Create app.py

app.py

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+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 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="html",
+    title="Personalized Email Agent",
+    description="Generate highly personalized sales emails for AdTech prospects.",
+)
+
+if __name__ == "__main__":
+    iface.launch(share=True)