# importing genai library from google 
from google import genai
from openai import OpenAI
import streamlit as st
import os
from huggingface_hub import InferenceClient
# from huggingface_hub import HfFolder


# creating client for HuggingFace
clientHuggingFace = InferenceClient(
  api_key = os.getenv("HUGGINGFACE_API_KEY")
)

# creating client for OpenAI
clientOpenAI = OpenAI(
  api_key=os.getenv('OPENAI_API_KEY')
)

# creating client for gemini
clientGemini = genai.Client(api_key=os.getenv('GEMINI_API_KEY'))


# Function to return response 
def load_answer_openAI(question):
    completion = clientOpenAI.chat.completions.create(
      model="gpt-4o-mini",
      store=True,
      messages=[
        {"role": "user", "content": question}
      ]
    )
    return completion.choices[0].message.content

def load_answer_Gemini(question):
    response = clientGemini.models.generate_content(
    model="gemini-2.0-flash",
    contents=question,
    )
    return response.text

def load_answer_HuggingFace(question, modelName):
    res = clientHuggingFace.chat.completions.create(
    model = modelName,
    messages=[
    {"role": "user", "content": question}
      ]
    )
    return res.choices[0].message.content

# def load_answer_huggingface(question):


# App UI 
st.set_page_config(page_title="Langchain Demo", page_icon=":robot:")
st.header("Langchain Demo")

def get_text():
    input_text = st.text_input("Question: ", key="input")
    return input_text

def get_modelName():
    input_model = st.text_input("Model Name for HuggingFace: ", key = "input1")
    return input_model

user_input =  get_text()

submitGPT = st.button("Generate with ChatGPT")
submitGemini = st.button("Generate with Gemini")
providedModelName = get_modelName()
submitHuggingFace = st.button("Generate with HuggingFace")



if submitGPT:
    response = load_answer_openAI(user_input)
    st.subheader("Answer: ")
    st.write(response)
elif submitGemini:
    response = load_answer_Gemini(user_input)
    st.subheader("Answer: ")
    st.write(response)
elif submitHuggingFace:
    response = load_answer_HuggingFace(user_input, providedModelName)
    st.subheader("Answer: ")
    st.write(response)