import gradio as gr
import requests
from gingerit.gingerit import GingerIt  # Import GingerIt for grammar correction
import os
from datetime import datetime  # Import datetime for date validation

# Initialize GingerIt parser
ginger_parser = GingerIt()

# Load Groq Cloud API key securely from environment variables
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):
    # Simulate fetched bio data for demonstration purposes
    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."
    
    # Simulate LinkedIn profile scraping (replace this with actual scraping logic)
    linkedin_profile = f"https://linkedin.com/in/{name.replace(' ', '').lower()}"
    
    return bio, linkedin_profile

# Helper function to call Groq Cloud LLM API to generate email
def generate_email_from_groq(bio, company_name, role):
    url = "https://api.groq.com/openai/v1/chat/completions"  # Updated API URL for Groq Cloud
    headers = {
        "Authorization": f"Bearer {groq_api_key}",  # Use the API key securely from environment
        "Content-Type": "application/json",
    }
    
    # Updated prompt for Groq Cloud chat completion
    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."
    
    # Construct the data payload for the API request
    data = {
        "messages": [
            {
                "role": "user",
                "content": prompt
            }
        ],
        "model": "llama3-8b-8192"  # Use the appropriate model from Groq Cloud
    }
    
    response = requests.post(url, headers=headers, json=data)
    
    if response.status_code == 200:
        # Extract the generated email content from the API response
        return response.json()["choices"][0]["message"]["content"].strip()
    else:
        # Print or log the error for debugging
        print(f"Error: {response.status_code}, {response.text}")
        return "Error generating email. Please check your API key or try again later."

# Grammar and Tone Checker Function using GingerIt
def check_grammar(email_text):
    corrected_text = ginger_parser.parse(email_text)["result"]
    return corrected_text

# Function to validate the DOB format (DD-MM-YYYY)
def validate_dob(dob):
    try:
        # Attempt to parse the DOB to the expected format
        datetime.strptime(dob, "%d-%m-%Y")
        return True
    except ValueError:
        # Return False if the format is invalid
        return False

# Main function to create the email and allow for saving, editing, or copying
def create_email(name, dob, city, company_name, role, email, phone):
    # Validate the DOB format (DD-MM-YYYY)
    if not validate_dob(dob):
        return "Invalid Date of Birth format. Please use DD-MM-YYYY."

    # Step 1: Fetch public data based on user info
    bio, linkedin_profile = fetch_public_data(name, dob, city)
    
    # Step 2: Generate the email using Groq Cloud LLM
    generated_email = generate_email_from_groq(bio, company_name, role)
    
    # Step 3: Add the user's email, phone number, and LinkedIn profile to the signature
    signature = f"\n\nBest regards,\n{name}\nEmail: {email}\nPhone: {phone}\nLinkedIn: {linkedin_profile}"

    # Step 4: Run grammar and tone check using GingerIt
    polished_email = check_grammar(generated_email + signature)

    # Return the final polished email with the signature
    return polished_email

# Define interface with Gradio
def gradio_ui():
    # Define inputs
    name_input = gr.Textbox(label="Name", placeholder="Enter your name")
    dob_input = gr.Textbox(label="Date of Birth", placeholder="Enter your date of birth (DD-MM-YYYY)")
    city_input = gr.Textbox(label="City", placeholder="Enter your city of residence")
    
    company_name_input = gr.Textbox(label="Company Name", placeholder="Enter the name of the company you are applying to")
    role_input = gr.Textbox(label="Role", placeholder="Enter the role you are applying for")
    
    email_input = gr.Textbox(label="Email Address", placeholder="Enter your email address")
    phone_input = gr.Textbox(label="Phone Number", placeholder="Enter your phone number")
    
    # Define output for the generated email
    email_output = gr.Textbox(label="Generated Email", placeholder="Your generated email will appear here", lines=10)
    
    # Create the Gradio interface
    demo = gr.Interface(
        fn=create_email,  # Function to call when the user submits
        inputs=[name_input, dob_input, city_input, company_name_input, role_input, email_input, phone_input],
        outputs=[email_output],
        title="Email Writing AI Agent",
        description="Generate a professional email for a job application by providing your basic info.",
        allow_flagging="never"  # Disable flagging
    )
    
    # Launch the Gradio app
    demo.launch()

# Start the Gradio app when running the script
if __name__ == "__main__":
    gradio_ui()