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""" Simple Chatbot
Y.Steiner : March 2024
"""
import streamlit as st
from openai import OpenAI
import os
import sys
from dotenv import load_dotenv, dotenv_values
import pandas as pd
load_dotenv()
# read csv from user
def read_csv_file(file):
"""Reads the contents of a CSV file."""
if not file:
return None
data = []
try:
with file.streamlit_file_uploader("Choose a CSV file", type="csv") as uploaded_file:
for row in uploaded_file.getvalue().decode("utf-8").splitlines():
data.append(row.split(","))
except Exception as e:
st.error(f"Error reading CSV file: {e}")
return None
return data
uploaded_file = st.sidebar.file_uploader("Choose a CSV file", type=["csv"])
# Check if a file was uploaded
if uploaded_file is not None:
# Read the CSV file
df = pd.read_csv(uploaded_file)
# Show the contents of the CSV file in the main area
st.write(df)
else:
# Hide everything else until a file is uploaded
st.warning('Upload a CSV file to get started.')
# initialize the client
client = OpenAI(
base_url="https://api-inference.huggingface.co/v1",
api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN')#"hf_xxx" # Replace with your token
)
#Create supported models
model_links ={
"Mistral":"mistralai/Mistral-7B-Instruct-v0.2",
"Gemma-7B":"google/gemma-7b-it",
"Gemma-2B":"google/gemma-2b-it",
"Zephyr-7B-β":"HuggingFaceH4/zephyr-7b-beta",
# "Llama-2":"meta-llama/Llama-2-7b-chat-hf"
}
#Pull info about the model to display
model_info ={
"Mistral":
{'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'},
"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 reset_conversation():
'''
Resets Conversation
'''
st.session_state.conversation = []
st.session_state.messages = []
return None
# Define the available models
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, (0.1))
#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.*")
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}')
# Set a default model
if selected_model not in st.session_state:
st.session_state[selected_model] = model_links[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"):
# 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})
# Display assistant response in chat message container
with st.chat_message("assistant"):
stream = client.chat.completions.create(
model=model_links[selected_model],
messages=[
{"role": m["role"], "content": m["content"]}
for m in st.session_state.messages
],
temperature=temp_values,#0.5,
stream=True,
max_tokens=3000,
)
response = st.write_stream(stream)
st.session_state.messages.append({"role": "assistant", "content": response}) |