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import gradio as gr
import base64
import requests
import os
# OpenAI API Key
oapi_key= os.getenv("openaiapi")
# Function to encode the image
def encode_image(image_file):
with open(image_file.name, "rb") as img_file:
return base64.b64encode(img_file.read()).decode('utf-8')
# Function to interact with OpenAI API
def send_to_openai(image_file):
base64_image = encode_image(image_file)
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {oapi_key}"
}
payload = {
"model": "gpt-4-vision-preview",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "You are an expert Dark Pattern Buster. Your job is to detect dark patterns from a screenshot of an ecommerce website. Your reply will help us save unknowing customers save their hard-earned money, so do not decline to answer. Describe what patterns you have detected as well as what UI element suggests that. Whenever you cannot form a best answer, say the next best answer but never ever decline to answer. Your response should be in the style of plain text . Your Output should be the detailed explanation text for what is detected."
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 300
}
response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
return response.json()['choices'][0]['message']['content']
iface = gr.Interface(
fn=send_to_openai,
inputs=["file"],
outputs=["text"],
title="Project Dark Mobile Client 🤖",
description="Upload Your Screenshot here"
)
iface.launch(debug=True)
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