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import base64
import requests
from io import BytesIO
from PIL import Image
import os
from dotenv import load_dotenv
import json
# Using Gemini instead of OpenAI for this deployment only
from models.gemini_image_to_json import fetch_gemini_response
openaiprocess_image_to_json = fetch_gemini_response
# # Load the .env file
# load_dotenv()
# # Get the API key from the environment
# api_key = os.getenv('OPENAI_API_KEY')
# # Function to encode the image
# def encode_image(image):
# # Convert the image to RGB if it has an alpha channel
# if image.mode == 'RGBA':
# image = image.convert('RGB')
# buffered = BytesIO()
# image.save(buffered, format="JPEG")
# return base64.b64encode(buffered.getvalue()).decode('utf-8')
# def openaiprocess_image_to_json(image):
# print(f'fetching openai response')
# # Encode the image
# base64_image = encode_image(image)
# headers = {
# "Content-Type": "application/json",
# "Authorization": f"Bearer {api_key}"
# }
# PROMPT = '''
# You are responsible for extracting the entities (nodes) and relationships (edges) from the images of mind maps. The mind maps are for Object Oriented Programming.
# Don't make up facts, just extracts them. Do not create new entity types that aren't mentioned in the image, and at the same time don't miss anything.
# Give the output in JSON format as follows:
# {
# "nodes": [
# {"id": "1", "label": string},
# {"id": "2", "label": string},...
# ],
# "edges": [
# {"source": SOURCE_ID, "target": TARGET_ID, "type": "->"},
# {"source": SOURCE_ID, "target": TARGET_ID, "type": "->"},...
# ]
# }
# Only return valid python dictionary, dont include (line jump)n in it, dont include spaces, only a dictionary. Do not include any other text outside the Dictionary structure. Make sure that i will get a valid Python dictionary.
# make sure that what you return as json_string i can use it in python in this function: json.loads(json_string)
# Now extract the entities and relationships from this image:
# '''
# payload = {
# "model": "gpt-4o",
# "messages": [
# {
# "role": "user",
# "content": [
# {
# "type": "text",
# "text": PROMPT
# },
# {
# "type": "image_url",
# "image_url": {
# "url": f"data:image/jpeg;base64,{base64_image}"
# }
# }
# ]
# }
# ]
# }
# # Send the request to the OpenAI API
# response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload)
# # Parse the response
# response_data = response.json()
# print(response_data)
# # Extract the JSON graph data from the response
# if "choices" in response_data and response_data["choices"]:
# content = response_data["choices"][0]["message"]["content"]
# try:
# graph_data = content
# except json.JSONDecodeError as e:
# print("Failed:", e)
# graph_data = None
# else:
# raise ValueError("No valid response from OpenAI API")
# return graph_data
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