# import os
# import langchain
# import langchain_huggingface
# from langchain_huggingface import HuggingFaceEndpoint,HuggingFacePipeline, ChatHuggingFace
# from langchain_core.messages import HumanMessage, SystemMessage, AIMessage

# os.environ["HF_TOKEN"]=os.getenv('Ayush')
# os.environ["HUGGINGFACEHUB_API_KEY"]=os.getenv('Ayush')

# llama_model = HuggingFaceEndpoint(repo_id= "meta-llama/Llama-3.2-3B-Instruct",provider= "nebius",temperature=0.6, max_new_tokens=70,task="conversational")
# model_d=ChatHuggingFace(llm =llama_model,repo_id= "meta-llama/Llama-3.2-3B-Instruct",provider= "nebius",temperature=0.6, max_new_tokens=70,task="conversational")
# message = [SystemMessage(content = "Answer like you are a hardcore pc gamer"),
#            HumanMessage(content = "Give me name of top 10 pc games of all time with description")]
# result = model_d.invoke(message)
# print(result.content)

import streamlit as st
from langchain_community.chat_models import ChatHuggingFace
from langchain_community.llms import HuggingFaceHub
from langchain_core.messages import HumanMessage, SystemMessage

# Setup API key (replace with your key or use st.secrets)
import os
os.environ["HF_TOKEN"]=os.getenv('Ayush')
os.environ["HUGGINGFACEHUB_API_KEY"]=os.getenv('Ayush')

# Load model
llm = HuggingFaceHub(
    repo_id="meta-llama/Llama-3.2-3B-Instruct",
    model_kwargs={"temperature": 0.6, "max_new_tokens": 100}
)
chat_model = ChatHuggingFace(llm=llm)

# Streamlit UI
st.title("🧪 Simple LLaMA Chat Test")

question = st.text_input("Ask a gaming-related question:", "Give me name of top 10 PC games of all time with description")

if st.button("Ask"):
    messages = [
        SystemMessage(content="Answer like you are a hardcore PC gamer"),
        HumanMessage(content=question)
    ]
    response = chat_model.invoke(messages)
    st.write("### Response:")
    st.write(response.content)