| 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) | |