app.py

# Import necessary libraries
import nest_asyncio
import gradio as gr
import requests
from huggingface_hub import InferenceClient
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.document_loaders import TextLoader
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import AsyncChromiumLoader
from langchain.document_loaders import TextLoader
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import AsyncChromiumLoader

# Apply nest_asyncio for asynchronous operations in environments like Jupyter notebooks
nest_asyncio.apply()

# Initialize the InferenceClient with the specified model
client = InferenceClient(
    "mistralai/Mistral-7B-Instruct-v0.1"
)

# Set up a prompt template for the model (customize as needed)
prompt_template = PromptTemplate()

# Define the list of articles to index
articles = [
    "https://www.fantasypros.com/2023/11/rival-fantasy-nfl-week-10/",
    "https://www.fantasypros.com/2023/11/5-stats-to-know-before-setting-your-fantasy-lineup-week-10/",
    "https://www.fantasypros.com/2023/11/nfl-week-10-sleeper-picks-player-predictions-2023/",
    "https://www.fantasypros.com/2023/11/nfl-dfs-week-10-stacking-advice-picks-2023-fantasy-football/",
    "https://www.fantasypros.com/2023/11/players-to-buy-low-sell-high-trade-advice-2023-fantasy-football/"
]

# Scrapes the blogs above
loader = AsyncChromiumLoader(articles)
docs = loader.load()

# Converts HTML to plain text 
html2text = Html2TextTransformer()
docs_transformed = html2text.transform_documents(docs)

# Chunk text
text_splitter = CharacterTextSplitter(chunk_size=100, 
                                      chunk_overlap=10)
chunked_documents = text_splitter.split_documents(docs_transformed)

# Load chunked documents into the FAISS index
db = FAISS.from_documents(chunked_documents, 
                          HuggingFaceEmbeddings(model_name='sentence-transformers/all-mpnet-base-v2'))

retriever = db.as_retriever()

# Create the RAG chain by combining the language model with the retriever
rag_chain = ({"context": retriever} | LLMChain)

# Define the generation function for the Gradio interface
def generate(
    prompt, history, temperature=0.7, max_new_tokens=256, top_p=0.95, repetition_penalty=1.1,
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = "<s>"
    for user_prompt, bot_response in history:
        formatted_prompt += f"[INST] {user_prompt} [/INST]"
        formatted_prompt += f" {bot_response}</s> "
    formatted_prompt += f"[INST] {prompt} [/INST]"

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text
        yield output
    return output

# Define additional input components for the Gradio interface
additional_inputs = [
    gr.Slider(
        label="Temperature",
        value=0.7,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=256,
        minimum=0,
        maximum=1024,
        step=64,
        interactive=True,
        info="The maximum number of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.95,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.1,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )
]

# Define CSS for styling the Gradio interface
css = """
  #mkd {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""

# Create the Gradio interface with the chat component
with gr.Blocks(css=css) as demo:
    gr.HTML("<h1><center>Mistral 7B Instruct<h1><center>")
    gr.HTML("<h3><center>In this demo, you can chat with <a href='https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1'>Mistral-7B-Instruct</a> model. 📜<h3><center>")
    gr.HTML("<h3><center>Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚<h3><center>")
    gr.ChatInterface(
        generate,
        additional_inputs=additional_inputs,
        examples=[["What is the secret to life?"], ["Write me a recipe for pancakes."]],
    )

# Launch the Gradio interface with debugging enabled
demo.queue().launch(debug=True)