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# -*- coding: utf-8 -*-
# @Time : 2025/1/1
# @Author : wenshao
# @Email : [email protected]
# @Project : browser-use-webui
# @FileName: test_llm_api.py
import os
import pdb
from dotenv import load_dotenv
load_dotenv()
import sys
sys.path.append(".")
def test_openai_model():
from langchain_core.messages import HumanMessage
from src.utils import utils
llm = utils.get_llm_model(
provider="openai",
model_name="gpt-4o",
temperature=0.8,
base_url=os.getenv("OPENAI_ENDPOINT", ""),
api_key=os.getenv("OPENAI_API_KEY", "")
)
image_path = "assets/examples/test.png"
image_data = utils.encode_image(image_path)
message = HumanMessage(
content=[
{"type": "text", "text": "describe this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
]
)
ai_msg = llm.invoke([message])
print(ai_msg.content)
def test_gemini_model():
# you need to enable your api key first: https://ai.google.dev/palm_docs/oauth_quickstart
from langchain_core.messages import HumanMessage
from src.utils import utils
llm = utils.get_llm_model(
provider="gemini",
model_name="gemini-2.0-flash-exp",
temperature=0.8,
api_key=os.getenv("GOOGLE_API_KEY", "")
)
image_path = "assets/examples/test.png"
image_data = utils.encode_image(image_path)
message = HumanMessage(
content=[
{"type": "text", "text": "describe this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
]
)
ai_msg = llm.invoke([message])
print(ai_msg.content)
def test_azure_openai_model():
from langchain_core.messages import HumanMessage
from src.utils import utils
llm = utils.get_llm_model(
provider="azure_openai",
model_name="gpt-4o",
temperature=0.8,
base_url=os.getenv("AZURE_OPENAI_ENDPOINT", ""),
api_key=os.getenv("AZURE_OPENAI_API_KEY", "")
)
image_path = "assets/examples/test.png"
image_data = utils.encode_image(image_path)
message = HumanMessage(
content=[
{"type": "text", "text": "describe this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
]
)
ai_msg = llm.invoke([message])
print(ai_msg.content)
def test_deepseek_model():
from langchain_core.messages import HumanMessage
from src.utils import utils
llm = utils.get_llm_model(
provider="deepseek",
model_name="deepseek-chat",
temperature=0.8,
base_url=os.getenv("DEEPSEEK_ENDPOINT", ""),
api_key=os.getenv("DEEPSEEK_API_KEY", "")
)
pdb.set_trace()
message = HumanMessage(
content=[
{"type": "text", "text": "who are you?"}
]
)
ai_msg = llm.invoke([message])
print(ai_msg.content)
if __name__ == '__main__':
# test_openai_model()
# test_gemini_model()
# test_azure_openai_model()
test_deepseek_model()
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