:tada: initial commit

.gitignore

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+.idea/
+venv/

README.md

@@ -1,10 +1,10 @@
 ---
 title: HotDogGPT
-emoji: 📉
+emoji: 🌭
 colorFrom: blue
 colorTo: yellow
 sdk: gradio
-sdk_version: 4.1.1
+sdk_version: 3.50.2
 app_file: app.py
 pinned: false
 ---

app.py

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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)

requirements.txt

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+numpy
+opencv-python
+requests
+gradio==3.50.2