pip install --upgrade gradio_client

from gradio_client import Client

client = Client("abidlabs/en2fr", token="hf_...")
result = client.predict("Hello, world!", api_name="/predict")
print(result)  # "Bonjour, le monde!"

https://<space-subdomain>.hf.space/gradio_api/openapi.json

from gradio_client import Client

client = Client("abidlabs/whisper", token="hf_...")
client.view_api()  # Prints all endpoints with parameters

pip install --upgrade gradio_client

from gradio_client import Client

# Public Space
client = Client("username/space-name")

# Private Space (requires token)
client = Client("username/private-space", token="hf_xxxxx")

result = client.predict("Hello", api_name="/predict")

job = client.submit("Hello", api_name="/predict")
# Do other work...
result = job.result()  # Get result when ready

from gradio_client import Client, handle_file

client = Client("abidlabs/whisper", token="hf_...")

# From local file
result = client.predict(audio=handle_file("audio.wav"), api_name="/predict")

# From URL
result = client.predict(audio=handle_file("https://example.com/audio.wav"), api_name="/predict")

job = client.submit("Hello", api_name="/predict")

# Check status
status = job.status()
print(f"Queue position: {status.rank}, ETA: {status.eta}")

# Check if complete
if job.done():
    result = job.result()

# Cancel a pending job
job.cancel()

job = client.submit(prompt="Write a story", api_name="/generate")

# Iterate over streaming outputs
for output in job:
    print(output)

npm i @gradio/client

<script type="module">
  import { Client } from "https://cdn.jsdelivr.net/npm/@gradio/client/dist/index.min.js";
</script>

import { Client } from "@gradio/client";

const app = await Client.connect("abidlabs/en2fr", { token: "hf_..." });
const result = await app.predict("/predict", ["Hello"]);
console.log(result.data);

import { Client, handle_file } from "@gradio/client";

const app = await Client.connect("abidlabs/whisper", { token: "hf_..." });
const result = await app.predict("/predict", [
  handle_file("https://example.com/audio.wav")
]);

const job = app.submit("/predict", ["Hello"]);

for await (const message of job) {
  if (message.type === "data") {
    console.log("Result:", message.data);
  }
  if (message.type === "status") {
    console.log("Queue position:", message.position);
  }
}

curl -X POST "https://huggingface.co/proxy/abidlabs-en2fr.hf.space/gradio_api/call/predict" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $HF_TOKEN" \
  -d '{"data": ["Hello, world"]}'

{"event_id": "abc123"}

curl -N "https://huggingface.co/proxy/abidlabs-en2fr.hf.space/gradio_api/call/predict/abc123" \
  -H "Authorization: Bearer $HF_TOKEN"

event: complete
data: ["Bonjour, le monde!"]

from fastapi import FastAPI
from gradio_client import Client, handle_file

app = FastAPI()
client = Client("abidlabs/whisper", token="hf_...")

@app.post("/transcribe/")
async def transcribe(file_url: str):
    result = client.predict(audio=handle_file(file_url), api_name="/predict")
    return {"transcription": result}

import time
from gradio_client import Client

def predict_with_retry(client, *args, max_retries=3, **kwargs):
    for attempt in range(max_retries):
        try:
            return client.predict(*args, **kwargs)
        except Exception as e:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
            else:
                raise

client = Client("username/space", token="hf_...")
result = predict_with_retry(client, "input", api_name="/predict")

import gradio as gr
from gradio_client import Client

def process(prompt, request: gr.Request):
    x_ip_token = request.headers.get('x-ip-token', '')
    client = Client("owner/zerogpu-space", headers={"x-ip-token": x_ip_token})
    return client.predict(prompt, api_name="/predict")

demo = gr.Interface(fn=process, inputs="text", outputs="text")
demo.launch()

curl -s "https://huggingface.co/api/spaces/semantic-search?q=text+to+speech&sdk=gradio"