formating and linting

src/backend/langflow/api/base.py

@@ -1,6 +1,7 @@
-from langflow.graph.utils import extract_input_variables_from_prompt
 from pydantic import BaseModel, validator
 
+from langflow.graph.utils import extract_input_variables_from_prompt
+
 
 class Code(BaseModel):
     code: str

src/backend/langflow/api/validate.py

@@ -1,4 +1,5 @@
-from fastapi import HTTPException
+from fastapi import APIRouter, HTTPException
+
 from langflow.api.base import (
     Code,
     CodeValidationResponse,
@@ -6,12 +7,8 @@ from langflow.api.base import (
     PromptValidationResponse,
     validate_prompt,
 )
-
-from langflow.utils.validate import validate_code
 from langflow.utils.logger import logger
-
+from langflow.utils.validate import validate_code
-
-from fastapi import APIRouter, HTTPException
 
 # build router
 router = APIRouter(prefix="/validate", tags=["validate"])

src/backend/langflow/interface/base.py

@@ -1,6 +1,6 @@
-from abc import ABC, abstractmethod
 import abc
-from typing import Any, Dict, List, Optional, Union
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional, Type, Union
 
 from pydantic import BaseModel
 
@@ -14,7 +14,7 @@ class LangChainTypeCreator(BaseModel, ABC):
     type_dict: Optional[Dict] = None
 
     @property
-    def frontend_node_class(self) -> str:
+    def frontend_node_class(self) -> Type[FrontendNode]:
         """The class type of the FrontendNode created in frontend_node."""
         return FrontendNode
 

src/backend/langflow/interface/chains/base.py

@@ -1,6 +1,6 @@
 from typing import Dict, List, Optional
-from langflow.custom.customs import get_custom_nodes
 
+from langflow.custom.customs import get_custom_nodes
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import chain_type_to_cls_dict
 from langflow.settings import settings

src/backend/langflow/interface/chains/custom.py

@@ -1,10 +1,11 @@
-from typing import Optional
+from typing import Dict, Optional, Type
+
 from langchain.chains import ConversationChain
-from langflow.graph.utils import extract_input_variables_from_prompt
-from pydantic import root_validator, Field
 from langchain.memory.buffer import ConversationBufferMemory
 from langchain.schema import BaseMemory
+from pydantic import Field, root_validator
 
+from langflow.graph.utils import extract_input_variables_from_prompt
 
 DEFAULT_SUFFIX = """"
 Current conversation:
@@ -93,7 +94,7 @@ class TimeTravelGuideChain(BaseCustomChain):
     AI:"""
 
 
-CUSTOM_CHAINS = {
+CUSTOM_CHAINS: Dict[str, Type[ConversationChain]] = {
     "SeriesCharacterChain": SeriesCharacterChain,
     "MidJourneyPromptChain": MidJourneyPromptChain,
     "TimeTravelGuideChain": TimeTravelGuideChain,

src/backend/langflow/interface/importing/utils.py

@@ -1,7 +1,7 @@
 # This module is used to import any langchain class by name.
 
 import importlib
-from typing import Any
+from typing import Any, Type
 
 from langchain import PromptTemplate
 from langchain.agents import Agent
@@ -10,7 +10,6 @@ from langchain.chat_models.base import BaseChatModel
 from langchain.llms.base import BaseLLM
 from langchain.tools import BaseTool
 
-
 from langflow.interface.tools.util import get_tool_by_name
 
 
@@ -66,7 +65,7 @@ def import_class(class_path: str) -> Any:
     return getattr(module, class_name)
 
 
-def import_prompt(prompt: str) -> PromptTemplate:
+def import_prompt(prompt: str) -> Type[PromptTemplate]:
     from langflow.interface.prompts.custom import CUSTOM_PROMPTS
 
     """Import prompt from prompt name"""
@@ -105,7 +104,7 @@ def import_tool(tool: str) -> BaseTool:
     return get_tool_by_name(tool)
 
 
-def import_chain(chain: str) -> Chain:
+def import_chain(chain: str) -> Type[Chain]:
     """Import chain from chain name"""
     from langflow.interface.chains.custom import CUSTOM_CHAINS
 

src/backend/langflow/interface/memories/base.py

@@ -1,8 +1,9 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Type
 
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.custom_lists import memory_type_to_cls_dict
 from langflow.settings import settings
+from langflow.template.base import FrontendNode
 from langflow.template.nodes import MemoryFrontendNode
 from langflow.utils.util import build_template_from_class
 
@@ -11,7 +12,7 @@ class MemoryCreator(LangChainTypeCreator):
     type_name: str = "memories"
 
     @property
-    def frontend_node_class(self) -> str:
+    def frontend_node_class(self) -> Type[FrontendNode]:
         """The class type of the FrontendNode created in frontend_node."""
         return MemoryFrontendNode
 

src/backend/langflow/interface/prompts/base.py

@@ -1,7 +1,8 @@
 from typing import Dict, List, Optional
 
-from langchain.prompts import loading
 from langchain import prompts
+from langchain.prompts import loading
+
 from langflow.custom.customs import get_custom_nodes
 from langflow.interface.base import LangChainTypeCreator
 from langflow.interface.importing.utils import import_class

src/backend/langflow/interface/prompts/custom.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Type
 
 from langchain.prompts import PromptTemplate
 from pydantic import root_validator
@@ -7,7 +7,6 @@ from langflow.graph.utils import extract_input_variables_from_prompt
 from langflow.template.base import Template, TemplateField
 from langflow.template.nodes import PromptTemplateNode
 
-
 # Steps to create a BaseCustomPrompt:
 # 1. Create a prompt template that endes with:
 #    Current conversation:
@@ -71,7 +70,9 @@ Human: {input}
     input_variables: List[str] = ["character", "series"]
 
 
-CUSTOM_PROMPTS = {"SeriesCharacterPrompt": SeriesCharacterPrompt}
+CUSTOM_PROMPTS: Dict[str, Type[BaseCustomPrompt]] = {
+    "SeriesCharacterPrompt": SeriesCharacterPrompt
+}
 
 if __name__ == "__main__":
     prompt = SeriesCharacterPrompt(character="Harry Potter", series="Harry Potter")