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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':'./Meta.png'},
"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':'./Qwen.png'},
"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':'./ms.png'},
}
def format_promt(message, custom_instructions=None):
prompt = ""
if custom_instructions:
prompt += f"[INST] {custom_instructions} [/INST]"
prompt += f"[INST] {message} [/INST]"
return prompt
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)
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("*Generated content can be 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?"):
custom_instruction = "Act like a Human in conversation, you are helpfull assistant"
with st.chat_message("user"):
st.markdown(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
formated_text = format_promt(prompt, custom_instruction)
with st.chat_message("assistant"):
client = InferenceClient(
model=model_links[selected_model],)
output = client.text_generation(
formated_text,
temperature=temp_values,#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:
full_response += 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})
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