
+ # render user prompt in chat
+ with st.chat_message("user"):
+ st.markdown(prompt)
+
+ # processing of the assistant response
+ with st.chat_message("assistant"):
+ # step 1. code generation
+ with st.spinner(
+ "Waiting for the whole generated code "
+ "(streaming not working with Streamlit and this beta version of vertexAI)..."
+ ):
+ # TODO componentize this direct usage of chat.send_message
+ api_response = chat.send_message(
+ prompt,
+ generation_config=GENERATION_CONFIG,
+ safety_settings=SAFETY_SETTINGS,
+ stream=False,
+ )
+ response = api_response.candidates[0].content.parts[0]._raw_part.text
+
+ st.markdown(response)
+ print("A")
+ st.session_state.messages.append({"role": "assistant", "content": response})
+
+ print("B")
+
+ response = response.strip()
+
+ # This response should be in the form of code
+ if not (response.startswith("```python") and response.endswith("```")):
+ st.error("AI response is not a code!")
+ pprint(response)
+ print("C")
+ st.rerun()
+
+ print("D")
+ with st.chat_message("assistant"):
+ st.info("So, this is code")
+
+ # so, the response IS code. Save it to tmp file
+ # TODO using only one and the same file is not safe in multiuser environment
+ file = "task.py"
+ with open(file, "w") as file:
+ file.write(response[10:-3])
+
+ msg = "Generated code was saved to the file"
+ st.info(msg)
+
+ # Now we should run remote execution
+ with st.spinner("Waiting for remote code execution..."):
+ # Step 2: Upload the module file to all workers
+ # TODO using the same file is unsafe, especially for multiple users working at the same time
+ file_name = "task.py"
+ try:
+ client.upload_file(file_name)
+ except Exception as e:
+ st.error(e)
+ st.write(e)
+ st.write(client)
+ st.write(client.status)
+
+ if client.status == "closed":
+ client.restart()
+
+ # 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({"df_table": dataframe})
+
+ execution_results = client.run(use_uploaded_module)
+ # get the result from the first executor TODO there is a dict at all?
+ result = next(iter(execution_results.values()))
+
+ with st.chat_message("assistant"):
+ # st.info(result)
+ print("vertexAI response:")
+ pprint(result)
+ answer = result.get("results")
+ st.write(answer)
+
+ # save results in the history, so they will ba available for chatbot later
+ st.session_state.messages.append({"role": "assistant", "content": answer})
+ api_response = chat.send_message(
+ f"Below is the result of the execution of your code, from the last response. "
+ f"So, the response to question {prompt}
is {answer}.",
+ generation_config=GENERATION_CONFIG,
+ safety_settings=SAFETY_SETTINGS,
+ stream=False,
+ )
+ response = api_response.candidates[0].content.parts[0]._raw_part.text
+ st.divider()
+ st.markdown(response)
+ st.divider()
+
+ # Let's show some charts
+ chart = result.get("chart")
+ if chart:
+ st.write("Chart is here")
+ kwargs = chart.get("kwargs", {})
+
+ st.write(answer)
+
+ st.markdown(f'*{chart.get("title")}*')
+ match chart["chart_type"]:
+ case "bar_chart":
+ st.bar_chart(
+ chart["source"],
+ x=chart.get("x"),
+ y=chart.get("y"),
+ x_label=kwargs.get("x_label"),
+ y_label=kwargs.get("y_label"),
+ )
+ case "line_chart":
+ st.line_chart(
+ chart["source"],
+ x=chart.get("x"),
+ y=chart.get("y"),
+ x_label=kwargs.get("x_label"),
+ y_label=kwargs.get("y_label"),
+ )
+ case "scatter_chart":
+ st.scatter_chart(
+ chart["source"],
+ x=chart.get("x"),
+ y=chart.get("y"),
+ x_label=kwargs.get("x_label"),
+ y_label=kwargs.get("y_label"),
+ )
diff --git a/apps/streamlit_ds_chat/experiments_standalone/__init__.py b/apps/streamlit_ds_chat/experiments_standalone/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/apps/streamlit_ds_chat/experiments_standalone/code_gen.py b/apps/streamlit_ds_chat/experiments_standalone/code_gen.py
new file mode 100644
index 0000000..d37f1cb
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_standalone/code_gen.py
@@ -0,0 +1,11 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "Salesforce/codegen-350M-mono"
+model = AutoModelForCausalLM.from_pretrained(checkpoint)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+
+text = "# Write python code to read from csv file with pandas intro dataframe"
+
+completion = model.generate(**tokenizer(text, return_tensors="pt"))
+
+print(tokenizer.decode(completion[0]))
diff --git a/apps/streamlit_ds_chat/experiments_standalone/conversational.py b/apps/streamlit_ds_chat/experiments_standalone/conversational.py
new file mode 100644
index 0000000..ad30966
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_standalone/conversational.py
@@ -0,0 +1,14 @@
+from transformers import pipeline
+
+generator = pipeline(model="HuggingFaceH4/zephyr-7b-beta")
+# Zephyr-beta is a conversational model, so let's pass it a chat instead of a single string
+result = generator(
+ [{"role": "user", "content": "What is the capital of France? Answer in one word."}],
+ do_sample=False,
+ max_new_tokens=2,
+)
+
+# [{'generated_text': [{'role': 'user', 'content': 'What is the capital of France? Answer in one word.'},
+# {'role': 'assistant', 'content': 'Paris'}]}]
+
+print(result)
diff --git a/apps/streamlit_ds_chat/experiments_standalone/gemini.py b/apps/streamlit_ds_chat/experiments_standalone/gemini.py
new file mode 100644
index 0000000..f4b9c41
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_standalone/gemini.py
@@ -0,0 +1,26 @@
+from pprint import pprint
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+oracle = pipeline("text2text-generation", model="describeai/gemini")
+# - `"question-answering"`: will return a [`QuestionAnsweringPipeline`].
+# - `"text2text-generation"`: will return a [`Text2TextGenerationPipeline`].
+
+# QuestionAnsweringPipeline
+
+# result = oracle(question="Write a short snippet of python code, which use pandas to read csv file into dataframe.", context="I am an expert Python engineer.")
+result = oracle(
+ inputs=[
+ "Write a short snippet of python code, which use pandas to read csv file into dataframe.",
+ "I am an expert Python engineer.",
+ ],
+ max_lenght=1000,
+ max_new_tokens=1000,
+)
+
+pprint(result)
+
+# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+# tokenizer = AutoTokenizer.from_pretrained("describeai/gemini")
+# model = AutoModelForSeq2SeqLM.from_pretrained("describeai/gemini")
diff --git a/apps/streamlit_ds_chat/experiments_standalone/google_vertex_ai.py b/apps/streamlit_ds_chat/experiments_standalone/google_vertex_ai.py
new file mode 100644
index 0000000..2a69254
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_standalone/google_vertex_ai.py
@@ -0,0 +1,118 @@
+"""
+pip install --upgrade google-cloud-aiplatform
+gcloud auth application-default login
+
+Command 'gcloud' not found, but can be installed with:
+sudo snap install google-cloud-cli # version 483.0.0, or
+sudo snap install google-cloud-sdk # version 483.0.0
+
+#################333
+Credentials saved to file: [/home/s-nechuiviter/.config/gcloud/application_default_credentials.json]
+
+These credentials will be used by any library that requests Application Default Credentials (ADC).
+WARNING:
+Cannot find a quota project to add to ADC. You might receive a "quota exceeded" or "API not enabled" error. Run $ gcloud auth application-default set-quota-project to add a quota project.
+"""
+
+import vertexai
+from vertexai.generative_models import GenerativeModel
+import vertexai.preview.generative_models as generative_models
+
+# import streamlit as st
+
+from google.oauth2.service_account import Credentials
+
+creds = {}
+
+credentials = Credentials.from_service_account_info(
+ creds
+) # st.secrets["gcs_connections"])
+
+
+def multiturn_generate_content():
+ vertexai.init(
+ project="pivotal-cable-428219-c5",
+ location="us-central1",
+ credentials=credentials,
+ )
+ model = GenerativeModel(
+ "gemini-1.5-flash-001",
+ system_instruction=[
+ """You are an expert python engineer with data scientist background."""
+ ],
+ )
+ chat = model.start_chat()
+ print(
+ chat.send_message(
+ [text1_1],
+ generation_config=generation_config,
+ safety_settings=safety_settings,
+ )
+ )
+
+
+text1_1 = """Write a short snippet of python code, which use pandas to read csv file into dataframe. Return only code, nothing else. So I could copy the response, so I could copy it into python module."""
+
+generation_config = {
+ "max_output_tokens": 8192,
+ "temperature": 1,
+ "top_p": 0.95,
+}
+
+safety_settings = {
+ generative_models.HarmCategory.HARM_CATEGORY_HATE_SPEECH: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+ generative_models.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+ generative_models.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+ generative_models.HarmCategory.HARM_CATEGORY_HARASSMENT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+}
+
+multiturn_generate_content()
+
+"""
+Example results:
+
+candidates {
+ content {
+ role: "model"
+ parts {
+ text: "```python\nimport pandas as pd\ndf = pd.read_csv(\'your_file.csv\')\n```"
+ }
+ }
+ finish_reason: STOP
+ safety_ratings {
+ category: HARM_CATEGORY_HATE_SPEECH
+ probability: NEGLIGIBLE
+ probability_score: 0.06632687151432037
+ severity: HARM_SEVERITY_NEGLIGIBLE
+ severity_score: 0.10017222911119461
+ }
+ safety_ratings {
+ category: HARM_CATEGORY_DANGEROUS_CONTENT
+ probability: NEGLIGIBLE
+ probability_score: 0.20134170353412628
+ severity: HARM_SEVERITY_NEGLIGIBLE
+ severity_score: 0.07599521428346634
+ }
+ safety_ratings {
+ category: HARM_CATEGORY_HARASSMENT
+ probability: NEGLIGIBLE
+ probability_score: 0.193451926112175
+ severity: HARM_SEVERITY_NEGLIGIBLE
+ severity_score: 0.09334687888622284
+ }
+ safety_ratings {
+ category: HARM_CATEGORY_SEXUALLY_EXPLICIT
+ probability: NEGLIGIBLE
+ probability_score: 0.06816437095403671
+ severity: HARM_SEVERITY_NEGLIGIBLE
+ severity_score: 0.05155818909406662
+ }
+}
+usage_metadata {
+ prompt_token_count: 52
+ candidates_token_count: 24
+ total_token_count: 76
+}
+
+
+"""
diff --git a/apps/streamlit_ds_chat/experiments_standalone/hf_chat.py b/apps/streamlit_ds_chat/experiments_standalone/hf_chat.py
new file mode 100644
index 0000000..f138de4
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_standalone/hf_chat.py
@@ -0,0 +1,47 @@
+from pprint import pprint
+
+import requests
+import streamlit as st
+
+# Your Hugging Face API key
+API_TOKEN = st.secrets["HF_API_KEY"]
+
+# API URL for Hugging Face models
+API_URL = "https://api-inference.huggingface.co/models/gpt2"
+# API_URL = "https://api-inference.huggingface.co/models/microsoft/DialoGPT-large"
+
+# Headers for authentication
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+
+
+# Function to query the model
+def query(payload):
+ response = requests.post(API_URL, headers=headers, json=payload)
+ return response.json()
+
+
+# Example input
+data = {
+ # "inputs": "Can you explain the theory of relativity in simple terms?"
+ # "inputs": "I have a dataset with columns ID, name, company, price. I need to make an aggragated dataset with average price by company. Write python pandas code to do this. Responde with pyhton code only."
+ # "inputs": "Write python code to read file into pandas dataframe.",
+ "inputs": {
+ "text": "Write python code to read file into pandas dataframe.",
+ },
+ # "max_length": 10000,
+ "wait_for_model": True,
+}
+
+# Get the response
+response = query(data)
+pprint(response) # only field: generated_text
+
+#############
+# from transformers import pipeline # conda install pytorch
+#
+# # Load the model and tokenizer
+# generator = pipeline('text-generation', model='gpt2')
+#
+# # Generate text
+# result = generator("Can you explain the theory of relativity in simple terms?", max_length=50)
+# pprint(result)
diff --git a/apps/streamlit_ds_chat/experiments_standalone/hf_chat_ms.py b/apps/streamlit_ds_chat/experiments_standalone/hf_chat_ms.py
new file mode 100644
index 0000000..bbef4a0
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_standalone/hf_chat_ms.py
@@ -0,0 +1,57 @@
+from pprint import pprint
+
+import requests
+import streamlit as st
+from transformers import pipeline
+
+API_TOKEN = st.secrets["HF_API_KEY"]
+
+
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+API_URL = "https://api-inference.huggingface.co/models/microsoft/DialoGPT-large"
+
+
+def query(payload):
+ response = requests.post(API_URL, headers=headers, json=payload)
+ print("response", response.status_code)
+ return response.json()
+
+
+data = query(
+ {
+ "inputs": {
+ "past_user_inputs": ["Which movie is the best ?"],
+ "generated_responses": ["It's Die Hard for sure."],
+ "text": "Can you explain why ?",
+ },
+ }
+)
+# Response
+# This is annoying
+# data.pop("warnings")
+pprint(
+ (
+ data,
+ {
+ "generated_text": "It's the best movie ever.",
+ "conversation": {
+ "past_user_inputs": [
+ "Which movie is the best ?",
+ "Can you explain why ?",
+ ],
+ "generated_responses": [
+ "It's Die Hard for sure.",
+ "It's the best movie ever.",
+ ],
+ },
+ # "warnings": ["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation."],
+ },
+ )
+)
+
+
+messages = [
+ {"role": "user", "content": "Who are you?"},
+]
+pipe = pipeline("text-generation", model="microsoft/DialoGPT-large")
+pipe(messages)
diff --git a/apps/streamlit_ds_chat/experiments_standalone/hf_chat_ms_2.py b/apps/streamlit_ds_chat/experiments_standalone/hf_chat_ms_2.py
new file mode 100644
index 0000000..f563184
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_standalone/hf_chat_ms_2.py
@@ -0,0 +1,45 @@
+import streamlit as st
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+API_TOKEN = st.secrets["HF_API_KEY"]
+
+
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+API_URL = "https://api-inference.huggingface.co/models/microsoft/DialoGPT-large"
+
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
+
+chat_history_ids = None
+
+# Let's chat for 5 lines
+for step in range(5):
+ # encode the new user input, add the eos_token and return a tensor in Pytorch
+ new_user_input_ids = tokenizer.encode(
+ input(">> User:") + tokenizer.eos_token, return_tensors="pt"
+ )
+
+ # append the new user input tokens to the chat history
+ bot_input_ids = (
+ torch.cat([chat_history_ids, new_user_input_ids], dim=-1)
+ if step > 0
+ else new_user_input_ids
+ )
+
+ # generated a response while limiting the total chat history to 1000 tokens,
+ chat_history_ids = model.generate(
+ bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id
+ )
+
+ # pretty print last ouput tokens from bot
+ print(
+ "DialoGPT: {}".format(
+ tokenizer.decode(
+ chat_history_ids[:, bot_input_ids.shape[-1] :][0],
+ skip_special_tokens=True,
+ )
+ )
+ )
diff --git a/apps/streamlit_ds_chat/experiments_standalone/vertex_code_bison.py b/apps/streamlit_ds_chat/experiments_standalone/vertex_code_bison.py
new file mode 100644
index 0000000..3d9bffb
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_standalone/vertex_code_bison.py
@@ -0,0 +1,12 @@
+import vertexai
+from vertexai.language_models import CodeGenerationModel
+
+vertexai.init(project="pivotal-cable-428219-c5", location="us-central1")
+parameters = {"candidate_count": 1, "max_output_tokens": 1024, "temperature": 0.9}
+model = CodeGenerationModel.from_pretrained("code-bison@002")
+response = model.predict(
+ prefix="""You are an expert python engineer with data scientist background.
+ Write a short snippet of python code, which use pandas to read csv file into dataframe.""",
+ **parameters,
+)
+print(f"Response from Model: {response.text}")
diff --git a/apps/streamlit_ds_chat/experiments_streamlit/__init__.py b/apps/streamlit_ds_chat/experiments_streamlit/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/apps/streamlit_ds_chat/experiments_streamlit/chat-gpt.py b/apps/streamlit_ds_chat/experiments_streamlit/chat-gpt.py
new file mode 100644
index 0000000..1ccd259
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_streamlit/chat-gpt.py
@@ -0,0 +1,33 @@
+from openai import OpenAI
+import streamlit as st
+
+st.title("ChatGPT-like clone")
+
+client = OpenAI(api_key=st.secrets["OPENAI_API_KEY"])
+
+if "openai_model" not in st.session_state:
+ st.session_state["openai_model"] = "gpt-3.5-turbo"
+
+if "messages" not in st.session_state:
+ st.session_state.messages = []
+
+for message in st.session_state.messages:
+ with st.chat_message(message["role"]):
+ st.markdown(message["content"])
+
+if prompt := st.chat_input("What is up?"):
+ st.session_state.messages.append({"role": "user", "content": prompt})
+ with st.chat_message("user"):
+ st.markdown(prompt)
+
+ with st.chat_message("assistant"):
+ stream = client.chat.completions.create(
+ model=st.session_state["openai_model"],
+ messages=[
+ {"role": m["role"], "content": m["content"]}
+ for m in st.session_state.messages
+ ],
+ stream=True,
+ )
+ response = st.write_stream(stream)
+ st.session_state.messages.append({"role": "assistant", "content": response})
diff --git a/apps/streamlit_ds_chat/experiments_streamlit/single_file_upload_example.py b/apps/streamlit_ds_chat/experiments_streamlit/single_file_upload_example.py
new file mode 100644
index 0000000..4906ec5
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_streamlit/single_file_upload_example.py
@@ -0,0 +1,35 @@
+import time
+
+import streamlit as st
+import pandas as pd
+from io import StringIO
+
+uploaded_file = st.file_uploader("Choose a file")
+if uploaded_file is not None:
+ # To read file as bytes:
+ bytes_data = uploaded_file.getvalue()
+ # st.write(bytes_data)
+
+ # To convert to a string based IO:
+ stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
+ # st.write(stringio)
+
+ # To read file as string:
+ string_data = stringio.readline()
+ st.write(string_data)
+
+ # Can be used wherever a "file-like" object is accepted:
+ dataframe = pd.read_csv(uploaded_file)
+
+ # from stqdm import stqdm
+ # dataframe = pd.concat([chunk for chunk in stqdm(pd.read_csv(uploaded_file, chunksize=1000), desc='Loading data')])
+
+ st.write(dataframe)
+
+ with st.sidebar:
+ with st.echo():
+ st.write("This code will be printed to the sidebar.")
+
+ with st.spinner("Loading..."):
+ time.sleep(5)
+ st.success("Done!")
diff --git a/apps/streamlit_ds_chat/experiments_streamlit/streamlit_app.py b/apps/streamlit_ds_chat/experiments_streamlit/streamlit_app.py
new file mode 100644
index 0000000..a352769
--- /dev/null
+++ b/apps/streamlit_ds_chat/experiments_streamlit/streamlit_app.py
@@ -0,0 +1,58 @@
+import streamlit as st
+import numpy as np
+import altair as alt
+import pandas as pd
+
+# the application layout and title
+st.set_page_config(
+ layout="centered",
+ page_title="DS agent chat",
+ page_icon=":shark:",
+ initial_sidebar_state="expanded",
+ menu_items={
+ "About": "This app should work as AI Data Scientist, how answers questions about the given data."
+ },
+)
+
+st.write("Hello world!")
+
+st.header("st.button")
+
+if st.button("Say hello"):
+ st.write("Why hello there")
+else:
+ st.write("Goodbye")
+
+"""
+st.write allows writing text and arguments to the Streamlit app.
+
+In addition to being able to display text, the following can also be displayed via the st.write() command:
+
+Prints strings; works like st.markdown()
+Displays a Python dict
+Displays pandas DataFrame can be displayed as a table
+Plots/graphs/figures from matplotlib, plotly, altair, graphviz, bokeh
+And more (see st.write on API docs)
+"""
+
+df = pd.DataFrame({"first column": [1, 2, 3, 4], "second column": [10, 20, 30, 40]})
+st.write(df)
+
+# Example 4
+
+st.write("Below is a DataFrame:", df, "Above is a dataframe.")
+
+# Example 5
+
+df2 = pd.DataFrame(np.random.randn(200, 3), columns=["a", "b", "c"])
+c = (
+ alt.Chart(df2)
+ .mark_circle()
+ .encode(x="a", y="b", size="c", color="c", tooltip=["a", "b", "c"])
+)
+st.write(c)
+
+# or
+chart_data = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
+
+st.line_chart(chart_data)
diff --git a/apps/streamlit_ds_chat/readme.md b/apps/streamlit_ds_chat/readme.md
new file mode 100644
index 0000000..e9c7ae4
--- /dev/null
+++ b/apps/streamlit_ds_chat/readme.md
@@ -0,0 +1,85 @@
+Title
+feat: add Streamlit app for DS chat with Google Vertex AI and Coiled integration
+
+Overview
+Introduced a new Streamlit application for data science chat functionality using Google Vertex AI.
+Added multiple supporting scripts and modules for handling various functionalities such as code generation, cloud execution, and data visualization.
+Integrated Coiled for distributed computing and remote code execution.
+Provided detailed documentation and examples for setting up the environment, using the application, and testing different components.
+
+
+# Setting up a local development environment
+
+Before we can actually start building Streamlit apps, we will first have to set up a development environment.
+
+Let's start by installing and setting up a conda environment.
+
+## **Install conda**
+- Install `conda` by going to https://docs.conda.io/en/latest/miniconda.html and choose your operating system (Windows, Mac or Linux).
+- Download and run the installer to install `conda`.
+
+## **Create a new conda environment**
+Now that you have conda installed, let's create a conda environment for managing all the Python library dependencies.
+
+To create a new environment with Python 3.9, enter the following:
+```bash
+conda create -n stenv python=3.9
+```
+where `create -n stenv` will create a conda environment named `stenv` and `python=3.9` will setup the conda environment with Python version 3.9.
+
+or
+
+```bash
+conda create -p ./env python=3.9
+```
+for local environment at `./env`
+
+
+## **Activate the conda environment**
+
+To use a conda environment that we had just created that is named `stenv`, enter the following into the command line:
+
+```bash
+conda activate stenv
+```
+
+## **Install the Streamlit library**
+
+It's now time to install the `streamlit` library:
+```bash
+conda install streamlit
+// or
+pip install streamlit
+```
+
+
+## Other dependencies
+
+### For HuggingFace transformers
+pip install transformers
+conda install pytorch
+
+### For Google vertex AI
+pip install google-cloud-aiplatform // --upgrade ?
+sudo snap install google-cloud-cli --classic
+pip3 install black
+conda install numpy"<=2.0"
+
+
+### Installing Ruff
+Ruff is available as ruff on PyPI:
+
+```
+pip install ruff
+```
+
+Once installed, you can run Ruff from the command line:
+
+```bash
+ruff check # Lint all files in the current directory.
+ruff format # Format all files in the current directory.
+```
+
+### Bokeh visualization
+
+pip install bokeh -- already installed?
diff --git a/apps/streamlit_ds_chat/vertex_ai_codegen.py b/apps/streamlit_ds_chat/vertex_ai_codegen.py
new file mode 100644
index 0000000..e5cd4db
--- /dev/null
+++ b/apps/streamlit_ds_chat/vertex_ai_codegen.py
@@ -0,0 +1,87 @@
+import vertexai
+import vertexai.preview.generative_models as generative_models
+from google.oauth2.service_account import Credentials
+from vertexai.generative_models import GenerativeModel, ChatSession
+
+GENERATION_CONFIG = {
+ "max_output_tokens": 8192,
+ "temperature": 1,
+ "top_p": 0.95,
+}
+
+SAFETY_SETTINGS = {
+ generative_models.HarmCategory.HARM_CATEGORY_HATE_SPEECH: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+ generative_models.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+ generative_models.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+ generative_models.HarmCategory.HARM_CATEGORY_HARASSMENT: generative_models.HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
+}
+
+
+def start_code_generator_chat(
+ service_account_info, *, model_name="gemini-1.5-flash-001"
+) -> ChatSession:
+ """Create (?) connection to Vertex AI and initialize chat model for Code Generation task.
+
+ Parameters
+ ==========
+ service_account_info : dict[str, str]
+ The service account info in Google format.
+ model_name : str
+ The name of the Vertex AI model.
+
+ Returns
+ =======
+ ChatSession
+ """
+ credentials = Credentials.from_service_account_info(service_account_info)
+ vertexai.init(
+ project="pivotal-cable-428219-c5",
+ location="us-central1",
+ credentials=credentials,
+ )
+ model = GenerativeModel(
+ model_name, # by default, it will be "gemini-1.5-flash-001",
+ system_instruction=[
+ "You are a data scientist manager."
+ "You responsibility is to provide a plan of actions, which should be taken to answer the given question."
+ "Each new question will be marked by tags: and
."
+ "If you don't have enough data to answer the question, respond: "
+ "Information about loaded data will be provided between tags and in JSON format."
+ "You will be questioned with analytical questions about the data available in ."
+ "Write a full python module, which should contain only 1 function def run(data). "
+ "The data parameter is expected to be a dictionary, with the same keys as and with corresponding values. "
+ "The function should return the answer to the question in form of dictionary. "
+ "Key `results` should include the answer represented in the most structured way: "
+ "it should be a single number if possible, it may be list, or pandas.DataFrame. "
+ "The function should not return the string unless it is directly requested by the user question. "
+ "For example, the function may return string, if question was asked about something specific, like name of the person, "
+ "who satisfy some conditions. For example: `Who likes the most number of fruits?`. "
+ "Key `chart` should be `None` if no charts need, and be a dictionary if a chart is requested. "
+ "This dictionary should contain the following keys: \n"
+ "`chart_type`: One from list [`bar_chart`, `line_chart`, `scatter_chart`]\n"
+ "`title`: the chart title\n"
+ "`x`: Column name or key associated to the x-axis data. "
+ "Should be `None` is the data index intended to be used for the x-axis values.\n"
+ "`y`: Column name(s) or key(s) associated to the y-axis data. "
+ "Should be `None`, if the data of all remaining columns intended to be used as data series."
+ "Should be a Sequence of strings, if intended to draw several series on the same chart "
+ "by melting wide-format table into a long-format table behind the scenes.\n"
+ "`x_label`: The label for the x-axis. Should be `None` if intended to use the column name specified in `x`."
+ "`y_label`: The label for the y-axis. Should be `None` if intended to use the column name specified in `y`."
+ "`kwargs`: dict with kwargs for the corresponding Bokeh charting tool\n"
+ "`source`: `pandas.DataFrame` with datapoints to be plotted.\n"
+ "You should *not* write any code for chart plotting inside the python code, only return needed kwargs."
+ "You must include explanations or any other important information into your answer in the form of code comments."
+ "User may update the state of with messages starting with 'Update locals:'. You should update the state of respectively"
+ "On message starting with '`locals' you should return the current state of .",
+ "{" "df_table: {type: pandas.DataFrame}" "}",
+ ],
+ )
+ chat = model.start_chat()
+ return chat
+
+
+"""
+ _USER_ROLE = "user"
+ _MODEL_ROLE = "model"
+"""
diff --git a/apps/streamlit_ds_chat/vertex_ai_codegen_streamlit.py b/apps/streamlit_ds_chat/vertex_ai_codegen_streamlit.py
new file mode 100644
index 0000000..0853058
--- /dev/null
+++ b/apps/streamlit_ds_chat/vertex_ai_codegen_streamlit.py
@@ -0,0 +1,60 @@
+import streamlit as st
+
+from vertex_ai_codegen import (
+ start_code_generator_chat,
+ GENERATION_CONFIG,
+ SAFETY_SETTINGS,
+)
+
+st.title(
+ "Chat with Google Vertex AI to experiment with prompts which could be used for the orchestrator agent."
+)
+
+########################################################################
+# init chat model
+
+if "vertex_ai_model" not in st.session_state:
+ st.session_state["vertex_ai_model"] = "gemini-1.5-flash-001"
+
+
+if "code_get_chat" not in st.session_state:
+ chat = start_code_generator_chat(
+ st.secrets["gcs_connections"], model_name=st.session_state["vertex_ai_model"]
+ )
+ st.session_state.code_get_chat = chat
+else:
+ chat = st.session_state.code_get_chat
+
+########################################################################
+# Working with chat itself
+
+if "messages" not in st.session_state:
+ st.session_state.messages = []
+
+for message in st.session_state.messages:
+ with st.chat_message(message["role"]):
+ st.markdown(message["content"])
+
+# streaming is not working with streamlit, exceptions inside `vertexai.generative_models import GenerativeModel`
+USE_STREAMING = False
+
+if prompt := st.chat_input("What is up?"):
+ st.session_state.messages.append({"role": "user", "content": prompt})
+ with st.chat_message("user"):
+ st.markdown(prompt)
+
+ with st.chat_message("assistant"):
+ with st.spinner(
+ "Wait for the whole response (streaming not working with Streamlit)..."
+ ):
+ api_response = chat.send_message(
+ prompt,
+ generation_config=GENERATION_CONFIG,
+ safety_settings=SAFETY_SETTINGS,
+ stream=False,
+ )
+ response = api_response.candidates[0].content.parts[0]._raw_part.text
+ st.write(response)
+
+ print(("response:", response))
+ st.session_state.messages.append({"role": "assistant", "content": response})
diff --git a/projects/ds_chat/20_in_progress/2024-07-02_use_streamlit_as_frontend.md b/projects/ds_chat/20_in_progress/2024-07-02_use_streamlit_as_frontend.md
new file mode 100644
index 0000000..7f29ecd
--- /dev/null
+++ b/projects/ds_chat/20_in_progress/2024-07-02_use_streamlit_as_frontend.md
@@ -0,0 +1,17 @@
+Use https://streamlit.io/ as chat frontend.
+
+Requirements:
+- simple Streamlit app - empty app - done
+- connect to backend
+- get user questions
+- print answers
+- synchronise with backend
+
+optional:
+- file upload
+- picture showing
+ - see examples for st.write(c) (Plots/graphs/figures from matplotlib, plotly, altair, graphviz, bokeh)
+- charts showing
+- interactive charts with Bokeh
+- interactive charts with other FE tool
+-
\ No newline at end of file
diff --git a/projects/ds_chat/requirements.md b/projects/ds_chat/requirements.md
new file mode 100644
index 0000000..219c926
--- /dev/null
+++ b/projects/ds_chat/requirements.md
@@ -0,0 +1,16 @@
+Minimal requirements:
+- some working application
+- it should use preloaded (or uploaded) data
+- UI should be in the form of a chat with the user
+- user should be able to question in natural language about provided data
+- the app should answer the questions
+- the app should generate Python code
+- the Python code should be run in a (secure) container
+- LangChain lib must not be used
+
+Extra requirements:
+- it may be a web application (maybe React)
+- dataset upload
+- drawing charts
+- preparing reports
+- saving reports in pdf
\ No newline at end of file