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get_api_res.py
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import os
import traceback
from dotenv import load_dotenv
import base64
import requests
import openai
from openai import AzureOpenAI
from openai import OpenAI
import google.generativeai as genai
from PIL import Image
load_dotenv()
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
def get_openai_text_response(
prompt,
image_file_list,
model,
system_prompt="You are a helpful assistant.",
temperature=0.0,
):
try:
api_key = os.getenv("OPENAI_API_KEY")
client = OpenAI(api_key=api_key)
if len(image_file_list) == 0:
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
]
elif len(image_file_list) == 1:
messages = [
{"role": "system", "content": system_prompt},
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt,
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{encode_image(image_file_list[0])}"
},
},
],
},
]
elif len(image_file_list) == 2:
messages = [
{"role": "system", "content": system_prompt},
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt,
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{encode_image(image_file_list[0])}"
},
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{encode_image(image_file_list[1])}"
},
},
],
},
]
elif len(image_file_list) >= 3:
messages = [
{"role": "system", "content": system_prompt},
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt,
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{encode_image(image_file_list[0])}"
},
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{encode_image(image_file_list[1])}"
},
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{encode_image(image_file_list[2])}"
},
},
],
},
]
response = client.chat.completions.create(
model=model, messages=messages, temperature=temperature
)
response_text = response.choices[0].message.content
return response_text
except Exception as e:
print(f"An error occurred: {str(e)}")
traceback.print_exc()
return None
def get_gemini_text_response(
prompt,
image_file_list,
model="gemini-1.5-flash",
system_prompt="You are a helpful assistant.",
temperature=0.0,
):
try:
api_key = os.getenv("GEMINI_API_KEY")
genai.configure(api_key=api_key)
model = genai.GenerativeModel(model, system_instruction=system_prompt)
if len(image_file_list) == 0:
response = model.generate_content(
[
prompt,
],
stream=True,
generation_config=genai.types.GenerationConfig(
max_output_tokens=2000,
temperature=temperature,
),
)
elif len(image_file_list) == 1:
image1 = Image.open(image_file_list[0])
response = model.generate_content(
[
prompt,
image1,
],
stream=True,
generation_config=genai.types.GenerationConfig(
max_output_tokens=2000,
temperature=temperature,
),
)
elif len(image_file_list) == 2:
image1 = Image.open(image_file_list[0])
image2 = Image.open(image_file_list[1])
response = model.generate_content(
[prompt, image1, image2],
stream=True,
generation_config=genai.types.GenerationConfig(
max_output_tokens=2000,
temperature=temperature,
),
)
elif len(image_file_list) >= 3:
image1 = Image.open(image_file_list[0])
image2 = Image.open(image_file_list[1])
image3 = Image.open(image_file_list[2])
response = model.generate_content(
[prompt, image1, image2, image3],
stream=True,
generation_config=genai.types.GenerationConfig(
max_output_tokens=2000,
temperature=temperature,
),
)
response.resolve()
result = response.text
return result
except Exception as e:
print(f"An error occurred: {str(e)}")
traceback.print_exc()
return None