Switched back to Resume Roasting

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.gradio/certificate.pem

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README.md

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----
-title: Resume Roaster
-emoji: ⚡
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.15.0
-app_file: main.py
-pinned: false
-tags:
-- smolagents
-- agent
-- smolagent
-- tool
-- agent-course
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,68 +0,0 @@
-from dotenv import load_dotenv
-import os
-from smolagents import CodeAgent, HfApiModel
-from smolagents.tools import Tool
-import yaml
-
-# Load environment variables from .env in the root
-load_dotenv()
-
-# Retrieve the Hugging Face token from the environment
-hf_token = os.getenv("HF_TOKEN")
-
-class FinalAnswerTool(Tool):
-    name = "final_answer"
-    description = "Use this tool to provide your final answer"
-    inputs = {
-        "answer": {
-            "type": "string",
-            "description": "The final answer to the problem"
-        }
-    }
-    output_type = "string"
-
-    def forward(self, answer: str) -> str:
-        return answer
-
-class LinkedInScraperTool(Tool):
-    name = "linkedin_scraper"
-    description = "Scrapes LinkedIn profiles to extract professional information"
-    inputs = {
-        "linkedin_url": {
-            "type": "string",
-            "description": "The URL of the LinkedIn profile"
-        }
-    }
-    output_type = "object"
-
-    def forward(self, linkedin_url: str):
-        # Dummy implementation; replace with actual scraping logic
-        return {
-            "experience": "10 years in industry",
-            "skills": "Python, AI",
-            "description": "Experienced professional with a robust background in technology."
-        }
-
-def create_agent():
-    final_answer = FinalAnswerTool()
-    linkedin_scraper = LinkedInScraperTool()
-    
-    model = HfApiModel(
-        max_tokens=2096,
-        temperature=0.5,
-        model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
-        custom_role_conversions=None,
-    )
-
-    with open("prompts.yaml", 'r') as stream:
-        prompt_templates = yaml.safe_load(stream)
-        
-    agent = CodeAgent(
-        model=model,
-        tools=[linkedin_scraper, final_answer],
-        max_steps=6,
-        verbosity_level=1,
-        prompt_templates=prompt_templates
-    )
-    
-    return agent

main.py

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-import gradio as gr
-from app import create_agent
-
-def roast_profile(linkedin_url):
-    agent = create_agent()
-    response = agent.run(
-        f"Scrape this LinkedIn profile: {linkedin_url} and create a humorous but not mean-spirited roast based on their experience, skills, and description. Keep it professional and avoid personal attacks."
-    )
-    return response
-
-demo = gr.Interface(
-    fn=roast_profile,
-    inputs=gr.Textbox(label="LinkedIn Profile URL"),
-    outputs=gr.Textbox(label="Roast Result"),
-    title="LinkedIn Profile Roaster",
-    description="Enter a LinkedIn profile URL and get a humorous professional roast!",
-    examples=[["https://www.linkedin.com/in/example-profile"]]
-)
-
-if __name__ == "__main__":
-    demo.launch(share=True)

prompts.yaml

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-system_prompt: |
-  You are a witty professional roaster who analyzes LinkedIn profiles.
-  Your job is to create humorous but not mean-spirited roasts based on people's professional experiences.
-  Focus on gentle teasing about common LinkedIn behaviors like:
-  - Overuse of buzzwords
-  - Lengthy job titles
-  - Humble brags
-  - Excessive use of emojis
-  - Connection collecting
-  Avoid personal attacks or inappropriate content.
-
-task_prompt: |
-  Using the provided LinkedIn profile information, create a humorous roast that:
-  1. References specific details from their profile
-  2. Keeps the tone light and professional
-  3. Focuses on common LinkedIn behaviors and professional quirks
-  4. Avoids mean-spirited or personal attacks
-  5. Would be appropriate to share in a professional setting
-
-final_answer:
-  pre_messages: "Here is your final roast:"
-  post_messages: ""

requirements.txt

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-gradio
-langchain
-PyYAML
-PyMuPDF
-transformers
-smolagents

tools/LinkedInScraperTool.py

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-from bs4 import BeautifulSoup
-import requests
-from typing import Dict
-from smolagents.tools import Tool
-
-class LinkedInScraperTool(Tool):
-    name = "linkedin_scraper"
-    description = "Scrapes LinkedIn profiles to extract professional information"
-    inputs = {"linkedin_url": str}
-    outputs = dict
-
-    def __call__(self, linkedin_url: str) -> dict:
-        try:
-            # Add headers to mimic a browser request
-            headers = {
-                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
-            }
-            
-            response = requests.get(linkedin_url, headers=headers)
-            soup = BeautifulSoup(response.text, 'html.parser')
-            
-            # Extract profile information
-            profile_data = {
-                'name': self._extract_name(soup),
-                'headline': self._extract_headline(soup),
-                'about': self._extract_about(soup),
-                'experience': self._extract_experience(soup),
-                'education': self._extract_education(soup),
-                'skills': self._extract_skills(soup)
-            }
-            
-            return profile_data
-            
-        except Exception as e:
-            return {"error": f"Failed to scrape profile: {str(e)}"}
-    
-    def _extract_name(self, soup):
-        name_element = soup.find('h1', {'class': 'text-heading-xlarge'})
-        return name_element.text.strip() if name_element else "Name not found"
-    
-    def _extract_headline(self, soup):
-        headline_element = soup.find('div', {'class': 'text-body-medium'})
-        return headline_element.text.strip() if headline_element else "Headline not found"
-    
-    def _extract_about(self, soup):
-        about_element = soup.find('div', {'class': 'pv-about-section'})
-        return about_element.text.strip() if about_element else "About section not found"
-    
-    def _extract_experience(self, soup):
-        experience_elements = soup.find_all('li', {'class': 'experience-item'})
-        experience = []
-        for exp in experience_elements:
-            title_element = exp.find('h3', {'class': 'experience-title'})
-            company_element = exp.find('p', {'class': 'experience-company'})
-            if title_element and company_element:
-                experience.append({
-                    'title': title_element.text.strip(),
-                    'company': company_element.text.strip()
-                })
-        return experience if experience else ["Experience not found"]
-    
-    def _extract_education(self, soup):
-        education_elements = soup.find_all('li', {'class': 'education-item'})
-        education = []
-        for edu in education_elements:
-            school_element = edu.find('h3', {'class': 'education-school'})
-            degree_element = edu.find('p', {'class': 'education-degree'})
-            if school_element and degree_element:
-                education.append({
-                    'school': school_element.text.strip(),
-                    'degree': degree_element.text.strip()
-                })
-        return education if education else ["Education not found"]
-    
-    def _extract_skills(self, soup):
-        skills_elements = soup.find_all('span', {'class': 'skill-name'})
-        return [skill.text.strip() for skill in skills_elements] if skills_elements else ["Skills not found"]