app.py

import streamlit as st
from huggingface_hub import InferenceClient
import os
import sys

st.title("SmallZOO ChatBot 3B")

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

model_links ={ 
    "Llama-3.2 [3B]":base_url+"meta-llama/Llama-3.2-3B-Instruct",
    "Qwen2.5 [3B]":base_url+"Qwen/Qwen2.5-3B-Instruct",
    "Phi-3.5 [3.82B]":base_url+"microsoft/Phi-3.5-mini-instruct"
}

model_info ={
    "Llama-3.2 [3B]":
        {'description':"""The Llama-3.2 3B Instruct model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
            \nA SLM (Large Language Model) is best for applications requiring fast response times, low resource consumption, and specific, narrow tasks. \n""",
        'logo':'logos/Meta.png',
        'url':'https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct'},

    "Qwen2.5 [3B]":
        {'description':"""The Qwen2.5 3B Instruct model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
            \nA SLM (Large Language Model) is best for applications requiring fast response times, low resource consumption, and specific, narrow tasks. \\n""",
        'logo':'logos/Qwen.png',
        'url':'https://huggingface.co/Qwen/Qwen2.5-3B-Instruct'},

      "Phi-3.5 [3.82B]":
         {'description':"""The Phi-3.5 mini instruct model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
          \nA SLM (Large Language Model) is best for applications requiring fast response times, low resource consumption, and specific, narrow tasks. \ \n""",
         'logo':'logos/ms.png',
         'url':'https://huggingface.co/microsoft/Phi-3.5-mini-instruct'},
}

def reset_conversation():
    '''
    Resets Conversation
    '''
    st.session_state.conversation = []
    st.session_state.messages = []
    return None

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

selected_model = st.sidebar.selectbox(
    "Select Model",
    models,
    help="""Choose your AI model:

 • Llama-3.2: Balanced & versatile

 • Qwen2.5: Strong in reasoning

 • Phi-3.5: Good at technical tasks"""
)

temp_value = st.sidebar.slider(
    'Select a temperature value', 
    0.0, 
    1.0, 
    (0.5),
    help="""Controls randomness in responses:

  0 = focused/deterministic

 0.5 = balanced

  1 = more creative/random"""
)

custom_instructions = st.sidebar.text_area(
    "Custom System Instructions",
    value="You are helpful assistant, act like a Human in conversation. Keep answers very short and in English only!",
    help="Customize how the AI should behave"
)

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(f"[View model on 🤗 Hugging Face]({model_info[selected_model]['url']})")
st.sidebar.markdown("*Generated content can be outdated, inaccurate, offensive or non-factual!!!*")

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.write(f"Changed to {selected_model}")
    st.session_state.prev_option = selected_model
    reset_conversation()


repo_id = model_links[selected_model]

st.subheader(f'{selected_model}')
# st.title(f'ChatBot Using {selected_model}')


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


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?"):

    with st.chat_message("user"):
        st.markdown(prompt)

    st.session_state.messages.append({"role": "system", "content": custom_instructions})

    st.session_state.messages.append({"role": "user", "content": prompt})

    with st.chat_message("assistant"):
        client = InferenceClient(
            model=model_links[selected_model],)

        try:
            
            output = client.text_generation(
                prompt,
                temperature=temp_value,#0.5
                max_new_tokens=3000,
                stream=True
            )

            # Create a placeholder for the streaming response
            message_placeholder = st.empty()
            full_response = ""

            # Stream the response and accumulate it
            for chunk in output:
                if isinstance(chunk, dict) and "generated_text" in chunk:
                    text_chunk = chunk["generated_text"]
                elif isinstance(chunk, str):
                    text_chunk = chunk
                else:
                    continue
                    
                full_response += text_chunk
                message_placeholder.markdown(full_response + "▌")
            
            # Display final response and store it
            message_placeholder.markdown(full_response)
            st.session_state.messages.append({"role": "assistant", "content": full_response})
        except Exception as e:
            st.error(f"Error: {str(e)}")