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| import os | |
| import requests | |
| import time | |
| import streamlit as st | |
| # Get the Hugging Face API Token from environment variables | |
| HF_API_TOKEN = os.getenv("HF_API_KEY") | |
| if not HF_API_TOKEN: | |
| raise ValueError("Hugging Face API Token is not set in the environment variables.") | |
| # Hugging Face API URLs and headers for models | |
| MISTRAL_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1" | |
| MINICHAT_API_URL = "https://api-inference.huggingface.co/models/GeneZC/MiniChat-2-3B" | |
| DIALOGPT_API_URL = "https://api-inference.huggingface.co/models/microsoft/DialoGPT-large" | |
| PHI3_API_URL = "https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct" | |
| META_LLAMA_70B_API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-70B-Instruct" | |
| META_LLAMA_8B_API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct" | |
| GEMMA_27B_API_URL = "https://api-inference.huggingface.co/models/google/gemma-2-27b" | |
| GEMMA_27B_IT_API_URL = "https://api-inference.huggingface.co/models/google/gemma-2-27b-it" | |
| HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"} | |
| def query_mistral(payload): | |
| response = requests.post(MISTRAL_API_URL, headers=HEADERS, json=payload) | |
| st.write(f"Mistral API response: {response.json()}") # Debugging log | |
| return response.json() | |
| def query_minichat(payload): | |
| response = requests.post(MINICHAT_API_URL, headers=HEADERS, json=payload) | |
| return response.json() | |
| def query_dialogpt(payload): | |
| response = requests.post(DIALOGPT_API_URL, headers=HEADERS, json=payload) | |
| return response.json() | |
| def query_phi3(payload): | |
| response = requests.post(PHI3_API_URL, headers=HEADERS, json=payload) | |
| return response.json() | |
| def query_meta_llama_70b(payload): | |
| response = requests.post(META_LLAMA_70B_API_URL, headers=HEADERS, json=payload) | |
| return response.json() | |
| def query_meta_llama_8b(payload): | |
| response = requests.post(META_LLAMA_8B_API_URL, headers=HEADERS, json=payload) | |
| return response.json() | |
| def query_gemma_27b(payload): | |
| response = requests.post(GEMMA_27B_API_URL, headers=HEADERS, json=payload) | |
| return response.json() | |
| def query_gemma_27b_it(payload): | |
| response = requests.post(GEMMA_27B_IT_API_URL, headers=HEADERS, json=payload) | |
| return response.json() | |
| def count_tokens(text): | |
| return len(text.split()) | |
| # Token limit handling | |
| MAX_TOKENS_PER_MINUTE = 1000 | |
| token_count = 0 | |
| start_time = time.time() | |
| def handle_token_limit(text): | |
| global token_count, start_time | |
| current_time = time.time() | |
| if current_time - start_time > 60: | |
| token_count = 0 | |
| start_time = current_time | |
| token_count += count_tokens(text) | |
| if token_count > MAX_TOKENS_PER_MINUTE: | |
| raise ValueError("Token limit exceeded. Please wait before sending more messages.") | |
| def add_message_to_conversation(user_message, bot_message, model_name): | |
| st.session_state.conversation.append((user_message, bot_message, model_name)) | |
| # Streamlit app | |
| st.set_page_config(page_title="Multi-LLM Chatbot Interface", layout="wide") | |
| st.title("Multi-LLM Chatbot Interface") | |
| st.write("Multi LLM-Chatbot Interface by Thariq Arian") | |
| # Initialize session state for conversation and model history | |
| if "conversation" not in st.session_state: | |
| st.session_state.conversation = [] | |
| if "model_history" not in st.session_state: | |
| st.session_state.model_history = {model: [] for model in ["Mistral-8x7B", "Meta-Llama-3-70B-Instruct", "Meta-Llama-3-8B-Instruct", "MiniChat-2-3B", "DialoGPT (GPT-2-1.5B)", "Phi-3-mini-4k-instruct", "Gemma-2-27B", "Gemma-2-27B-IT"]} | |
| # Dropdown for LLM selection | |
| llm_selection = st.selectbox("Select Language Model", ["Mistral-8x7B", "Meta-Llama-3-70B-Instruct", "Meta-Llama-3-8B-Instruct", "MiniChat-2-3B", "DialoGPT (GPT-2-1.5B)", "Phi-3-mini-4k-instruct", "Gemma-2-27B", "Gemma-2-27B-IT"]) | |
| # User input for question | |
| question = st.text_input("Question", placeholder="Enter your question here...") | |
| # Handle user input and LLM response | |
| if st.button("Send") and question: | |
| try: | |
| handle_token_limit(question) | |
| with st.spinner("Waiting for the model to respond..."): | |
| chat_history = " ".join(st.session_state.model_history[llm_selection]) + f"User: {question}\n" | |
| if llm_selection == "Mistral-8x7B": | |
| mistral_response = query_mistral({"inputs": chat_history}) | |
| if isinstance(mistral_response, list) and len(mistral_response) > 0: | |
| mistral_answer = mistral_response[0].get("generated_text", "No response") | |
| else: | |
| mistral_answer = "No response" | |
| add_message_to_conversation(question, mistral_answer, llm_selection) | |
| st.session_state.model_history[llm_selection].append(f"User: {question}\nMistral-8x7B: {mistral_answer}\n") | |
| elif llm_selection == "Meta-Llama-3-70B-Instruct": | |
| meta_llama_70b_response = query_meta_llama_70b({"inputs": chat_history}) | |
| if isinstance(meta_llama_70b_response, dict) and "generated_text" in meta_llama_70b_response: | |
| meta_llama_70b_answer = meta_llama_70b_response["generated_text"] | |
| elif isinstance(meta_llama_70b_response, list) and len(meta_llama_70b_response) > 0: | |
| meta_llama_70b_answer = meta_llama_70b_response[0].get("generated_text", "No response") | |
| else: | |
| meta_llama_70b_answer = "No response" | |
| add_message_to_conversation(question, meta_llama_70b_answer, llm_selection) | |
| st.session_state.model_history[llm_selection].append(f"User: {question}\nMeta-Llama-3-70B-Instruct: {meta_llama_70b_answer}\n") | |
| elif llm_selection == "Meta-Llama-3-8B-Instruct": | |
| meta_llama_8b_response = query_meta_llama_8b({"inputs": chat_history}) | |
| if isinstance(meta_llama_8b_response, dict) and "generated_text" in meta_llama_8b_response: | |
| meta_llama_8b_answer = meta_llama_8b_response["generated_text"] | |
| elif isinstance(meta_llama_8b_response, list) and len(meta_llama_8b_response) > 0: | |
| meta_llama_8b_answer = meta_llama_8b_response[0].get("generated_text", "No response") | |
| else: | |
| meta_llama_8b_answer = "No response" | |
| add_message_to_conversation(question, meta_llama_8b_answer, llm_selection) | |
| st.session_state.model_history[llm_selection].append(f"User: {question}\nMeta-Llama-3-8B-Instruct: {meta_llama_8b_answer}\n") | |
| elif llm_selection == "MiniChat-2-3B": | |
| minichat_response = query_minichat({"inputs": chat_history}) | |
| if "error" in minichat_response and "is currently loading" in minichat_response["error"]: | |
| minichat_answer = f"Model is loading, please wait {minichat_response['estimated_time']} seconds." | |
| elif isinstance(minichat_response, list) and len(minichat_response) > 0: | |
| minichat_answer = minichat_response[0].get("generated_text", "No response") | |
| else: | |
| minichat_answer = "No response" | |
| add_message_to_conversation(question, minichat_answer, llm_selection) | |
| st.session_state.model_history[llm_selection].append(f"User: {question}\nMiniChat-2-3B: {minichat_answer}\n") | |
| elif llm_selection == "DialoGPT (GPT-2-1.5B)": | |
| dialogpt_response = query_dialogpt({"inputs": chat_history}) | |
| if isinstance(dialogpt_response, dict) and "generated_text" in dialogpt_response: | |
| dialogpt_answer = dialogpt_response["generated_text"] | |
| elif isinstance(dialogpt_response, list) and len(dialogpt_response) > 0: | |
| dialogpt_answer = dialogpt_response[0].get("generated_text", "No response") | |
| else: | |
| dialogpt_answer = "No response" | |
| add_message_to_conversation(question, dialogpt_answer, llm_selection) | |
| st.session_state.model_history[llm_selection].append(f"User: {question}\nDialoGPT (GPT-2-1.5B): {dialogpt_answer}\n") | |
| elif llm_selection == "Phi-3-mini-4k-instruct": | |
| phi3_response = query_phi3({"inputs": chat_history}) | |
| if isinstance(phi3_response, list) and len(phi3_response) > 0: | |
| phi3_answer = phi3_response[0].get("generated_text", "No response") | |
| else: | |
| phi3_answer = "No response" | |
| add_message_to_conversation(question, phi3_answer, llm_selection) | |
| st.session_state.model_history[llm_selection].append(f"User: {question}\nPhi-3-mini-4k-instruct: {phi3_answer}\n") | |
| elif llm_selection == "Gemma-2-27B": | |
| gemma_response = query_gemma_27b({"inputs": chat_history}) | |
| if isinstance(gemma_response, dict) and "generated_text" in gemma_response: | |
| gemma_answer = gemma_response["generated_text"] | |
| elif isinstance(gemma_response, list) and len(gemma_response) > 0: | |
| gemma_answer = gemma_response[0].get("generated_text", "No response") | |
| else: | |
| gemma_answer = "No response" | |
| add_message_to_conversation(question, gemma_answer, llm_selection) | |
| st.session_state.model_history[llm_selection].append(f"User: {question}\nGemma-2-27B: {gemma_answer}\n") | |
| elif llm_selection == "Gemma-2-27B-IT": | |
| gemma_27b_it_response = query_gemma_27b_it({"inputs": chat_history}) | |
| if isinstance(gemma_27b_it_response, dict) and "generated_text" in gemma_27b_it_response: | |
| gemma_27b_it_answer = gemma_27b_it_response["generated_text"] | |
| elif isinstance(gemma_27b_it_response, list) and len(gemma_27b_it_response) > 0: | |
| gemma_27b_it_answer = gemma_27b_it_response[0].get("generated_text", "No response") | |
| else: | |
| gemma_27b_it_answer = "No response" | |
| add_message_to_conversation(question, gemma_27b_it_answer, llm_selection) | |
| st.session_state.model_history[llm_selection].append(f"User: {question}\nGemma-2-27B-IT: {gemma_27b_it_answer}\n") | |
| except ValueError as e: | |
| st.error(str(e)) | |
| # Custom CSS for chat bubbles | |
| st.markdown( | |
| """ | |
| <style> | |
| .chat-bubble { | |
| padding: 10px 14px; | |
| border-radius: 14px; | |
| margin-bottom: 10px; | |
| display: inline-block; | |
| max-width: 80%; | |
| color: black; | |
| } | |
| .chat-bubble.user { | |
| background-color: #dcf8c6; | |
| align-self: flex-end; | |
| } | |
| .chat-bubble.bot { | |
| background-color: #fff; | |
| align-self: flex-start; | |
| } | |
| .chat-container { | |
| display: flex; | |
| flex-direction: column; | |
| gap: 10px; | |
| margin-top: 20px; | |
| } | |
| </style> | |
| """, | |
| unsafe_allow_html=True | |
| ) | |
| # Display the conversation | |
| st.write('<div class="chat-container">', unsafe_allow_html=True) | |
| for user_message, bot_message, model_name in st.session_state.conversation: | |
| st.write(f'<div class="chat-bubble user">You: {user_message}</div>', unsafe_allow_html=True) | |
| st.write(f'<div class="chat-bubble bot">{model_name}: {bot_message}</div>', unsafe_allow_html=True) | |
| st.write('</div>', unsafe_allow_html=True) |