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import streamlit as st | |
from huggingface_hub import InferenceClient | |
import os | |
import fitz # PyMuPDF | |
st.title("ChatGPT-like Chatbot") | |
base_url = "https://api-inference.huggingface.co/models/" | |
API_KEY = os.environ.get('HUGGINGFACE_API_KEY') | |
headers = {"Authorization": "Bearer " + str(API_KEY)} | |
model_links = { | |
"Mistral-7B": base_url + "mistralai/Mistral-7B-Instruct-v0.2", | |
"Mistral-22B": base_url + "mistral-community/Mixtral-8x22B-v0.1", | |
} | |
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'}, | |
"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'} | |
} | |
def format_prompt(message, custom_instructions=None): | |
prompt = "" | |
if custom_instructions: | |
prompt += f"[INST] {custom_instructions} [/INST]" | |
prompt += f"[INST] {message} [/INST]" | |
return prompt | |
def reset_conversation(): | |
st.session_state.conversation = [] | |
st.session_state.messages = [] | |
return None | |
def read_pdf(file_path): | |
doc = fitz.open(file_path) | |
text = "" | |
for page in doc: | |
text += page.get_text() | |
return text | |
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 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.session_state.prev_option = selected_model | |
reset_conversation() | |
repo_id = model_links[selected_model] | |
st.subheader(f'AI - {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"]) | |
pdf_path = st.file_uploader("Upload a PDF file", type="pdf") | |
if pdf_path: | |
pdf_text = read_pdf(pdf_path) | |
if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"): | |
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}) | |
formatted_prompt = format_prompt(prompt, custom_instruction) | |
with st.chat_message("assistant"): | |
with st.spinner("Thinking..."): | |
client = InferenceClient(model=repo_id, token=API_KEY) | |
if pdf_path: | |
# If a PDF is uploaded, use its text for answering | |
formatted_prompt = f"{pdf_text}\n\n{formatted_prompt}" | |
response = client.text_generation( | |
formatted_prompt, | |
max_length=500, | |
temperature=temp_values | |
) | |
response_content = response["generated_text"] | |
st.markdown(response_content) | |
st.session_state.messages.append({"role": "assistant", "content": response_content}) |