# -*- coding: utf-8 -*- # @Time : 2025/1/1 # @Author : wenshao # @Email : wenshaoguo1026@gmail.com # @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()