Update app.py

app.py

@@ -20,6 +20,31 @@ class AutonomousEmailAgent:
         self.role_description = None
         self.company_url = None
 
+    # Fetch LinkedIn data via Proxycurl
+    def fetch_linkedin_data(self):
+        proxycurl_api_key = os.getenv("PROXYCURL_API_KEY")
+        if not self.linkedin_url:
+            print("Action: No LinkedIn URL provided, using default bio.")
+            self.bio = "A professional with diverse experience."
+            self.skills = ["Adaptable", "Hardworking"]
+            self.experiences = ["Worked across various industries"]
+        else:
+            print("Action: Fetching LinkedIn data via Proxycurl.")
+            headers = {"Authorization": f"Bearer {proxycurl_api_key}"}
+            url = f"https://nubela.co/proxycurl/api/v2/linkedin?url={self.linkedin_url}"
+            response = requests.get(url, headers=headers)
+            if response.status_code == 200:
+                data = response.json()
+                self.bio = data.get("summary", "No bio available")
+                self.skills = data.get("skills", [])
+                self.experiences = data.get("experiences", [])
+                print("LinkedIn data fetched successfully.")
+            else:
+                print("Error: Unable to fetch LinkedIn profile. Using default bio.")
+                self.bio = "A professional with diverse experience."
+                self.skills = ["Adaptable", "Hardworking"]
+                self.experiences = ["Worked across various industries"]
+
     # Reason and Act via LLM: Let the LLM control reasoning and actions dynamically
     def autonomous_reasoning(self):
         print("Autonomous Reasoning: Letting the LLM fully reason and act on available data...")
@@ -113,14 +138,77 @@ class AutonomousEmailAgent:
             print("Error: Unrecognized instruction from LLM. Proceeding with available data.")
             return self.generate_email()
 
-    # Other methods (fetch_linkedin_data, fetch_company_url, fetch_company_info_with_firecrawl, generate_email) remain unchanged...
+    # Fetch company URL using SERP API (same as before)
+    def fetch_company_url(self):
+        serp_api_key = os.getenv("SERP_API_KEY")
+        print(f"Fetching company URL for {self.company_name} using SERP API...")
+        serp_url = f"https://serpapi.com/search.json?q={self.company_name}&api_key={serp_api_key}&num=1"
+        response = requests.get(serp_url)
+        
+        if response.status_code == 200:
+            serp_data = response.json()
+            if 'organic_results' in serp_data and len(serp_data['organic_results']) > 0:
+                self.company_url = serp_data['organic_results'][0]['link']
+                print(f"Found company URL: {self.company_url}")
+            else:
+                print("No URL found for the company via SERP API.")
+                self.company_url = None
+        else:
+            print(f"Error fetching company URL: {response.status_code}")
+
+    # Fetch company information via Firecrawl API using company URL (same as before)
+    def fetch_company_info_with_firecrawl(self, company_url):
+        firecrawl_api_key = os.getenv("FIRECRAWL_API_KEY")
+        print(f"Fetching company info for {company_url} using Firecrawl.")
+        headers = {"Authorization": f"Bearer {firecrawl_api_key}"}
+        firecrawl_url = "https://api.firecrawl.dev/v1/scrape"
+        data = {"url": company_url, "patterns": ["description", "about", "careers", "company overview"]}
+        
+        response = requests.post(firecrawl_url, json=data, headers=headers)
+        if response.status_code == 200:
+            firecrawl_data = response.json()
+            self.company_info = firecrawl_data.get("description", "No detailed company info available.")
+            print(f"Company info fetched: {self.company_info}")
+        else:
+            print(f"Error: Unable to fetch company info via Firecrawl. Status code: {response.status_code}")
+            self.company_info = "A leading company in its field."
+
+    # Final Action: Generate the email using Groq Cloud LLM (same as before)
+    def generate_email(self):
+        print("Action: Generating the email using Groq Cloud LLM with the gathered information.")
+
+        prompt = f"""
+        Write a professional job application email applying for the {self.role} position at {self.company_name}.
+
+        The email should follow the "Start with Why" approach:
+        1. **Why**: Explain why the candidate is passionate about this role and company.
+        2. **How**: Highlight the candidate’s skills and experiences.
+        3. **What**: Provide examples of past achievements.
+        4. **Call to Action**: Request a meeting or discussion.
+
+        - LinkedIn bio: {self.bio}
+        - Skills: {', '.join(self.skills)}
+        - Experience: {', '.join([exp['title'] for exp in self.experiences])}
+        - Company information: {self.company_info}
+
+        Signature:
+        Best regards,
+        {self.user_name}
+        Email: {self.email}
+        Phone: {self.phone}
+        LinkedIn: {self.linkedin}
+
+        Limit the email to {self.word_limit} words.
+        """
+
+        return self.send_request_to_llm(prompt)
 
     # Main loop following ReAct pattern
     def run(self):
         self.fetch_linkedin_data()
         return self.autonomous_reasoning()
 
-# Gradio UI setup remains the same as before
+# Gradio UI setup remains the same
 def gradio_ui():
     name_input = gr.Textbox(label="Your Name", placeholder="Enter your name")
     company_input = gr.Textbox(label="Company Name or URL", placeholder="Enter the company name or website URL")