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Gradio Space: ask OpenAI Chat Completions in two boring ways — raw HTTP (`requests`)
and Hugging Face (`huggingface_hub.InferenceClient`). Put OPENAI_API_KEY in Space secrets.
"""
import os
import gradio as gr
import requests
from huggingface_hub import InferenceClient
# Pick any model name your OpenAI key is allowed to call.
MODEL = "gpt-5-nano"
def ask_with_requests(question: str) -> str:
"""
Path 1: build the HTTP request yourself.
- URL + JSON body match OpenAI's REST docs for Chat Completions.
- `Authorization: Bearer …` is how OpenAI expects the API key.
"""
key = os.environ["OPENAI_API_KEY"]
response = requests.post(
"https://api.openai.com/v1/chat/completions",
headers={
"Authorization": f"Bearer {key}",
"Content-Type": "application/json",
},
json={
"model": MODEL,
"messages": [{"role": "user", "content": question}],
},
timeout=120,
)
response.raise_for_status()
data = response.json()
return data["choices"][0]["message"]["content"]
def ask_with_huggingface_hub(question: str) -> str:
"""
Path 2: let `huggingface_hub` speak OpenAI-style Chat Completions.
`InferenceClient.chat.completions.create(...)` is documented to follow the same idea as
OpenAI's SDK; here we aim it at OpenAI by setting `base_url` to OpenAI's `/v1` root.
"""
client = InferenceClient(
base_url="https://api.openai.com/v1",
api_key=os.environ["OPENAI_API_KEY"],
)
completion = client.chat.completions.create(
model=MODEL,
messages=[{"role": "user", "content": question}],
)
return completion.choices[0].message.content
demo = gr.TabbedInterface(
[
gr.Interface(
fn=ask_with_requests,
inputs=gr.Textbox(label="Question", lines=3),
outputs=gr.Textbox(label="Answer", lines=12),
),
gr.Interface(
fn=ask_with_huggingface_hub,
inputs=gr.Textbox(label="Question", lines=3),
outputs=gr.Textbox(label="Answer", lines=12),
),
],
tab_names=["requests", "huggingface_hub"],
title="Ask OpenAI (two simple clients)",
)
demo.launch()
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