import gradio as gr
import os
from langchain_community.llms import HuggingFaceEndpoint
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from dotenv import load_dotenv

load_dotenv()

# Setting the API
model_Api = os.getenv("HUGGINGFACEHUB_API_TOKEN")
repo_id = "mistralai/Mistral-7B-Instruct-v0.3"

def QueryBuilding():
    Query_template = """You are a supportive and friendly assistant. For initial greetings like 'hello,' respond briefly and warmly. For more specific queries from the user {query}, respond in a straightforward, compassionate way. Keep it simple and concise unless the user indicates they need detailed guidance. When the user shares something concerning, gently encourage them to reach out to a professional if needed.
                    Answer: provide guidance and support to the user in a more detailed, simple and straightforward manner when situation gets dire direct the user to an actual professional."""
    return Query_template

def PromptEngineering():
    Prompt = PromptTemplate.from_template(QueryBuilding())
    return Prompt

def LLM_building():
    llm_model = HuggingFaceEndpoint(
        repo_id=repo_id,
        max_length=128,
        token=model_Api
    )
    return llm_model

def langchainning():
    llm_chain = LLMChain(prompt=PromptEngineering(), llm=LLM_building())
    return llm_chain

def user_input(user):
    ans = langchainning().run(user)
    return ans

def chat_interface(message, history):
    response = user_input(message)
    return response

iface = gr.ChatInterface(
    fn=chat_interface,
    title="Mind Whizz",
    description="Hello Friend, How can I help you today?",
    examples=["Can you motivate me to learn to code?", "how can i stay positive in a negative situation?"],
)

if __name__ == "__main__":
    iface.launch(share=True)