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'''
Taken directly from : https://huggingface.co/spaces/Sagar23p/mistralAI_chatBoat/tree/main
'''
import streamlit as st
from huggingface_hub import InferenceClient
import os
import sys
st.title("ChatGPT-like Chatbot")
base_url="https://api-inference.huggingface.co/models/"
API_KEY = os.environ.get('HUGGINGFACE_API_KEY')
# print(API_KEY)
# headers = {"Authorization":"Bearer "+API_KEY}
model_links ={
"Mistral-7B":base_url+"mistralai/Mistral-7B-Instruct-v0.2",
"Mistral-22B":base_url+"mistral-community/Mixtral-8x22B-v0.1",
# "Gemma-2B":base_url+"google/gemma-2b-it",
# "Zephyr-7B-β":base_url+"HuggingFaceH4/zephyr-7b-beta",
# "Llama-2":"meta-llama/Llama-2-7b-chat-hf"
}
#Pull info about the model to display
model_info ={
"Mistral-7B":
{'description':"""The Mistral model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
\nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-7b/) team as has over **7 billion parameters.** \n""",
'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'},
"Mistral-22B":
{'description':"""The Mistral model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
\nIt was created by the [**Mistral AI**](https://mistral.ai/news/announcing-mistral-22b/) team as has over **22 billion parameters.** \n""",
'logo':'https://mistral.ai/images/logo_hubc88c4ece131b91c7cb753f40e9e1cc5_2589_256x0_resize_q97_h2_lanczos_3.webp'}
# "Gemma-7B":
# {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
# \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over **7 billion parameters.** \n""",
# 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'},
# "Gemma-2B":
# {'description':"""The Gemma model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
# \nIt was created by the [**Google's AI Team**](https://blog.google/technology/developers/gemma-open-models/) team as has over **2 billion parameters.** \n""",
# 'logo':'https://pbs.twimg.com/media/GG3sJg7X0AEaNIq.jpg'},
# "Zephyr-7B":
# {'description':"""The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
# \nFrom Huggingface: \n\
# Zephyr is a series of language models that are trained to act as helpful assistants. \
# [Zephyr 7B Gemma](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1)\
# is the third model in the series, and is a fine-tuned version of google/gemma-7b \
# that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""",
# 'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1/resolve/main/thumbnail.png'},
# "Zephyr-7B-β":
# {'description':"""The Zephyr model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
# \nFrom Huggingface: \n\
# Zephyr is a series of language models that are trained to act as helpful assistants. \
# [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)\
# is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 \
# that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO)\n""",
# 'logo':'https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.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()]
# Create the sidebar with the dropdown for model selection
selected_model = st.sidebar.selectbox("Select Model", models)
#Create a temperature slider
temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
#Add reset button to clear conversation
st.sidebar.button('Reset Chat', on_click=reset_conversation) #Reset button
# Create model description
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 may be inaccurate or false.*")
st.sidebar.markdown("\nLearn how to build this chatbot by original author of this chatbot [here](https://ngebodh.github.io/projects/2024-03-05/).")
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()
#Pull in the model we want to use
repo_id = model_links[selected_model]
st.subheader(f'AI - {selected_model}')
# st.title(f'ChatBot Using {selected_model}')
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Accept user input
if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):
custom_instruction = "Act like a Human in conversation"
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
formated_text = format_promt(prompt, custom_instruction)
# Display assistant response in chat message container
with st.chat_message("assistant"):
client = InferenceClient(
model=model_links[selected_model],)
# headers=headers)
output = client.text_generation(
formated_text,
temperature=temp_values,#0.5
max_new_tokens=3000,
stream=True
)
response = st.write_stream(output)
st.session_state.messages.append({"role": "assistant", "content": response})
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