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app.py

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+# Import necessary libraries
+import nest_asyncio
+import gradio as gr
+import requests
+from bs4 import BeautifulSoup
+from huggingface_hub import InferenceClient
+from langchain.chains import RAGChain, RunnablePassthrough, LLMChain
+from langchain.retrievers import FaissRetriever
+from langchain.prompts import PromptTemplate
+from langchain.wrappers import HuggingFacePipeline
+from langchain.indexing import AsyncChromiumLoader, Html2TextTransformer, CharacterTextSplitter, FAISS, HuggingFaceEmbeddings
+
+# 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, "question": RunnablePassthrough()} | 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)

requirements.txt

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+gradio==2.1.4
+requests==2.26.0
+beautifulsoup4==4.10.0
+huggingface-hub==0.0.17
+nest-asyncio==1.5.1
+sentence-transformers==2.1.0
+torch==1.9.0
+transformers==4.11.3
+langchain==0.6.2