app.py

import asyncio
import nest_asyncio
from langchain_community.agent_toolkits.playwright.toolkit import PlayWrightBrowserToolkit
from langchain_community.tools.playwright.utils import create_async_playwright_browser
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
import gradio as gr

# Allow nested async calls.
nest_asyncio.apply()

async def extract_reviews(url):
    # Create an asynchronous browser instance using Playwright.
    async_browser = create_async_playwright_browser()

    # Get the browser toolkit which provides utility functions.
    toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=async_browser)
    tools = toolkit.get_tools()

    # Create a dictionary for accessing tools by their name.
    tools_by_name = {tool.name: tool for tool in tools}
    navigate_tool = tools_by_name["navigate_browser"]
    get_elements_tool = tools_by_name["get_elements"]

    # Navigate to the Amazon product reviews URL.
    await navigate_tool.arun({"url": url})

    # Extract reviews from the webpage using the provided selector.
    elements = await get_elements_tool.arun({"selector": ".review", "attributes": ["innerText"]})
    
    # Close the browser after extraction.
    await async_browser.close()

    return elements

async def summarize_reviews(url, openai_api_key):
    reviews = await extract_reviews(url)
    
    # Define the template for the prompt.
    prompt_template = """
    From the reviews delimited by ```
    Provide a detailed summary of the reviews to find the pros and cons of the product. 
    Simplify the words used in the reviews and provide more information about the product, 
    including its features, functionality, and performance. Also, mention the brand name and 
    give an overview of what the product is about. Additionally, provide an overall rating 
    score from 1 (very poor) to 5 (best) based on the reviews.

    ```
    {reviews}
    ```
    """
    
    # Initialize the prompt template.
    prompt = PromptTemplate(template=prompt_template, input_variables=["reviews"])
    
    # Initialize the OpenAI Chat model.
    llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo", openai_api_key=openai_api_key)
    
    # Create an extraction chain using the schema and the Chat model.
    chain = LLMChain(llm=llm, prompt=prompt)
    
    # Get the summarized results.
    summary = chain.run(reviews=reviews)
    
    return summary

async def main(url, openai_api_key):
    summary = await summarize_reviews(url, openai_api_key)
    return summary

# Gradio Interface
def gradio_interface(openai_api_key, url):
    summary = asyncio.run(main(url, openai_api_key))
    return summary

iface = gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Textbox(lines=1, placeholder="Enter OpenAI API Key Here..."), 
        gr.Textbox(lines=2, placeholder="Enter Website URL Here...")
    ],
    outputs="text",
    title="Product Review Summarizer",
    description="Input the OpenAI API key and product URL to extract and summarize reviews.",
    live=False,
    allow_flagging="never"
)

if __name__ == "__main__":
    iface.launch()