Custom Component with no of outputs depending on length of input size

[sohamchatterjee50]

Is there a way where the output list length of a component is equal to the given integer input ?

[dosubot[bot]]

Hey @sohamchatterjee50! I'm here to help you out with any issues or questions you have. Let's work together to solve the problem at hand.

To create a custom component where the number of outputs depends on the length of the input size, with the output list length equal to a given integer input, you can modify the DynamicOutputsComponent class to dynamically generate the outputs based on the provided integer input. Here is an example of how you can achieve this:

from langflow.custom import Component
from langflow.inputs import MessageTextInput, IntInput
from langflow.template import Output
from langflow.schema import Data

class DynamicOutputsComponent(Component):
    display_name = "Dynamic Outputs Component"
    description = "A component that generates outputs based on an integer input."
    icon = "icon-name"

    inputs = [
        MessageTextInput(
            name="input_text",
            display_name="Input Text",
            info="Text input for the component.",
        ),
        IntInput(
            name="number_of_outputs",
            display_name="Number of Outputs",
            info="Number of outputs to generate.",
        ),
    ]

    def __init__(self):
        super().__init__()
        self.outputs = []

    def generate_outputs(self):
        self.outputs = [
            Output(display_name=f"Output {i+1}", name=f"output_{i+1}", method=f"output_method_{i+1}")
            for i in range(self.number_of_outputs)
        ]

    def output_method(self, index: int) -> Data:
        return Data(data={"result": f"Output {index+1} for input: {self.input_text}"})

    def __getattr__(self, name):
        if name.startswith("output_method_"):
            index = int(name.split("_")[-1]) - 1
            return lambda: self.output_method(index)
        raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")

    def process(self):
        self.generate_outputs()
        return {output.name: getattr(self, output.method)() for output in self.outputs}

In this example:

• The generate_outputs method dynamically creates the outputs based on the integer input number_of_outputs.
• The output_method generates the output data for each output.
• The __getattr__ method is overridden to dynamically handle method calls for the generated outputs.
• The process method is added to generate the outputs and return the results.

This way, the number of outputs will be equal to the integer input provided ^[1][2].

To continue talking to Dosu, mention @dosu.

[sohamchatterjee50]

@dosu
In this piece of code, process method is not called which results in a empty output list.

[dosubot[bot]]

To ensure the process method is called in your custom component, you need to make sure it is properly defined and referenced. Additionally, you should verify that the component is correctly integrated into the flow and that the flow is being executed properly.

Here are some steps to troubleshoot and ensure the process method is called:

1. Define the process Method: Ensure that the process method is correctly defined within your component class.

2. Reference the process Method in Outputs: Make sure the outputs list references the process method correctly.

3. Initialize and Run the Flow: Use the load_flow_from_json and run_flow_from_json methods to load and run the flow, ensuring the environment is set up correctly.

Here is an example of a custom component with a properly defined process method:

from langflow.custom import Component
from langflow.inputs import MessageTextInput, IntInput
from langflow.template import Output
from langflow.schema import Data

class DynamicOutputsComponent(Component):
    display_name = "Dynamic Outputs Component"
    description = "A component that generates outputs based on an integer input."
    icon = "icon-name"

    inputs = [
        MessageTextInput(
            name="input_text",
            display_name="Input Text",
            info="Text input for the component.",
        ),
        IntInput(
            name="number_of_outputs",
            display_name="Number of Outputs",
            info="Number of outputs to generate.",
        ),
    ]

    def __init__(self):
        super().__init__()
        self.outputs = []

    def generate_outputs(self):
        self.outputs = [
            Output(display_name=f"Output {i+1}", name=f"output_{i+1}", method=f"output_method_{i+1}")
            for i in range(self.number_of_outputs)
        ]

    def output_method(self, index: int) -> Data:
        return Data(data={"result": f"Output {index+1} for input: {self.input_text}"})

    def __getattr__(self, name):
        if name.startswith("output_method_"):
            index = int(name.split("_")[-1]) - 1
            return lambda: self.output_method(index)
        raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")

    def process(self):
        self.generate_outputs()
        return {output.name: getattr(self, output.method)() for output in self.outputs}

Ensure Flow Execution

To ensure the flow is executed and the process method is called, use the following methods:

from langflow.processing.base import load_flow_from_json, run_flow_from_json

# Load the flow from a JSON file or object
flow = load_flow_from_json("path_to_flow.json")

# Run the flow with the given input
result = run_flow_from_json(flow, input_value="Your input value")

These methods will set up the environment and execute the flow, ensuring that the process method is called as part of the flow execution ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[Adrinalin4ik]

@dosu I think it doesn't work as component is going to be called automatically. You need to subscribe somehow on input change.