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import base64
import cv2
import gradio as gr
import numpy as np
import requests
MARKDOWN = """
# HotDogGPT 💬 + 🌭
HotDogGPT is OpenAI Vision API experiment reproducing the famous
[Hot Dog, Not Hot Dog](https://www.youtube.com/watch?v=ACmydtFDTGs) app from Silicon
Valley.
<p align="center">
<img width="600" src="https://miro.medium.com/v2/resize:fit:650/1*VrpXE1hE4rO1roK0laOd7g.png" alt="hotdog">
</p>
Visit [awesome-openai-vision-api-experiments](https://github.com/roboflow/awesome-openai-vision-api-experiments)
repository to find more OpenAI Vision API experiments or contribute your own.
"""
API_URL = "https://api.openai.com/v1/chat/completions"
CLASSES = ["🌭 Hot Dog", "❌ Not Hot Dog"]
def preprocess_image(image: np.ndarray) -> np.ndarray:
image = np.fliplr(image)
return cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
def encode_image_to_base64(image: np.ndarray) -> str:
success, buffer = cv2.imencode('.jpg', image)
if not success:
raise ValueError("Could not encode image to JPEG format.")
encoded_image = base64.b64encode(buffer).decode('utf-8')
return encoded_image
def compose_payload(image: np.ndarray, prompt: str) -> dict:
base64_image = encode_image_to_base64(image)
return {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 300
}
def compose_classification_prompt(classes: list) -> str:
return (f"What is in the image? Return the class of the object in the image. Here "
f"are the classes: {', '.join(classes)}. You can only return one class "
f"from that list.")
def compose_headers(api_key: str) -> dict:
return {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}"
}
def prompt_image(api_key: str, image: np.ndarray, prompt: str) -> str:
headers = compose_headers(api_key=api_key)
payload = compose_payload(image=image, prompt=prompt)
response = requests.post(url=API_URL, headers=headers, json=payload).json()
if 'error' in response:
raise ValueError(response['error']['message'])
return response['choices'][0]['message']['content']
def classify_image(api_key: str, image: np.ndarray) -> str:
if not api_key:
raise ValueError(
"API_KEY is not set. "
"Please follow the instructions in the README to set it up.")
image = preprocess_image(image=image)
prompt = compose_classification_prompt(classes=CLASSES)
response = prompt_image(api_key=api_key, image=image, prompt=prompt)
return response
with gr.Blocks() as demo:
gr.Markdown(MARKDOWN)
api_key_textbox = gr.Textbox(
label="🔑 OpenAI API", type="password")
with gr.TabItem("Basic"):
with gr.Column():
input_image = gr.Image(
image_mode='RGB', type='numpy', height=500)
output_text = gr.Textbox(
label="Output")
submit_button = gr.Button("Submit")
submit_button.click(
fn=classify_image,
inputs=[api_key_textbox, input_image],
outputs=output_text)
demo.launch(debug=False, show_error=True)
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