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mckinsey · Jul 23, 2024 · 365d875 · 365d875
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commit 365d875
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Showing 5 changed files with 7 additions and 36 deletions.
diff --git a/vizro-ai/pyproject.toml b/vizro-ai/pyproject.toml
@@ -34,11 +34,6 @@ name = "vizro_ai"
 readme = "README.md"
 requires-python = ">=3.9"
 
-[project.optional-dependencies]
-anthropic = [
-  "langchain-anthropic"
-]
-
 [tool.coverage.paths]
 vizro = ["src/vizro_ai"]  # omit tests for clarity, although this can be useful to see what test lines DID NOT run
 

diff --git a/vizro-ai/snyk/requirements.txt b/vizro-ai/snyk/requirements.txt
@@ -10,4 +10,3 @@ ipython>=8.10.0
 urllib3>=2.0.7
 aiohttp>=3.9.2
 langchain-core>=0.1.31
-langchain-anthropic
diff --git a/vizro-ai/src/vizro_ai/chains/_llm_models.py b/vizro-ai/src/vizro_ai/chains/_llm_models.py
@@ -4,12 +4,6 @@
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_openai import ChatOpenAI
 
-try:
-    from langchain_anthropic import ChatAnthropic
-except ImportError:
-    ChatAnthropic = None
-
-
 SUPPORTED_MODELS = {
     "OpenAI": [
         "gpt-4-0613",
@@ -25,14 +19,9 @@
         "gpt-4o-2024-05-13",
         "gpt-4o",
     ],
-    "Anthropic": [
-        "claude-3-haiku-20240307",
-        "claude-3-sonnet-20240229",
-        "claude-3-opus-20240229",
-    ],
 }
 
-DEFAULT_WRAPPER_MAP: Dict[str, BaseChatModel] = {"OpenAI": ChatOpenAI, "Anthropic": ChatAnthropic}
+DEFAULT_WRAPPER_MAP: Dict[str, BaseChatModel] = {"OpenAI": ChatOpenAI}
 DEFAULT_MODEL = "gpt-3.5-turbo"
 DEFAULT_TEMPERATURE = 0
 

diff --git a/vizro-ai/src/vizro_ai/dashboard/response_models/components.py b/vizro-ai/src/vizro_ai/dashboard/response_models/components.py
@@ -53,9 +53,11 @@ def create(self, model, df_metadata) -> ComponentType:
             elif self.component_type == "AgGrid":
                 return vm.AgGrid(id=component_id_unique, figure=dash_ag_grid(data_frame=self.df_name))
             elif self.component_type == "Card":
-                result_proxy = _get_pydantic_output(
-                    query=self.component_description, llm_model=model, response_model=vm.Card
+                card_prompt = (
+                    "The Card uses the dcc.Markdown component from Dash as its underlying text component. "
+                    f"Create a card based on the card description: {self.component_description}."
                 )
+                result_proxy = _get_pydantic_output(query=card_prompt, llm_model=model, response_model=vm.Card)
                 proxy_dict = result_proxy.dict()
                 proxy_dict["id"] = component_id_unique
                 return vm.Card.parse_obj(proxy_dict)

diff --git a/vizro-ai/src/vizro_ai/dashboard/utils.py b/vizro-ai/src/vizro_ai/dashboard/utils.py
@@ -9,17 +9,6 @@
 import tqdm.std as tsd
 import vizro.models as vm
 
-IMPORT_STATEMENTS = (
-    "import vizro.plotly.express as px\n"
-    "from vizro.models.types import capture\n"
-    "import plotly.graph_objects as go\n"
-    "from vizro.tables import dash_ag_grid\n"
-    "from vizro.models import AgGrid, Card, Dashboard, Filter, Layout, Page, Graph\n"
-    "from vizro.managers import data_manager\n"
-    "from vizro import Vizro\n"
-    "import pandas as pd\n"
-)
-
 
 @dataclass
 class MetadataContent:
@@ -69,11 +58,8 @@ def _execute_step(pbar: tsd.tqdm, description: str, value: Any) -> Any:
 
 def _dashboard_code(dashboard: vm.Dashboard) -> str:
     """Generate dashboard code from dashboard object."""
-    dashboard_code_str = IMPORT_STATEMENTS + repr(dashboard)
-
-    # TODO: use black or ruff to format the code
-    # formatted_code = black.format_str(dashboard_code_str, mode=black.Mode())
-    return dashboard_code_str
+    # return dashboard.to_python()
+    return
 
 
 def _run_dashboard(dashboard: vm.Dashboard, df_metadata: DfMetadata) -> None: