Fix formatting

apps/streamlit_ds_chat/chat_with_vertex_ai.py

@@ -1,7 +1,7 @@
 import streamlit as st
 
 import vertexai
-from vertexai.generative_models import GenerativeModel, Part
+from vertexai.generative_models import GenerativeModel
 import vertexai.preview.generative_models as generative_models
 from google.oauth2.service_account import Credentials
 

apps/streamlit_ds_chat/cloud_coiled_io/executor_1.py

@@ -1,11 +1,11 @@
 import coiled
-import importlib
+
+# 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()
 
-# from test_func import estimate_pi_simple
-import test_func
 
 for i in range(10):
     input("go?")

apps/streamlit_ds_chat/cloud_coiled_io/serverless_example.py

@@ -1,4 +1,5 @@
-import coiled, random
+import coiled
+import random
 
 
 @coiled.function()

apps/streamlit_ds_chat/ds_chat_streamlit_app.py

@@ -77,7 +77,7 @@
                 #     stream=False,
                 # )
                 # response = api_response.candidates[0].content.parts[0]._raw_part.text
-                response = f"AI was questioned. Test answer `here`. :) "
+                response = "AI was questioned. Test answer `here`. :) "
                 st.markdown(response)
 
             print(("response:", response))

apps/streamlit_ds_chat/experiments_standalone/google_vertex_ai.py

@@ -14,9 +14,8 @@
 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 base64
 import vertexai
-from vertexai.generative_models import GenerativeModel, Part
+from vertexai.generative_models import GenerativeModel
 import vertexai.preview.generative_models as generative_models
 
 # import streamlit as st

apps/streamlit_ds_chat/experiments_standalone/hf_chat_ms.py

@@ -1,89 +1,57 @@
-import json
 from pprint import pprint
 
+import requests
+import streamlit as st
+from transformers import pipeline
+
 API_TOKEN = st.secrets["HF_API_KEY"]
 
-import requests
 
 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."],
-#     },
-# ))
-
-# from transformers import pipeline
-#
-# messages = [
-#     {"role": "user", "content": "Who are you?"},
-# ]
-# pipe = pipeline("text-generation", model="microsoft/DialoGPT-large")
-# pipe(messages)
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
 
-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"
+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."],
+        },
     )
+)
 
-    # 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,
-            )
-        )
-    )
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+pipe = pipeline("text-generation", model="microsoft/DialoGPT-large")
+pipe(messages)

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,
+            )
+        )
+    )

apps/streamlit_ds_chat/vertex_ai_codegen_streamlit.py

@@ -1,7 +1,7 @@
 import streamlit as st
 
 import vertexai
-from vertexai.generative_models import GenerativeModel, Part
+from vertexai.generative_models import GenerativeModel
 import vertexai.preview.generative_models as generative_models
 from google.oauth2.service_account import Credentials