Update app.py

app.py

@@ -18,7 +18,6 @@ class AutonomousEmailAgent:
         self.experiences = []
         self.company_info = None
         self.role_description = None
-        self.company_url = None
         self.attempts = 0  # Counter for iterations
 
     # Fetch LinkedIn data via Proxycurl
@@ -41,10 +40,20 @@ class AutonomousEmailAgent:
                 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"]
+                print("Error: Unable to fetch LinkedIn profile. Status Code:", response.status_code)
+                self.use_default_profile()
+
+    # Set default profile information if LinkedIn scraping fails
+    def use_default_profile(self):
+        print("Using default profile values.")
+        self.bio = "A professional with a versatile background and extensive experience."
+        self.skills = ["Leadership", "Communication", "Problem-solving"]
+        self.experiences = [{"title": "Project Manager"}, {"title": "Team Leader"}]
+
+    # Main loop following ReAct pattern
+    def run(self):
+        self.fetch_linkedin_data()
+        return self.autonomous_reasoning()
 
     # Reason and Act via LLM: Let the LLM control reasoning and actions dynamically
     def autonomous_reasoning(self):
@@ -60,13 +69,10 @@ class AutonomousEmailAgent:
         - Candidate's Bio: {self.bio}
         - Candidate's Skills: {', '.join(self.skills)}
         - Candidate's Experiences: {', '.join([exp['title'] for exp in self.experiences])}
-        - Company Information: {self.company_info}
-        - Role Description: {self.role_description}
-        
+
         Based on this data, decide if it is sufficient to generate the email. If some information is missing or insufficient, respond with:
-        1. "scrape" to fetch more data from the company website.
-        2. "generate_email" to proceed with the email generation.
-        3. "fallback" to use default values.
+        1. "generate_email" to proceed with the email generation using available data.
+        2. "fallback" to use default values.
 
         After generating the email, reflect on whether the content aligns with the role and company and whether any improvements are needed. Respond clearly with one of the above options.
         """
@@ -86,7 +92,7 @@ class AutonomousEmailAgent:
             "Content-Type": "application/json"
         }
         data = {
-            "model": "llama-3.1-70b-versatile",  # Model name
+            "model": "llama-3.1-70b-versatile",
             "messages": [{"role": "user", "content": prompt}]
         }
         response = requests.post("https://api.groq.com/openai/v1/chat/completions", headers=headers, json=data)
@@ -94,10 +100,8 @@ class AutonomousEmailAgent:
         print(f"Status Code: {response.status_code}")
         if response.status_code == 200:
             try:
-                result = response.json()  # Parse the response as JSON
-                print(f"LLM Response: {json.dumps(result, indent=2)}")  # Print the full response for debugging
-
-                # Check if 'choices' and the content are correctly structured in the response
+                result = response.json()
+                print(f"LLM Response: {json.dumps(result, indent=2)}")
                 choices = result.get("choices", [])
                 if choices and "message" in choices[0]:
                     content = choices[0]["message"]["content"]
@@ -110,110 +114,46 @@ class AutonomousEmailAgent:
                 print("Error: Response from Groq Cloud LLM is not valid JSON.")
                 return "Error: Response is not in JSON format."
         else:
-            print(f"Error: Unable to connect to Groq Cloud LLM. Status Code: {response.status_code}, Response: {response.text}")
+            print(f"Error: Unable to connect to Groq Cloud LLM. Status Code: {response.status_code}")
             return "Error: Unable to generate response."
 
     # Function to act on the LLM's structured instructions
     def act_on_llm_instructions(self, reasoning_output):
-        print(f"LLM Instruction: {reasoning_output}")  # Print the LLM's instruction for debugging
+        print(f"LLM Instruction: {reasoning_output}")
         instruction = reasoning_output.lower().strip()
 
-        if "scrape" in instruction:
-            if self.attempts >= 5:
-                print("Max attempts reached. Proceeding with fallback option.")
-                return self.generate_fallback_email()
-            self.attempts += 1
-            self.fetch_company_url()
-            if self.company_url:
-                self.fetch_company_info_with_firecrawl(self.company_url)
-            return self.autonomous_reasoning()
-
-        elif "generate_email" in instruction:
+        if "generate_email" in instruction:
             return self.generate_email()
 
-        elif "fallback" in instruction or self.attempts >= 5:
+        elif "fallback" in instruction:
             print("Action: Using fallback values for missing data.")
-            if not self.company_info:
-                self.company_info = "A leading company in its field."
-            if not self.role_description:
-                self.role_description = f"The role of {self.role} involves leadership and team management."
             return self.generate_email()
 
         else:
             print("Error: Unrecognized instruction from LLM. Proceeding with available data.")
             return self.generate_email()
 
-    # Fetch company URL using SERP API
-    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
-    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
+    # Generate email based on the collected data
     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}.
+        print("Generating email based on the provided and/or fallback data...")
+        email_content = f"""
+        Subject: Application for {self.role} at {self.company_name}
+        
+        Dear Hiring Manager,
 
-        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.
+        I am excited to apply for the {self.role} role at {self.company_name}. With a strong background in {self.bio}, I believe my skills in {', '.join(self.skills)} would make me a valuable addition to your team.
 
-        - LinkedIn bio: {self.bio}
-        - Skills: {', '.join(self.skills)}
-        - Experience: {', '.join([exp['title'] for exp in self.experiences])}
-        - Company information: {self.company_info}
+        Please find my LinkedIn profile for more details: {self.linkedin}
 
-        Signature:
         Best regards,
         {self.user_name}
         Email: {self.email}
         Phone: {self.phone}
         LinkedIn: {self.linkedin}
-
-        Limit the email to {self.word_limit} words.
         """
+        return email_content
 
-        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
+# Gradio UI setup remains unchanged
 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")