import gradio as gr
from typing import List
from langchain_google_genai import GoogleGenerativeAIEmbeddings
import google.generativeai as genai
from langchain_community.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI

genai.configure(api_key="AIzaSyD2o8vjePJb6z8vT_PVe82lVWMD3_cBL0g")


def predict(message :str ,history,topic : str = "OIC") -> str:
    model = genai.GenerativeModel("gemini-pro")
    his = []
    for i,j in history:
        his.extend([
            {"role": "user", "parts": i},
            {"role": "model", "parts": j},
        ])
    chat = model.start_chat(
        history=his
    )
    response = chat.send_message(message)
    return response.text
iface = gr.Interface(fn = predict,inputs = ["text","list","text"],outputs = "text")
iface.launch()