app.py

import gradio as gr
import requests
import language_tool_python
from bs4 import BeautifulSoup
import os

# Load Groq Cloud API key from Hugging Face secrets
groq_api_key = os.getenv("GROQ_CLOUD_API_KEY")

# Scraping function to fetch user public data (for demo purposes, we will simulate this)
def fetch_public_data(name, dob, city):
    # Here, you could implement logic to fetch public data from sources like LinkedIn, GitHub, etc.
    # For simplicity, we will return dummy data to simulate a successful fetch.
    # You can implement web scraping or API integration to fetch real data.
    # Example scraping code can go here (or via LinkedIn API, etc.)
    
    bio = f"{name} is a software engineer from {city} with over 10 years of experience. Known for work in AI, cloud computing, and leadership in various engineering teams."
    return bio

# Helper function to call Groq Cloud LLM API to generate email
def generate_email_from_groq(bio, company_name, role):
    url = "https://api.groq.cloud/generate"  # Adjust based on Groq's API documentation
    headers = {
        "Authorization": f"Bearer {groq_api_key}",
        "Content-Type": "application/json",
    }
    prompt = f"Write a professional email applying for a {role} position at {company_name}. Use this bio: {bio}. The email should include an introduction, relevant experience, skills, and a closing."
    data = {
        "model": "groq-model",  # Adjust the model name based on Groq documentation
        "prompt": prompt,
        "max_tokens": 300
    }
    
    response = requests.post(url, headers=headers, json=data)
    
    if response.status_code == 200:
        return response.json().get("choices")[0].get("text").strip()
    else:
        return "Error generating email. Please check your API key or try again later."

# Grammar and Tone Checker Function
def check_grammar(email_text):
    tool = language_tool_python.LanguageTool('en-US')
    matches = tool.check(email_text)
    corrected_text = language_tool_python