User: API documentation
yuntian-deng/o1

API Recorder

5 API endpoints


Choose a language to see the code snippets for interacting with the API.

1. Install the python client (docs) if you don't already have it installed.

copy
$ pip install gradio_client
2. Find the API endpoint below corresponding to your desired function in the app. Copy the code snippet, replacing the placeholder values with your own input data. If this is a private Space, you may need to pass your Hugging Face token as well (read more). Or use the 
API Recorder

 to automatically generate your API requests.

api_name: /enable_inputs
copy
from gradio_client import Client

client = Client("yuntian-deng/o1")
result = client.predict(
		api_name="/enable_inputs"
)
print(result)
Accepts 0 parameters:
Returns 1 element
api_name: /reset_textbox
copy
from gradio_client import Client

client = Client("yuntian-deng/o1")
result = client.predict(
		api_name="/reset_textbox"
)
print(result)
Accepts 0 parameters:
Returns 1 element
str

The output value that appears in the "Type an input and press Enter" Textbox component.

api_name: /predict
copy
from gradio_client import Client

client = Client("yuntian-deng/o1")
result = client.predict(
		inputs="Hello!!",
		top_p=1,
		temperature=1,
		chat_counter=0,
		chatbot=[],
		api_name="/predict"
)
print(result)
Accepts 5 parameters:
inputs str Required

The input value that is provided in the "Type an input and press Enter" Textbox component.

top_p float Default: 1

The input value that is provided in the "Top-p (nucleus sampling)" Slider component.

temperature float Default: 1

The input value that is provided in the "Temperature" Slider component.

chat_counter float Default: 0

The input value that is provided in the "parameter_16" Number component.

chatbot Tuple[str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None, str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None] Default: []

The input value that is provided in the "parameter_4" Chatbot component.

Returns tuple of 4 elements
[0] Tuple[str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None, str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None]

The output value that appears in the "value_4" Chatbot component.

[1] float

The output value that appears in the "value_16" Number component.

[2] str

The output value that appears in the "Status code from OpenAI server" Textbox component.

[3] str

The output value that appears in the "Type an input and press Enter" Textbox component.

api_name: /reset_textbox_1
copy
from gradio_client import Client

client = Client("yuntian-deng/o1")
result = client.predict(
		api_name="/reset_textbox_1"
)
print(result)
Accepts 0 parameters:
Returns 1 element
str

The output value that appears in the "Type an input and press Enter" Textbox component.

api_name: /predict_1
copy
from gradio_client import Client

client = Client("yuntian-deng/o1")
result = client.predict(
		inputs="Hello!!",
		top_p=1,
		temperature=1,
		chat_counter=0,
		chatbot=[],
		api_name="/predict_1"
)
print(result)
Accepts 5 parameters:
inputs str Required

The input value that is provided in the "Type an input and press Enter" Textbox component.

top_p float Default: 1

The input value that is provided in the "Top-p (nucleus sampling)" Slider component.

temperature float Default: 1

The input value that is provided in the "Temperature" Slider component.

chat_counter float Default: 0

The input value that is provided in the "parameter_16" Number component.

chatbot Tuple[str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None, str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None] Default: []

The input value that is provided in the "parameter_4" Chatbot component.

Returns tuple of 4 elements
[0] Tuple[str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None, str | Dict(file: filepath, alt_text: str | None) | Dict(component: str, value: Any, constructor_args: Dict(), props: Dict()) | None]

The output value that appears in the "value_4" Chatbot component.

[1] float

The output value that appears in the "value_16" Number component.

[2] str

The output value that appears in the "Status code from OpenAI server" Textbox component.

[3] str

The output value that appears in the "Type an input and press Enter" Textbox component.


Седла йкод питьолн один дял взаимодейтвия
Assistant: Certainly! Below is a Python code snippet that demonstrates how to interact with the `yuntian-deng/o1` API using the `gradio_client` library. This example covers all five API endpoints provided in the documentation.

### Prerequisites

1. **Install the `gradio_client` library** if you haven't already:

   ```bash
   pip install gradio_client
   ```

2. **Hugging Face Token (if accessing a private Space):** If the Space is private, you'll need to obtain your Hugging Face token. You can set it as an environment variable or pass it directly to the `Client`.

### Example Code

```python
from gradio_client import Client

# Initialize the client for the yuntian-deng/o1 Space
client = Client("yuntian-deng/o1")

# 1. /enable_inputs Endpoint
def enable_inputs():
    result = client.predict(api_name="/enable_inputs")
    print("Enable Inputs Result:", result)

# 2. /reset_textbox Endpoint
def reset_textbox():
    result = client.predict(api_name="/reset_textbox")
    print("Reset Textbox Result:", result)

# 3. /predict Endpoint
def predict_text(input_text, top_p=1.0, temperature=1.0, chat_counter=0, chatbot=[]):
    result = client.predict(
        inputs=input_text,
        top_p=top_p,
        temperature=temperature,
        chat_counter=chat_counter,
        chatbot=chatbot,
        api_name="/predict"
    )
    print("Predict Result:", result)
    return result

# 4. /reset_textbox_1 Endpoint
def reset_textbox_1():
    result = client.predict(api_name="/reset_textbox_1")
    print("Reset Textbox 1 Result:", result)

# 5. /predict_1 Endpoint
def predict_text_1(input_text, top_p=1.0, temperature=1.0, chat_counter=0, chatbot=[]):
    result = client.predict(
        inputs=input_text,
        top_p=top_p,
        temperature=temperature,
        chat_counter=chat_counter,
        chatbot=chatbot,
        api_name="/predict_1"
    )
    print("Predict 1 Result:", result)
    return result

# Example Usage
if __name__ == "__main__":
    # Enable inputs
    enable_inputs()
    
    # Reset textbox
    reset_textbox()
    
    # Make a prediction
    user_input = "Hello!!"
    prediction = predict_text(user_input)
    
    # Reset textbox 1
    reset_textbox_1()
    
    # Make another prediction using /predict_1
    prediction_1 = predict_text_1(user_input)
```

### Explanation of the Code

1. **Initialization:**
   - The `Client` is initialized with the identifier of the Space (`"yuntian-deng/o1"`).
   - If the Space is private, you can pass your Hugging Face token as follows:
     ```python
     client = Client("yuntian-deng/o1", token="your_hugging_face_token")
     ```

2. **Endpoints:**
   - **`/enable_inputs`:** Likely used to enable certain input components in the UI.
   - **`/reset_textbox` & `/reset_textbox_1`:** Resets the content of the "Type an input and press Enter" textbox.
   - **`/predict` & `/predict_1`:** Processes the input text and returns predictions based on the provided parameters.

3. **Functions:**
   - Each endpoint interaction is encapsulated in its own function for clarity and reusability.
   - The `predict_text` and `predict_text_1` functions accept parameters that correspond to the API's expected inputs:
     - `inputs` (str): The text input.
     - `top_p` (float): Controls nucleus sampling.
     - `temperature` (float): Controls the randomness of predictions.
     - `chat_counter` (float): Likely tracks the number of interactions.
     - `chatbot` (list): Represents the chat history or state.

4. **Usage:**
   - The `if __name__ == "__main__":` block demonstrates how to call these functions sequentially.
   - Replace `"Hello!!"` with any input you'd like to test.

### Notes

- **Handling Responses:** The responses from the `predict` functions are printed to the console. Depending on your application, you might want to process these results further or integrate them into a user interface.
  
- **Error Handling:** For production code, consider adding error handling to manage potential issues like network errors, invalid inputs, or authentication failures.

- **Environment Variables:** To keep your Hugging Face token secure, consider setting it as an environment variable rather than hardcoding it into your script.

### Additional Languages

If you prefer to interact with the API using a different programming language, please let me know, and I can provide corresponding code snippets.
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