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import os
import sys
import gradio.inputs as gr_inputs
import gradio.outputs as gr_outputs
import gradio as gr
from pinecone import Pinecone, ServerlessSpec
from langchain_community.llms import Replicate
from langchain_pinecone import PineconeVectorStore
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_huggingface.embeddings import HuggingFaceEmbeddings
from langchain.chains import ConversationalRetrievalChain
import time
key1 = os.environ.get('REPLICATE_API_TOKEN')
key2 = os.environ.get('PINECONE_API_KEY')
os.environ['REPLICATE_API_TOKEN'] = key1
os.environ["PINECONE_API_KEY"] = key2
# Initialize Pinecone
pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
# Function to process PDF and set up chatbot
def process_pdf(pdf_doc):
# Save uploaded file
filename = pdf_doc.name
pdf_doc.save(filename)
# Load PDF and create index
loader = PyPDFLoader(filename)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings()
index_name = "pdfchatbot"
existing_indexes = [index_info["name"] for index_info in pc.list_indexes()]
if index_name in existing_indexes:
pc.delete_index(index_name)
while index_name in [index_info["name"] for index_info in pc.list_indexes()]:
time.sleep(1)
pc.create_index(
name=index_name,
dimension=768,
metric="cosine",
spec=ServerlessSpec(cloud="aws", region="us-east-1"),
)
while not pc.describe_index(index_name).status["ready"]:
time.sleep(1)
index = pc.Index(index_name)
vectordb = PineconeVectorStore.from_documents(texts, embeddings, index_name=index_name)
llm = Replicate(
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
input={"temperature": 0.75, "max_length": 3000}
)
global qa_chain
qa_chain = ConversationalRetrievalChain.from_llm(
llm,
vectordb.as_retriever(search_kwargs={'k': 2}),
return_source_documents=True
)
return "Ready"
# Function to handle user queries
def query(history, text):
langchain_history = [(msg[1], history[i+1][1] if i+1 < len(history) else "") for i, msg in enumerate(history) if i % 2 == 0]
result = qa_chain({"question": text, "chat_history": langchain_history})
new_history = history + [(text,result['answer'])]
return new_history, ""
# Define the Gradio interface
css = """
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
"""
title_html = """
<div style="text-align: center;max-width: 700px;">
<h1>Chat with PDF</h1>
"""
iface = gr.Interface(
fn=process_pdf,
inputs=gr_inputs.File(label="Load a PDF", type="file", accept=".pdf"),
outputs=gr_outputs.Textbox(label="Status", type="auto", default=""),
title="PDF Chatbot Interface",
description="Upload a PDF file to start interacting with the chatbot.",
allow_flagging=False,
css=css
)
# Add chat history and question input to the interface
chatbot_interface = gr.Interface(
fn=query,
inputs=gr_inputs.Textbox(label="Question", placeholder="Type your question and hit Enter"),
outputs=gr_outputs.Textbox(label="Chat History", type="auto", default=""),
title=title_html,
live=True,
css=css
)
# Launch the combined interface
iface.launch()
chatbot_interface.launch()
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