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import gradio as gr
from langchain.llms import Replicate
from langchain.vectorstores import Pinecone
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import ConversationalRetrievalChain
from datasets import load_dataset
import os
import pinecone
key = os.environ.get('API')
yeh = os.environ.get('pineapi')
os.environ["REPLICATE_API_TOKEN"] = key
pinecone.init(api_key=yeh, environment='gcp-starter')
import sentence_transformers
import faiss
def loading_pdf():
return "Loading..."
def pdf_changes(pdf_doc):
loader = PyPDFLoader(pdf_doc.name)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings()
index_name = "chatbot"
index = pinecone.Index(index_name)
vectordb = Pinecone.from_documents(texts, embeddings, index_name=index_name)
llm = Replicate(
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
input={"temperature": 0.2, "max_length": 3000, "length_penalty":1.5, "num_beams":3}
)
global qa
qa = ConversationalRetrievalChain.from_llm(
llm,
vectordb.as_retriever(search_kwargs={'k': 2}),
return_source_documents=True
)
return "Ready"
def text(history, text):
result = qa({'question': text, 'chat_history': history})
history.append((text, result['answer']))
return history,""
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>
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
with gr.Column():
pdf_doc = gr.File(label="Load a PDF", file_types=['.pdf'], type="file")
load_pdf = gr.Button("Load PDF")
langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False)
chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350)
question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
submit_btn = gr.Button("Send message")
load_pdf.click(pdf_changes, inputs=[pdf_doc], outputs=[langchain_status], queue=False)
question.submit(text, [chatbot, question], [chatbot, question])
submit_btn.click(text, [chatbot, question], [chatbot, question])
demo.launch()
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