app.py

import gradio as gr

from langchain.document_loaders import WebBaseLoader

from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=350, chunk_overlap=10)

from langchain.llms import HuggingFaceHub
model_id = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.1, "max_new_tokens":300})

from langchain.embeddings import HuggingFaceHubEmbeddings
embeddings = HuggingFaceHubEmbeddings()

from langchain.vectorstores import Chroma

from langchain.chains import RetrievalQA

web_links = ["https://www.databricks.com/","https://help.databricks.com","https://databricks.com/try-databricks","https://help.databricks.com/s/","https://docs.databricks.com","https://kb.databricks.com/","http://docs.databricks.com/getting-started/index.html","http://docs.databricks.com/introduction/index.html","http://docs.databricks.com/getting-started/tutorials/index.html","http://docs.databricks.com/release-notes/index.html","http://docs.databricks.com/ingestion/index.html","http://docs.databricks.com/exploratory-data-analysis/index.html","http://docs.databricks.com/data-preparation/index.html","http://docs.databricks.com/data-sharing/index.html","http://docs.databricks.com/marketplace/index.html","http://docs.databricks.com/workspace-index.html","http://docs.databricks.com/machine-learning/index.html","http://docs.databricks.com/sql/index.html","http://docs.databricks.com/delta/index.html","http://docs.databricks.com/dev-tools/index.html","http://docs.databricks.com/integrations/index.html","http://docs.databricks.com/administration-guide/index.html","http://docs.databricks.com/security/index.html","http://docs.databricks.com/data-governance/index.html","http://docs.databricks.com/lakehouse-architecture/index.html","http://docs.databricks.com/reference/api.html","http://docs.databricks.com/resources/index.html","http://docs.databricks.com/whats-coming.html","http://docs.databricks.com/archive/index.html","http://docs.databricks.com/lakehouse/index.html","http://docs.databricks.com/getting-started/quick-start.html","http://docs.databricks.com/getting-started/etl-quick-start.html","http://docs.databricks.com/getting-started/lakehouse-e2e.html","http://docs.databricks.com/getting-started/free-training.html","http://docs.databricks.com/sql/language-manual/index.html","http://docs.databricks.com/error-messages/index.html","http://www.apache.org/","https://databricks.com/privacy-policy","https://databricks.com/terms-of-use"]
loader = WebBaseLoader(web_links)
documents = loader.load()
 
texts = text_splitter.split_documents(documents)
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever()
global qa 
qa = RetrievalQA.from_chain_type(llm=model_id, chain_type="stuff", retriever=retriever, return_source_documents=True)


def add_text(history, text):
 history = history + [(text, None)]
 return history, ""

def bot(history):
 response = infer(history[-1][0])
 history[-1][1] = response['result']
 return history

def infer(question):
 
 query = question
 result = qa({"query": query})

 return result

css="""
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""

title = """
<div style="text-align: center;max-width: 700px;">
 <h1>Chat with PDF</h1>
 <p style="text-align: center;">Upload a .PDF from your computer, click the "Load PDF to LangChain" button, <br />
 when everything is ready, you can start asking questions about the pdf ;)</p>
</div>
"""


with gr.Blocks(css=css) as demo:
 with gr.Column(elem_id="col-container"):
 gr.HTML(title) 
 chatbot = gr.Chatbot([], elem_id="chatbot")
 with gr.Row():
 question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
 question.submit(add_text, [chatbot, question], [chatbot, question]).then(
 bot, chatbot, chatbot
 )

demo.launch()