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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
import json
# 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"
llm_model_instance = HuggingFaceEndpoint(
repo_id=repo_id, max_length=128, temperature=0.3, 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},
"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()
# Ensure the result is properly formatted as JSON
if isinstance(result, dict):
result_json = result
else:
try:
result_json = json.loads(result)
except json.JSONDecodeError as e:
# Attempt to extract JSON from the result
start_index = result.find("[")
end_index = result.rfind("]")
if start_index != -1 and end_index != -1:
json_str = result[start_index:end_index+1]
try:
result_json = json.loads(json_str)
except json.JSONDecodeError as inner_e:
raise ValueError(f"Invalid JSON output: {result}") from inner_e
else:
raise ValueError(f"Invalid JSON output: {result}") from e
return result_json
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("<h1>Websites Scraper using Mistral AI</h1>")
gr.Markdown("""This is a no code ML app for scraping <br> 1. Just provide the Prompt, ie., the items you wanna Scrap from the website <br> 2. Provide the url for the site you wanna Scrap, click Generate<br> And BOOM 💥 you can copy the result and view the execution details in the right side pannel """)
with gr.Row():
with gr.Column():
prompt_input = gr.Textbox(label="Prompt", value="List me all the hospital or clinic names and their opening closing time, if the mobile number is present provide it too.")
source_input = gr.Textbox(label="Source URL", value="https://www.yelp.com/biz/all-smiles-dental-san-francisco-5?osq=dentist")
scrape_button = gr.Button("Generate")
with gr.Column():
result_output = gr.JSON(label="Result")
scrape_button.click(
scrape_and_summarize,
inputs=[prompt_input, source_input],
outputs=[result_output]
)
# Launch the Gradio app
if __name__ == "__main__":
demo.launch() |