Add application file

.env

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+OPENAI_API_KEY =  'sk-proj-aBuDB2BxH30A7ityocWiT3BlbkFJS0mf2ammDemVDNcQ97gp'

app.py

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+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 os
+from dotenv import load_dotenv, find_dotenv
+import gradio as gr
+
+# Load environment variables from the .env file.
+load_dotenv(find_dotenv())
+
+# 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):
+    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=os.getenv("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):
+    summary = await summarize_reviews(url)
+    return summary
+
+# Gradio Interface
+def gradio_interface(url):
+    summary = asyncio.run(main(url))
+    return summary
+
+
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Textbox(lines=2, placeholder="Enter Website URL Here..."),
+    outputs="text",
+    title="Product Review Summarizer",
+    description="Input the product URL to extract and summarize reviews.",
+    live=True,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch(debug=True)