app.py

import os

from groq import Groq
from openai import OpenAI
from anthropic import Anthropic
import google.generativeai as genai

from dotenv import load_dotenv
import streamlit as st

load_dotenv()

st.set_page_config(page_title="strftime AI", page_icon="🕘")

with open("static/style.css") as f:
    st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)

st.header("strftime AI")

with st.sidebar:
    provider = st.radio(
        "Choose AI Provider",
        options=[
            "Groq",
            "Anthropic",
            "OpenAI",
            "Google Gemini",
            ]
        )

text = st.text_input("Enter datetime text eg. 2023-09-28T15:27:58Z", value="2023-09-28T15:27:58Z")

if text:
    prompt = (
        "Convert the following datetime string into strftime format, "
        "show the strftime string on top and then breakdown of % symbols: " + text
    )

    if provider == "Groq":
        client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
        chat_completion = client.chat.completions.create(
            messages=[{"role": "user", "content": prompt}],
            model="mixtral-8x7b-32768",
            max_tokens=1024,
        )
        st.markdown(chat_completion.choices[0].message.content)
    elif provider == "OpenAI":
        client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
        chat_completion = client.chat.completions.create(
            messages=[{"role": "user", "content": prompt}],
            model="gpt-3.5-turbo",
            max_tokens=1024,
        )
        st.markdown(chat_completion.choices[0].message.content)
    elif provider == "Anthropic":
        client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
        message = client.messages.create(
            messages=[{"role": "user", "content": prompt}],
            model="claude-3-haiku-20240307",
            max_tokens=1024,
        )
        st.markdown(message.content[0].text)
    elif provider == "Google Gemini":
        genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
        model = genai.GenerativeModel("gemini-pro")
        response = model.generate_content(prompt)
        st.markdown(response.text)