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| import os | |
| from dotenv import load_dotenv | |
| from scrapegraphai.graphs import SmartScraperGraph | |
| from scrapegraphai.utils import prettify_exec_info | |
| from langchain_community.llms import HuggingFaceEndpoint | |
| from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings | |
| import gradio as gr | |
| import subprocess | |
| # Ensure Playwright installs required browsers and dependencies | |
| subprocess.run(["playwright", "install"]) | |
| #subprocess.run(["playwright", "install-deps"]) | |
| # Load environment variables | |
| load_dotenv() | |
| HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN') | |
| # Initialize the model instances | |
| #repo_id = "mistralai/Mistral-7B-Instruct-v0.2" | |
| repo_id = "Qwen/Qwen2.5-72B-Instruct" | |
| llm_model_instance = HuggingFaceEndpoint( | |
| repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN | |
| ) | |
| embedder_model_instance = HuggingFaceInferenceAPIEmbeddings( | |
| api_key=HUGGINGFACEHUB_API_TOKEN, model_name="sentence-transformers/all-MiniLM-l6-v2" | |
| ) | |
| graph_config = { | |
| "llm": { | |
| "model_instance": llm_model_instance, | |
| "model_tokens": 100000, | |
| }, | |
| "embeddings": {"model_instance": embedder_model_instance} | |
| } | |
| def scrape_and_summarize(prompt, source): | |
| smart_scraper_graph = SmartScraperGraph( | |
| prompt=prompt, | |
| source=source, | |
| config=graph_config | |
| ) | |
| result = smart_scraper_graph.run() | |
| exec_info = smart_scraper_graph.get_execution_info() | |
| return result, prettify_exec_info(exec_info) | |
| # Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Scrape websites, no-code version") | |
| gr.Markdown("""Easily scrape and summarize web content using advanced AI models on the Hugging Face Hub without writing any code. Input your desired prompt and source URL to get started. | |
| This is a no-code version of the excellent lib [ScrapeGraphAI](https://github.com/VinciGit00/Scrapegraph-ai). | |
| It's a basic demo and a work in progress. Please contribute to it to make it more useful!""") | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_dropdown = gr.Textbox(label="Model", value="Qwen/Qwen2.5-72B-Instruct") | |
| prompt_input = gr.Textbox(label="Prompt", value="List all the press releases with their headlines and urls.") | |
| source_input = gr.Textbox(label="Source URL", value="https://www.whitehouse.gov/") | |
| scrape_button = gr.Button("Scrape and Summarize") | |
| with gr.Column(): | |
| result_output = gr.Textbox(label="Result") | |
| exec_info_output = gr.Textbox(label="Execution Info") | |
| scrape_button.click( | |
| scrape_and_summarize, | |
| inputs=[prompt_input, source_input], | |
| outputs=[result_output, exec_info_output] | |
| ) | |
| # Launch the Gradio app | |
| if __name__ == "__main__": | |
| demo.launch() |