Spaces:
Running
Running
File size: 8,227 Bytes
6116c39 2394e8b 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 e1f6aec 6116c39 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
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"
GEMMA_API_URL = "https://api-inference.huggingface.co/models/google/gemma-1.1-7b-it"
GEMMA_2B_API_URL = "https://api-inference.huggingface.co/models/google/gemma-1.1-2b-it"
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_model(api_url, payload):
response = requests.post(api_url, headers=HEADERS, json=payload)
return response.json()
def count_tokens(text):
return len(text.split())
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")
# 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", "MiniChat-2-3B", "DialoGPT (GPT-2-1.5B)", "Phi-3-mini-4k-instruct",
"Gemma-1.1-7B", "Gemma-1.1-2B", "Meta-Llama-3-70B-Instruct", "Meta-Llama-3-8B-Instruct",
"Gemma-2-27B", "Gemma-2-27B-IT"
]}
# Dropdown for LLM selection
llm_selection = st.selectbox("Select Language Model", [
"Mistral-8x7B", "MiniChat-2-3B", "DialoGPT (GPT-2-1.5B)", "Phi-3-mini-4k-instruct",
"Gemma-1.1-7B", "Gemma-1.1-2B", "Meta-Llama-3-70B-Instruct", "Meta-Llama-3-8B-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) # Check token limit before processing
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":
response = query_model(MISTRAL_API_URL, {"inputs": chat_history})
answer = response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
elif llm_selection == "MiniChat-2-3B":
response = query_model(MINICHAT_API_URL, {"inputs": chat_history})
if "error" in response and "is currently loading" in response["error"]:
answer = f"Model is loading, please wait {response['estimated_time']} seconds."
else:
answer = response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
elif llm_selection == "DialoGPT (GPT-2-1.5B)":
response = query_model(DIALOGPT_API_URL, {"inputs": chat_history})
answer = response.get("generated_text", "No response") if isinstance(response, dict) else response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
elif llm_selection == "Phi-3-mini-4k-instruct":
response = query_model(PHI3_API_URL, {"inputs": chat_history})
answer = response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
elif llm_selection == "Gemma-1.1-7B":
response = query_model(GEMMA_API_URL, {"inputs": chat_history})
answer = response.get("generated_text", "No response") if isinstance(response, dict) else response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
elif llm_selection == "Gemma-1.1-2B":
response = query_model(GEMMA_2B_API_URL, {"inputs": chat_history})
answer = response.get("generated_text", "No response") if isinstance(response, dict) else response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
elif llm_selection == "Meta-Llama-3-70B-Instruct":
response = query_model(META_LLAMA_70B_API_URL, {"inputs": chat_history})
answer = response.get("generated_text", "No response") if isinstance(response, dict) else response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
elif llm_selection == "Meta-Llama-3-8B-Instruct":
response = query_model(META_LLAMA_8B_API_URL, {"inputs": chat_history})
answer = response.get("generated_text", "No response") if isinstance(response, dict) else response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
elif llm_selection == "Gemma-2-27B":
response = query_model(GEMMA_27B_API_URL, {"inputs": chat_history})
answer = response.get("generated_text", "No response") if isinstance(response, dict) else response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
elif llm_selection == "Gemma-2-27B-IT":
response = query_model(GEMMA_27B_IT_API_URL, {"inputs": chat_history})
answer = response.get("generated_text", "No response") if isinstance(response, dict) else response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
handle_token_limit(answer) # Check token limit for output
add_message_to_conversation(question, answer, llm_selection)
st.session_state.model_history[llm_selection].append(f"User: {question}\n{llm_selection}: {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) |