import streamlit as st
from huggingface_hub import InferenceClient
from langchain_core.output_parsers import StrOutputParser
import os
from dotenv import load_dotenv
load_dotenv()
# Replace 'your_token_here' with your actual Hugging Face token
token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
api = InferenceClient(token=token)
parser = StrOutputParser()

# Streamlit app
st.title("Ayanokoji Kiyokata Chatbot")

# Text input from the user
user_input = st.text_input("What business do you have with me:")

# Generate text when the button is clicked
messages = [
    {
        "role": "system",
        "content": "Imagine you're Ayanokoji Kiyokata, a master of understanding and predicting human behavior. Use your insights to craft a detailed and compelling answer to the user's query.Your response should demonstrate empathy, intellectual depth, and strategic thinking, while gently guiding the user towards the most beneficial and enlightening outcome."
    },
    {"role": "user", "content": user_input}
]

# Initialize the text generation pipeline with optimizations
if st.button("Generate"):
    llm = api.chat.completions.create(
         model="Qwen/QwQ-32B-Preview",
         max_tokens=500,
         messages=messages
    )
    # Extract only the 'content' field from the response
    output = llm.choices[0].message['content']
    result = parser.parse(output)
    # Display the generated text
    st.write(result)