from langchain_ollama.chat_models import ChatOllama

from pydantic import BaseModel
from dotenv import load_dotenv

load_dotenv()

class response(BaseModel):
    answer: str
    confidence: float

llm = ChatOllama(model="gpt-oss:20b", base_url="http://192.168.1.26:11434")

question= """
Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.

You may assume that each input would have exactly one solution, and you may not use the same element twice.

You can return the answer in any order.

 

Example 1:

Input: nums = [2,7,11,15], target = 9
Output: [0,1]
Explanation: Because nums[0] + nums[1] == 9, we return [0, 1].
Example 2:

Input: nums = [3,2,4], target = 6
Output: [1,2]
Example 3:

Input: nums = [3,3], target = 6
Output: [0,1]


:provide me answer
"""

result = llm.stream(question)

for chunk in result:
    if hasattr(chunk, 'content') and chunk.content:
        print(chunk.content, end='', flush=True)
print()