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| 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", encoding="utf-8") 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="llama3-8b-8192", | |
| 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) | |