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import streamlit as st
import requests
from groq import Groq
# Function to scrape LinkedIn profile using Proxycurl API
def scrape_linkedin_profile(linkedin_url):
api_url = "https://nubela.co/proxycurl/api/v2/linkedin" # Endpoint for Proxycurl API
api_key = "Y5xigulf0B1C_wr20seh8g" # Replace with your actual Proxycurl API key
headers = {
"Authorization": f"Bearer {api_key}" # Correctly formatted Authorization header
}
params = {
"url": linkedin_url,
"use_cache": "if-present"
}
response = requests.get(api_url, headers=headers, params=params)
if response.status_code == 200:
return response.json() # Return the JSON response containing profile data
else:
return f"Error scraping LinkedIn profile: {response.text}" # Improved error message
# Function to generate email using Llama 3.2 from Groq with ReAct methodology
def generate_email(name, email, phone, role, tokens, linkedin_url):
reasoning_trace = []
# Reasoning step: Initiate profile scrape
reasoning_trace.append(f"Initiating profile scrape for {name}.")
profile_data = scrape_linkedin_profile(linkedin_url)
if isinstance(profile_data, str) and "Error" in profile_data:
return profile_data # Return error message if scraping fails
# Reasoning step: Profile data obtained
reasoning_trace.append(f"Obtained profile data: {profile_data}.")
# Initialize Groq client
client = Groq(api_key='YOUR_GROQ_API_KEY') # Replace with your actual Groq API key
# Prepare messages for Llama model
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": f"Generate an email for {name} applying for {role}."},
{"type": "text", "text": f"Email: {email}, Phone: {phone}, Profile Data: {profile_data}"}
]
}
]
# Call to Llama model
completion = client.chat.completions.create(
model="llama-3.2-90b-vision-preview",
messages=messages,
max_tokens=tokens,
temperature=1,
top_p=1,
stream=False,
stop=None
)
# Reasoning step: Email generated
reasoning_trace.append("Email content generated.")
email_body = completion['choices'][0]['message']['content']
# Combine email content with reasoning trace
return f"Email Content:\n{email_body}\n\nReasoning Trace: {'; '.join(reasoning_trace)}"
# Streamlit app layout
def main():
st.title("LinkedIn Profile Scraper and Email Generator")
# Input fields for user data
name = st.text_input("Name of the Sender")
email = st.text_input("Email Address of the Sender")
phone = st.text_input("Phone Number of the Sender")
linkedin_url = st.text_input("LinkedIn Profile URL")
role = st.text_input("Role Applying For")
tokens = st.number_input("Number of Tokens", min_value=1, max_value=500, value=50)
# Button to scrape LinkedIn profile and generate email
if st.button("Scrape Profile and Generate Email"):
if name and email and phone and linkedin_url and role and tokens:
email_content = generate_email(name, email, phone, role, tokens, linkedin_url)
st.subheader("Generated Email:")
st.write(email_content)
else:
st.error("Please fill in all fields.")
# Run the app
if __name__ == '__main__':
main() |