app.py

import streamlit as st
from huggingface_hub import InferenceClient
import os

st.title("CODEFUSSION ☄")  

base_url = "https://api-inference.huggingface.co/models/"
API_KEY = os.environ.get('HUGGINGFACE_API_KEY')

model_links = {
    "LegacyLift🚀": base_url + "mistralai/Mistral-7B-Instruct-v0.2",  
    "ModernMigrate⭐": base_url + "mistralai/Mixtral-8x7B-Instruct-v0.1",  
    "RetroRecode🔄": base_url + "microsoft/Phi-3-mini-4k-instruct" 
}

model_info = {
    "LegacyLift🚀": {
        'description': """The LegacyLift model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
            \nThis model is best for minimal problem-solving, content writing, and daily tips.\n""",
        'logo': './11.jpg'
    },
    "ModernMigrate⭐": {
        'description': """The ModernMigrate model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
            \nThis model excels in coding, logical reasoning, and high-speed inference. \n""",
        'logo': './2.jpg'
    },
    "RetroRecode🔄": {
        'description': """The RetroRecode model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
          \nThis model is best suited for critical development, practical knowledge, and serverless inference.\n""",
        'logo': './3.jpg'
    },
}

def format_prompt(message, conversation_history, custom_instructions=None):
    prompt = ""
    if custom_instructions:
        prompt += f"[INST] {custom_instructions} [/INST]"
    
    prompt += "[CONV_HISTORY]\n"
    for role, content in conversation_history:
        prompt += f"{role.upper()}: {content}\n"
    prompt += "[/CONV_HISTORY]"
    
    prompt += f"[INST] {message} [/INST]"
    return prompt

def reset_conversation():
    '''
    Resets Conversation
    '''
    st.session_state.messages = []
    st.session_state.conversation_history = []

if "messages" not in st.session_state:
    st.session_state.messages = []
    st.session_state.conversation_history = []

models = [key for key in model_links.keys()]

selected_model = st.sidebar.selectbox("Select Model", models)
temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5)
st.sidebar.button('Reset Chat', on_click=reset_conversation)

st.sidebar.write(f"You're now chatting with **{selected_model}**")
st.sidebar.markdown(model_info[selected_model]['description'])
st.sidebar.image(model_info[selected_model]['logo'])
st.sidebar.markdown("*Generating the code might go slow if you are using low power resources *")

if "prev_option" not in st.session_state:
    st.session_state.prev_option = selected_model

if st.session_state.prev_option != selected_model:
    st.session_state.messages = []
    st.session_state.conversation_history = []
    st.session_state.prev_option = selected_model

repo_id = model_links[selected_model]

st.subheader(f'{selected_model}')

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

if prompt := st.chat_input(f"Hi I'm {selected_model}, How can I help you today?"):
    custom_instruction = "Act like a Human in conversation"
    with st.chat_message("user"):
        st.markdown(prompt)
    
    st.session_state.messages.append({"role": "user", "content": prompt})
    st.session_state.conversation_history.append(("user", prompt))

    formatted_text = format_prompt(prompt, st.session_state.conversation_history, custom_instruction)

    with st.chat_message("assistant"):
        client = InferenceClient(model=model_links[selected_model])
        output = client.text_generation(formatted_text, temperature=temp_values, max_new_tokens=3000, stream=True)
        response = "".join([chunk for chunk in output])
        st.markdown(response)
    
    st.session_state.messages.append({"role": "assistant", "content": response})
    st.session_state.conversation_history.append(("assistant", response))