app.py

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)