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import base64
import requests
import os
class GPT:
def __init__(self):
self.api_key = os.getenv("OPENAI_API_KEY")
self.headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {self.api_key}"
}
self.system_prompt = "You will receive an image and a question. Please start by answering the question with a simple 'Yes', 'No', or a brief answer. Afterward, provide a detailed explanation of how you arrived at your answer, including a rationale or description of the key details in the image that led to your conclusion. Ensure the evaluation is as precise and exacting as possible, scrutinizing the image thoroughly."
self.input_tokens_count = 0
self.output_tokens_count = 0
def encode_image(self, image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def update_tokens_count(self, response):
self.input_tokens_count += response.json()["usage"]["prompt_tokens"]
self.output_tokens_count += response.json()["usage"]["completion_tokens"]
def show_usage(self):
print(f"Total vlm input tokens used: {self.input_tokens_count}\nTotal vlm output tokens used: {self.output_tokens_count}")
def predict(self, image_path, query):
# Getting the base64 string
base64_image = self.encode_image(image_path)
payload = {
"model": "gpt-4o-2024-08-06",
"messages": [
{
"role": "system",
"content": self.system_prompt
},
{
"role": "user",
"content": [
{
"type": "text",
"text": query
},
{
"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=self.headers, json=payload)
self.update_tokens_count(response)
response_content = response.json()["choices"][0]["message"]["content"]
return response_content
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