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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
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