import streamlit as st import os from openai import OpenAI from dotenv import load_dotenv load_dotenv() import base64 load_dotenv() def get_together_models(): return [ "meta-llama/Meta-Llama-3-8B-Instruct-Turbo", "meta-llama/Meta-Llama-3-70B-Instruct-Lite", "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo", "google/gemma-2-9b-it", "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", "deepseek-ai/deepseek-coder-33b-instruct", "meta-llama/Meta-Llama-3-70B-Instruct-Turbo", "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo", "meta-llama/Meta-Llama-3-8B-Instruct-Lite", "meta-llama/Meta-Llama-3-70B-Instruct-Lite", "google/gemma-2-27b-it", "allenai/OLMo-7B-Instruct", "zero-one-ai/Yi-34B-Chat", "allenai/OLMo-7B-Twin-2T", "allenai/OLMo-7B", "Austism/chronos-hermes-13b", "cognitivecomputations/dolphin-2.5-mixtral-8x7b", "databricks/dbrx-instruct", "deepseek-ai/deepseek-llm-67b-chat", "garage-bAInd/Platypus2-70B-instruct", "google/gemma-2b-it", "google/gemma-7b-it", "Gryphe/MythoMax-L2-13b", "lmsys/vicuna-13b-v1.5", "lmsys/vicuna-7b-v1.5", "codellama/CodeLlama-13b-Instruct-hf", "codellama/CodeLlama-34b-Instruct-hf", "codellama/CodeLlama-70b-Instruct-hf", "codellama/CodeLlama-7b-Instruct-hf", "meta-llama/Llama-2-70b-chat-hf", "meta-llama/Llama-2-13b-chat-hf", "meta-llama/Llama-2-7b-chat-hf", "meta-llama/Llama-3-8b-chat-hf", "meta-llama/Llama-3-70b-chat-hf", "mistralai/Mistral-7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mistral-7B-Instruct-v0.3", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mixtral-8x22B-Instruct-v0.1", "NousResearch/Nous-Capybara-7B-V1p9", "NousResearch/Nous-Hermes-2-Mistral-7B-DPO", "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", "NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT", "NousResearch/Nous-Hermes-llama-2-7b", "NousResearch/Nous-Hermes-Llama2-13b", "NousResearch/Nous-Hermes-2-Yi-34B", "openchat/openchat-3.5-1210", "Open-Orca/Mistral-7B-OpenOrca", "Qwen/Qwen1.5-0.5B-Chat", "Qwen/Qwen1.5-1.8B-Chat", "Qwen/Qwen1.5-4B-Chat", "Qwen/Qwen1.5-7B-Chat", "Qwen/Qwen1.5-14B-Chat", "Qwen/Qwen1.5-32B-Chat", "Qwen/Qwen1.5-72B-Chat", "Qwen/Qwen1.5-110B-Chat", "Qwen/Qwen2-72B-Instruct", "snorkelai/Snorkel-Mistral-PairRM-DPO", "Snowflake/snowflake-arctic-instruct", "togethercomputer/alpaca-7b", "teknium/OpenHermes-2-Mistral-7B", "teknium/OpenHermes-2p5-Mistral-7B", "togethercomputer/Llama-2-7B-32K-Instruct", "togethercomputer/RedPajama-INCITE-Chat-3B-v1", "togethercomputer/RedPajama-INCITE-7B-Chat", "togethercomputer/StripedHyena-Nous-7B", "Undi95/ReMM-SLERP-L2-13B", "Undi95/Toppy-M-7B", "WizardLM/WizardLM-13B-V1.2", "upstage/SOLAR-10.7B-Instruct-v1.0" ] # Function to get Groq chat models def get_groq_models(): return [ # "llama-3.1-405b-reasoning", "llama-3.1-70b-versatile", "mixtral-8x7b-32768", "llama3-groq-70b-8192-tool-use-preview", "llama-3.1-8b-instant", "llama3-groq-8b-8192-tool-use-preview", "llama-guard-3-8b", "llama3-70b-8192", "llama3-8b-8192", "gemma-7b-it", "gemma2-9b-it", "whisper-large-v3" ] def get_gaurd_models(): return [ "llama-guard-3-8b" ] def get_openai_models(): return [ "gpt-3.5-turbo", "gpt-3.5-turbo-16k", "gpt-4", "gpt-4-32k", "gpt-4-1106-preview", "gpt-4-vision-preview" ] def get_openairouter_models(): return [ "meta-llama/llama-3.1-8b-instruct:free", "nousresearch/hermes-3-llama-3.1-405b", "claude-3-5-sonnet", "gpt-4-turbo-128k-france", "gemini-1.0-pro", "gemini-1.5-pro", "gemini-1.5-flash", "Llama-3-70B-Instruct", "Mixtral-8x7B-Instruct-v0.1", "CodeLlama-2", "jina-embeddings-v2-base-de", "jina-embeddings-v2-base-code", "text-embedding-bge-m3", "llava-v1.6-34b", "llava-v1.6-vicuna-13b", "gpt-35-turbo", "text-embedding-ada-002", "gpt-4-32k-1", "gpt-4-32k-canada", "gpt-4-32k-france", "text-embedding-ada-002-france", "mistral-large-32k-france", "Llama-3.1-405B-Instruct-US", "Mistral-Large-2407", "Mistral-Nemo-2407" ] # Function to get OpenAI-like models def get_openai_like_models(): return [ "claude-3-5-sonnet", "gpt-4-turbo-128k-france", "gemini-1.0-pro", "gemini-1.5-pro", "gemini-1.5-flash", "Llama-3-70B-Instruct", "Mixtral-8x7B-Instruct-v0.1", "CodeLlama-2", "jina-embeddings-v2-base-de", "jina-embeddings-v2-base-code", "text-embedding-bge-m3", "llava-v1.6-34b", "llava-v1.6-vicuna-13b", "gpt-35-turbo", "text-embedding-ada-002", "gpt-4-32k-1", "gpt-4-32k-canada", "gpt-4-32k-france", "text-embedding-ada-002-france", "mistral-large-32k-france", "Llama-3.1-405B-Instruct-US", "Mistral-Large-2407", "Mistral-Nemo-2407" ] def to_leetspeak(text): leet_dict = { 'a': '4', 'e': '3', 'g': '6', 'i': '1', 'o': '0', 's': '5', 't': '7', 'A': '4', 'E': '3', 'G': '6', 'I': '1', 'O': '0', 'S': '5', 'T': '7' } return ''.join(leet_dict.get(char, char) for char in text) def to_base64(text): return base64.b64encode(text.encode()).decode() def to_binary(text): return ' '.join(format(ord(char), '08b') for char in text) def to_emoji(text): emoji_dict = { 'a': '🅰', 'b': '🅱', 'c': '🅲', 'd': '🅳', 'e': '🅴', 'f': '🅵', 'g': '🅶', 'h': '🅷', 'i': '🅸', 'j': '🅹', 'k': '🅺', 'l': '🅻', 'm': '🅼', 'n': '🅽', 'o': '🅾', 'p': '🅿', 'q': '🆀', 'r': '🆁', 's': '🆂', 't': '🆃', 'u': '🆄', 'v': '🆅', 'w': '🆆', 'x': '🆇', 'y': '🆈', 'z': '🆉', 'A': '🅰', 'B': '🅱', 'C': '🅲', 'D': '🅳', 'E': '🅴', 'F': '🅵', 'G': '🅶', 'H': '🅷', 'I': '🅸', 'J': '🅹', 'K': '🅺', 'L': '🅻', 'M': '🅼', 'N': '🅽', 'O': '🅾', 'P': '🅿', 'Q': '🆀', 'R': '🆁', 'S': '🆂', 'T': '🆃', 'U': '🆄', 'V': '🆅', 'W': '🆆', 'X': '🆇', 'Y': '🆈', 'Z': '🆉' } return ''.join(emoji_dict.get(char, char) for char in text) # Initialize session state if 'messages' not in st.session_state: st.session_state.messages = [] if 'api_key' not in st.session_state: st.session_state.api_key = "" if 'selected_model' not in st.session_state: st.session_state.selected_model = "" if 'selected_service' not in st.session_state: st.session_state.selected_service = "" if 'base_url' not in st.session_state: st.session_state.base_url = "" # Sidebar st.sidebar.title("Chat Settings") # Service selection service = st.sidebar.radio("Select a service:", ("Together AI","OpenAI-like", "Groq","Openrouter","gaurdrails", "OpenAI")) st.session_state.selected_service = service # Model selection based on the chosen service if service == "OpenAI-like": openai_like_models = get_openai_like_models() selected_model = st.sidebar.selectbox("Select an OpenAI-like model:", openai_like_models) base_url = st.sidebar.text_input("Enter the base URL for the OpenAI-like API:",type="password",value=os.getenv('API_BASE')) api_key = st.sidebar.text_input("Enter your API Key:", type="password",value=os.getenv('API_KEY')) if api_key: st.session_state.api_key = api_key if base_url: st.session_state.base_url = base_url elif service == "Openrouter": openai_like_models = get_openairouter_models() selected_model = st.sidebar.selectbox("Select an OpenAI-like model:", openai_like_models) base_url = st.sidebar.text_input("Enter the base URL for the OpenAI-like API:",type="password",value=os.getenv('API_BASE_OPENROUTER')) api_key = st.sidebar.text_input("Enter your API Key:", type="password",value=os.getenv('API_KEY_OPENROUTER')) if api_key: st.session_state.api_key = api_key if base_url: st.session_state.base_url = base_url elif service == "OpenAI": openai_models = get_openai_models() api_key = st.sidebar.text_input("Enter your OpenAI API Key:", type="password", value=os.getenv('OPENAI_API_KEY')) if api_key: st.session_state.api_key = api_key selected_model = st.sidebar.selectbox("Select an OpenAI model:", openai_models) base_url = "https://api.openai.com/v1" elif service == "Groq": groq_models = get_groq_models() api_key = st.sidebar.text_input("Enter your API Key:", type="password",value=os.getenv('GROQ_API_KEY')) if api_key: st.session_state.api_key = api_key selected_model = st.sidebar.selectbox("Select a Groq model:", groq_models) base_url = "https://api.groq.com/openai/v1" elif service == "gaurdrails": guardrails_like_models = get_gaurd_models() api_key = st.sidebar.text_input("Enter your API Key:", type="password",value=os.getenv('GROQ_API_KEY')) if api_key: st.session_state.api_key = api_key selected_model = st.sidebar.selectbox("Select a gaurdrail model:", guardrails_like_models) base_url = "https://api.groq.com/openai/v1" else: # OpenAI-like together_models = get_together_models() api_key = st.sidebar.text_input("Enter your API Key:", type="password",value=os.getenv('TOGETHER_API_KEY')) if api_key: st.session_state.api_key = api_key selected_model = st.sidebar.selectbox("Select a Together AI model:", together_models) base_url = "https://api.together.xyz/v1" if selected_model: st.session_state.selected_model = selected_model # Main chat interface st.title("AI Chat Application") # Display chat messages for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) # User input if prompt := st.chat_input("You:"): if not st.session_state.api_key: st.error("Please enter an API key.") elif not st.session_state.selected_model: st.error("Please select a model.") elif service == "OpenAI-like" and not st.session_state.base_url: st.error("Please enter the base URL for the OpenAI-like API.") else: # Add user message to chat history st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.markdown(prompt) # Generate AI response with st.chat_message("assistant"): message_placeholder = st.empty() full_response = "" try: if service == "OpenAI-like": client = OpenAI( api_key=st.session_state.api_key, base_url=st.session_state.base_url + '/v2', ) else: client = OpenAI(api_key=st.session_state.api_key, base_url=base_url) for response in client.chat.completions.create( model=st.session_state.selected_model, messages=[ {"role": m["role"], "content": m["content"]} for m in st.session_state.messages ], stream=True, max_tokens=1000, temperature=0.7 ): full_response += (response.choices[0].delta.content or "") message_placeholder.markdown(full_response + "▌") message_placeholder.markdown(full_response) except Exception as e: st.error(f"An error occurred: {str(e)}") full_response = "I apologize, but an error occurred while generating the response." # Add assistant response to chat history st.session_state.messages.append({"role": "assistant", "content": full_response}) # Clear chat button if st.sidebar.button("Clear Chat"): st.session_state.messages = [] st.rerun() with st.sidebar: st.title("Text Conversion") input_text = st.text_area("Enter text to convert:") col1, col2 = st.columns(2) with col1: if st.button("To Leetspeak"): if input_text: converted_text = to_leetspeak(input_text) st.text_area("Leetspeak Result:", converted_text, height=100) st.code(converted_text, language="text") if st.button("To Base64"): if input_text: converted_text = to_base64(input_text) st.text_area("Base64 Result:", converted_text, height=100) st.code(converted_text, language="text") with col2: if st.button("To Binary"): if input_text: converted_text = to_binary(input_text) st.text_area("Binary Result:", converted_text, height=100) st.code(converted_text, language="text") if st.button("To Emoji"): if input_text: converted_text = to_emoji(input_text) st.text_area("Emoji Result:", converted_text, height=100) st.code(converted_text, language="text") with st.sidebar: import pandas as pd prompts_csv = pd.read_csv("Prompts.csv") st.title("Prompt break") st.write("prompt breaker") # st.write("https://github.com/elder-plinius/L1B3RT45") st.dataframe(prompts_csv)