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

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

# # Set HuggingFace token from env or st.secrets
# os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("keys")
# os.environ["HF_TOKEN"]=os.getenv('Ayush')


# # Topic-wise base prompts and models
# topic_config = {
#     "Python": {
#         "prompt": "Answer like a senior Python developer and coding mentor.",
#         "model": "meta-llama/Llama-3.2-3B-Instruct"
#     },
#     "SQL": {
#         "prompt": "Answer like a senior SQL engineer with industry experience.",
#         "model": "google/gemma-3-27b-it"
#     },
#     "Power BI": {
#         "prompt": "Answer like a Power BI expert helping a beginner.",
#         "model": "mistralai/Mistral-7B-Instruct-v0.1"
#     },
#     "Statistics": {
#         "prompt": "Answer like a statistics professor explaining key concepts to a student.",
#         "model": "deepseek-ai/DeepSeek-R1"
#     },
#     "Machine Learning": {
#         "prompt": "Answer like an ML mentor guiding a junior data scientist.",
#         "model": "google/gemma-3-27b-it"
#     },
#     "Deep Learning": {
#         "prompt": "Answer like a deep learning researcher with real-world insights.",
#         "model": "meta-llama/Llama-3.2-3B-Instruct"
#     },
#     "Generative AI": {
#         "prompt": "Answer like an expert in LLMs and Generative AI research.",
#         "model": "deepseek-ai/DeepSeek-R1"
#     }
# }

# # Experience level adjustments to prompt
# experience_prompts = {
#     "Beginner": "Explain with simple language and clear examples for a beginner.",
#     "Intermediate": "Provide a detailed answer suitable for an intermediate learner.",
#     "Expert": "Give an in-depth and advanced explanation suitable for an expert."
# }

# # Streamlit app setup
# st.set_page_config(page_title="Data Science Mentor", page_icon="📘")
# st.title("📘 Data Science Mentor App")

# if "chat_history" not in st.session_state:
#     st.session_state.chat_history = []

# # Multi-select topics
# selected_topics = st.multiselect("Select one or more topics:", list(topic_config.keys()), default=["Python"])

# # Select experience level
# experience_level = st.selectbox("Select mentor experience level:", list(experience_prompts.keys()))

# question = st.text_area("Ask your question here:")

# if st.button("Get Answer"):
#     if not selected_topics:
#         st.warning("Please select at least one topic.")
#     elif not question.strip():
#         st.warning("Please enter your question.")
#     else:
#         # Combine prompts from selected topics + experience level
#         combined_prompt = ""
#         models_used = set()
#         for topic in selected_topics:
#             base_prompt = topic_config[topic]["prompt"]
#             combined_prompt += f"{base_prompt} "
#             models_used.add(topic_config[topic]["model"])

#         combined_prompt += experience_prompts[experience_level]

#         # Choose the first model from selected topics (or could do more advanced merging)
#         chosen_model = list(models_used)[0]

#         # Load model
#         llm = HuggingFaceHub(
#             repo_id=chosen_model,
#             model_kwargs={"temperature": 0.6, "max_new_tokens": 150}
#         )
#         chat_model = ChatHuggingFace(llm=llm)

#         messages = [
#             SystemMessage(content=combined_prompt),
#             HumanMessage(content=question)
#         ]

#         with st.spinner("Mentor is typing..."):
#             response = chat_model.invoke(messages)

#         st.markdown("### 🧠 Mentor's Response:")
#         st.markdown(response.content)

#         # Save chat
#         st.session_state.chat_history.append((selected_topics, experience_level, question, response.content))

# # Display chat history
# if st.session_state.chat_history:
#     st.markdown("---")
#     st.subheader("📝 Chat History")
#     for i, (topics, exp, q, a) in enumerate(st.session_state.chat_history, 1):
#         st.markdown(f"**{i}. Topics:** {', '.join(topics)} | **Mentor Level:** {exp}")
#         st.markdown(f"**You:** {q}")
#         st.markdown(f"**Mentor:** {a}")
#         st.markdown("---")

#     # Download PDF
#     if st.button("📄 Download PDF of this chat"):
#         pdf = FPDF()
#         pdf.add_page()
#         pdf.set_font("Arial", size=12)

#         pdf.cell(200, 10, txt="Data Science Mentor Chat History", ln=True, align="C")
#         pdf.ln(10)

#         for i, (topics, exp, q, a) in enumerate(st.session_state.chat_history, 1):
#             pdf.multi_cell(0, 10, f"{i}. Topics: {', '.join(topics)} | Mentor Level: {exp}\nYou: {q}\nMentor: {a}\n\n")

#         pdf_path = "/tmp/mentor_chat.pdf"
#         pdf.output(pdf_path)

#         with open(pdf_path, "rb") as f:
#             st.download_button("📥 Click to Download PDF", f, file_name="mentor_chat.pdf", mime="application/pdf")


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

# Directly enter or securely load your Hugging Face API token
HF_TOKEN = "your_huggingface_token_here"  # 🔐 Replace with your token or use st.secrets

# Load model with token explicitly passed
llm = HuggingFaceHub(
    repo_id="meta-llama/Llama-3.2-3B-Instruct",
    huggingfacehub_api_token=HF_TOKEN,
    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)

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

# Directly enter or securely load your Hugging Face API token
HF_TOKEN = "your_huggingface_token_here"  # 🔐 Replace with your token or use st.secrets

# Load model with token explicitly passed
llm = HuggingFaceHub(
    repo_id="meta-llama/Llama-3.2-3B-Instruct",
    huggingfacehub_api_token=HF_TOKEN,
    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)