"""
Web Scraper and Summarizer using Mistral AI.

This module provides a Gradio-based web application for scraping websites
and summarizing content using the Mistral AI language model. It allows users
to input a prompt and a source URL, then generates a JSON output of the
scraped and summarized information.

Developer: Vicky_111
LinkedIn: https://www.linkedin.com/in/itz-me-vicky111/
"""

import os
import json
import subprocess
from typing import Dict, Any

import gradio as gr
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

# Ensure Playwright installs required browsers and dependencies
subprocess.run(["playwright", "install"], check=True)
# 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
)
# Embed using Hugging face interferance embedding
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}
}

# Using smart scraper graph the content is scrapped and summarised
def scrape_and_summarize(prompt: str, source: str) -> Dict[str, Any]:
    """
    Scrape a website and summarize its content based on a given prompt.

    This function uses the SmartScraperGraph to scrape the provided URL
    and generate a summary based on the given prompt. It ensures the output
    is in a valid JSON format.

    Args:
        prompt (str): The prompt to guide the scraping and summarization.
        source (str): The URL of the website to scrape.

    Returns:
        Dict[str, Any]: A JSON-formatted dictionary containing the scraped
        and summarized information.

    Raises:
        ValueError: If the output cannot be parsed as valid JSON.
    """
    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):
        return result

    try:
        return 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:
                return 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


# Gradio User 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, i.e., the items you want to scrape from the website <br>
    2. Provide the URL for the site you want to scrape, click Generate<br>
    And BOOM 💥 you can copy the result and view the execution details in the right side panel
    """)

    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]
    )


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