Resume Hosting :)

.gitattributes

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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

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+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
+PyPDF2
+PyYAML
+smolagents
+python-dotenv

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"]

tools/resumescraper.py

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+from smolagents.tools import Tool
+
+class ResumeScraperTool(Tool):
+    name = "resume_scraper"
+    description = (
+        "Parses a resume (in plain text) to extract key sections such as Summary, "
+        "Experience, Education, and Skills. This tool expects the resume text to include "
+        "section headers like 'Summary:', 'Experience:', 'Education:', and 'Skills:'."
+    )
+    inputs = {
+        "resume_text": {
+            "type": "string",
+            "description": "The plain text of the resume"
+        }
+    }
+    output_type = "object"
+
+    def forward(self, resume_text: str) -> dict:
+        # Basic extraction using simple markers; in a real-world case, you might want to use NLP.
+        sections = {
+            "summary": "Summary not found",
+            "experience": "Experience not found",
+            "education": "Education not found",
+            "skills": "Skills not found"
+        }
+        lower_text = resume_text.lower()
+
+        if "summary:" in lower_text:
+            start = lower_text.index("summary:")
+            # Assume the section ends at the next double newline or end of text
+            end = lower_text.find("\n\n", start)
+            sections["summary"] = resume_text[start + len("summary:"): end].strip() if end != -1 else resume_text[start + len("summary:"):].strip()
+
+        if "experience:" in lower_text:
+            start = lower_text.index("experience:")
+            end = lower_text.find("\n\n", start)
+            sections["experience"] = resume_text[start + len("experience:"): end].strip() if end != -1 else resume_text[start + len("experience:"):].strip()
+
+        if "education:" in lower_text:
+            start = lower_text.index("education:")
+            end = lower_text.find("\n\n", start)
+            sections["education"] = resume_text[start + len("education:"): end].strip() if end != -1 else resume_text[start + len("education:"):].strip()
+
+        if "skills:" in lower_text:
+            start = lower_text.index("skills:")
+            end = lower_text.find("\n\n", start)
+            sections["skills"] = resume_text[start + len("skills:"): end].strip() if end != -1 else resume_text[start + len("skills:"):].strip()
+
+        return sections