app.py

import os
import requests
from bs4 import BeautifulSoup
import gradio as gr
from groq import Groq

# Initialize Groq client
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

# Function to extract content from a URL
def extract_content(url):
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        paragraphs = soup.find_all('p')
        content = ' '.join([para.get_text() for para in paragraphs])
        return content[:2000]  # Limit content to 2000 characters to avoid overload
    except Exception as e:
        return f"Error extracting content from {url}: {str(e)}"

# Function to fetch LinkedIn profile insights using Proxycurl API
def fetch_linkedin_insights(profile_url):
    api_key = os.environ.get("PROXYCURL_API_KEY")
    api_endpoint = "https://nubela.co/proxycurl/api/v2/linkedin"
    headers = {"Authorization": f"Bearer {api_key}"}
    params = {"url": profile_url, "fallback_to_cache": "on-error"}

    try:
        response = requests.get(api_endpoint, headers=headers, params=params, timeout=10)
        response.raise_for_status()
        profile_data = response.json()
        insights = f"{profile_data.get('headline', '')}. {profile_data.get('summary', '')}"
        return insights
    except Exception as e:
        return f"Error fetching LinkedIn insights: {str(e)}"

# Function to generate email using Llama
def generate_email(name, linkedin_url, website_url, context_url, word_count):
    # Fetch insights from LinkedIn and reference URLs
    linkedin_insights = fetch_linkedin_insights(linkedin_url)
    website_content = extract_content(website_url)
    context_content = extract_content(context_url) if context_url else ""

    # Fetch details from AdTech company website
    adtech_content = extract_content("https://www.abcd.com")

    # Construct the prompt for Llama
    prompt = f"""
    You are an AI assistant helping an AdTech company draft personalized sales emails.
    Here are the details of the prospect:
    - Name: {name}
    - LinkedIn Insights: {linkedin_insights}
    - Website Content: {website_content}
    - Additional Context: {context_content}

    The company provides the following offerings:
    {adtech_content}

    Draft a personalized email addressing the prospect's specific needs and pain points.
    Focus on highlighting only relevant solutions from the company offerings.
    Ensure the email is professional, engaging, and stays within {word_count} words (5-10% flexibility).
    Output the email in HTML format.
    """

    # Generate email using Llama
    try:
        chat_response = client.chat.completions.create(
            messages=[{"role": "user", "content": prompt}],
            model="llama3-8b-8192",
        )
        email_content = chat_response.choices[0].message.content
        return email_content
    except Exception as e:
        return f"Error generating email: {str(e)}"

# Gradio Interface
def email_agent(name, linkedin_url, website_url, context_url, word_count):
    return "<div style='font-style: italic;'>Hold on tight, your personalized email is on the way...</div>", generate_email(name, linkedin_url, website_url, context_url, word_count)

iface = gr.Interface(
    fn=email_agent,
    inputs=[
        gr.Textbox(label="Prospect's Name"),
        gr.Textbox(label="LinkedIn Profile URL"),
        gr.Textbox(label="Publishing Website URL"),
        gr.Textbox(label="Additional Context URL (optional)"),
        gr.Slider(label="Email Length (words)", minimum=150, maximum=500, step=10, value=300),
    ],
    outputs=[
        gr.HTML(label="Status"),
        gr.HTML(label="Generated Email")
    ],
    title="Personalized Email Agent",
    description="Generate highly personalized sales emails for AdTech prospects."
)

iface.launch(share=True)