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import hvplot.pandas
import numpy as np
import panel as pn
import pandas as pd
import openai
from llama_index import VectorStoreIndex, download_loader
from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from langchain.chains.conversation.memory import ConversationBufferMemory
from panel.chat import ChatInterface
import time
pn.extension("perspective")
def callback(contents: str, user: str, instance: pn.chat.ChatInterface):
message = f"Echoing {user}: {contents}"
return message
chat_interface = pn.chat.ChatInterface(callback=callback)
msg_panel = chat_interface.send(
"Enter a WEB link and ask anything!-\nNote: images in the link will be ignored!!!",
user="assistant",
respond=False,
)
apikey = pn.widgets.TextInput(name='OPENAI API KEY', placeholder="sk-********")
apply = pn.widgets.Button(name='Apply', button_type='default')
website_url_input = pn.widgets.TextInput(name='Website URL', placeholder="https://www.google.com/")
submit = pn.widgets.Button(name='Submit', button_type='primary')
def on_submit(event, contents, ):
try:
SimpleWebPageReader = download_loader("SimpleWebPageReader")
# Set OpenAI API key
openai.api_key = apikey.value # Replace with your actual API key
# Get the entered website URL
website_url = website_url_input.value
if website_url:
# Initialize SimpleWebPageReader with the provided website URL
loader = SimpleWebPageReader()
documents = loader.load_data(urls=[website_url])
# Create VectorStoreIndex from documents
index = VectorStoreIndex.from_documents(documents)
# Initialize LangChain OpenAI
index = VectorStoreIndex.from_documents(documents)
llm = OpenAI(openai_api_key=apikey.value, temperature=0, streaming = True
)
# Initialize ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history")
# Initialize agent chain
tools = [
Tool(
name="Website Index",
func=lambda q: index.as_query_engine(),
description="Useful when you want to answer questions about the text on websites.",
),
]
query_engine = index.as_query_engine()
response = query_engine.query(contents)
return str(response),
except Exception as e:
print(f"Error: {e}")
def even_or_odd(contents, user, instance):
response_tuple = on_submit(event='', contents=contents)
# Extracting the first element of the tuple and converting it to a string
response_string = str(response_tuple)
return response_string
# Set the callback function for the button click event
submit.on_click(on_submit)
# Instantiate the template with widgets displayed in the sidebar
template = pn.template.FastListTemplate(
title='Chat with Web',
sidebar=[apikey,website_url_input, submit,
msg_panel],
header=[],
)
ChatInterface(callback=even_or_odd)
def callback(contents: str, user: str, instance: pn.chat.ChatInterface):
message = query_engine.query(contents)
return message
template.main.append(
ChatInterface(
callback=even_or_odd,
user="User",
avatar="🧑",
callback_user="System",
)
)
# Display the app
template.servable() |