Create app.py

app.py

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+import streamlit as st
+import requests
+from groq import Groq
+
+# Function to scrape LinkedIn profile using Firecrawl API
+def scrape_linkedin_profile(linkedin_url):
+    api_url = "https://api.firecrawl.dev/v1/scrape"  # Endpoint for scraping
+    headers = {"Authorization": "Bearer YOUR_FIRECRAWL_API_KEY"}  # Replace with your actual API key
+    response = requests.post(api_url, json={"url": linkedin_url}, headers=headers)
+    
+    if response.status_code == 200:
+        return response.json()['data']  # Adjust based on actual response structure
+    else:
+        return "Error scraping LinkedIn profile."
+
+# 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 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()