Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+src/projects_images/1726049646844.jpeg filter=lfs diff=lfs merge=lfs -text
+src/projects_images/bi.png filter=lfs diff=lfs merge=lfs -text
+src/projects_images/cast.jpeg filter=lfs diff=lfs merge=lfs -text
+src/projects_images/robot.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,10 @@
+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv

.python-version

@@ -0,0 +1 @@
+3.10

DEPLOYMENT.md

@@ -0,0 +1,86 @@
+# Hugging Face Spaces Deployment Guide
+
+## Files Created for Deployment
+
+1. **app.py** (root) - Main application file configured for Hugging Face Spaces
+2. **requirements.txt** - Minimal dependencies needed for the app
+3. **README.md** - Space metadata and description
+
+## Deployment Steps
+
+### 1. Prepare Your Repository
+
+The current file structure should work. The app.py in the root will automatically detect files in `src/` or root directories.
+
+### 2. Create a Hugging Face Space
+
+1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
+2. Click "Create new Space"
+3. Choose:
+   - **SDK**: Gradio
+   - **Python version**: 3.10 or higher
+   - **Hardware**: CPU (or GPU if needed)
+   - **Visibility**: Public or Private
+
+### 3. Push Your Code
+
+You can either:
+
+**Option A: Using Git**
+```bash
+# Initialize git if not already done
+git init
+git add app.py requirements.txt README.md src/
+git commit -m "Deploy to Hugging Face Spaces"
+git remote add origin https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
+git push -u origin main
+```
+
+**Option B: Using Hugging Face CLI**
+```bash
+pip install huggingface_hub
+huggingface-cli login
+# Then upload files through the web interface or use the API
+```
+
+### 4. Set Environment Variables
+
+1. Go to your Space settings
+2. Navigate to "Variables and secrets"
+3. Add a new secret:
+   - **Name**: `OPENAI_API_KEY`
+   - **Value**: Your OpenAI API key
+
+### 5. Verify File Structure
+
+Make sure these files/folders are in your Space:
+```
+├── app.py
+├── requirements.txt
+├── README.md
+└── src/
+    ├── cv/
+    │   ├── me.txt
+    │   └── avatar.jpeg
+    └── projects_images/
+        ├── s_up.jpeg
+        ├── llm.jpeg
+        ├── bi.png
+        └── robot.png
+```
+
+### 6. Wait for Build
+
+The Space will automatically build and deploy. You can monitor the build logs in the Space interface.
+
+## Troubleshooting
+
+- **File not found errors**: Make sure all files in `src/` are committed and pushed
+- **API key errors**: Verify `OPENAI_API_KEY` is set as a secret (not a variable)
+- **Import errors**: Check that `requirements.txt` includes all necessary packages
+
+## Notes
+
+- The app.py automatically detects file paths (works with both `src/` and root-level files)
+- Make sure `.env` file is NOT committed (it's in .gitignore)
+- The app uses Hugging Face Space secrets for the API key

README.md

@@ -1,12 +1,6 @@
 ---
-title: Cv Bot
-emoji: 🏆
-colorFrom: red
-colorTo: gray
+title: cv-bot
+app_file: app.py
 sdk: gradio
 sdk_version: 6.0.2
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,119 @@
+import gradio as gr
+from openai import OpenAI
+from dotenv import load_dotenv
+import os 
+
+# Try to load .env file if it exists (for local development)
+load_dotenv()
+
+# Initialize OpenAI client - will use OPENAI_API_KEY from environment
+# For Hugging Face Spaces, set this as a secret in the Space settings
+api_key = os.getenv("OPENAI_API_KEY")
+if not api_key:
+    raise ValueError("OPENAI_API_KEY not found. Please set it in your environment variables or Hugging Face Space secrets.")
+
+client = OpenAI(api_key=api_key)
+
+# File paths - adjust based on where the app runs
+# For Hugging Face Spaces, files should be in the root or adjust paths accordingly
+cv_path = "src/cv/me.txt" if os.path.exists("src/cv/me.txt") else "cv/me.txt"
+avatar_path = "src/cv/avatar.jpeg" if os.path.exists("src/cv/avatar.jpeg") else "cv/avatar.jpeg"
+projects_base = "src/projects_images" if os.path.exists("src/projects_images") else "projects_images"
+
+projects = [
+    {"image": f"{projects_base}/s_up.jpeg", "title": "Ai Recommendation System"},
+    {"image": f"{projects_base}/llm.jpeg", "title": "LLM Automation"},
+    {"image": f"{projects_base}/bi.png", "title": "BI"},
+    {"image": f"{projects_base}/robot.png", "title": "Robot Arm Control With Ros Python and AI "},
+]
+
+with open(cv_path, "r") as f: 
+    cv_text = f.read()
+
+system_prompt = f"""
+Your name is Alexander.You are acting as Alexander Todorov. You will answer questions related to your career, skills, work experience, and education. \
+Questions will be asked by visitors, headhunters, or recruiters about potential job opportunities. \
+Respond professionally and use professional language. \
+Answer only questions that are directly related to your CV. If you do not find the answer in your CV, respond with: \
+"I can only answer questions about my CV."
+
+CV: {cv_text}
+With this context, please chat with the user, always staying in character as Alexander Todorov.
+"""
+
+def chat(message, history):
+    messages = [{"role":"system", "content":system_prompt}] + history + [{"role":"user", "content":message}]
+    response = client.chat.completions.create(model="gpt-4o-mini", messages=messages)
+    return response.choices[0].message.content
+
+
+with gr.Blocks() as ui:
+    # name and job title    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown('<div style="font-size:36px; font-weight:bold;">Alexander Todorov</div>')
+
+        with gr.Column(scale=4):    
+            gr.Markdown("""
+            <a href="https://www.linkedin.com/in/alexander-t-50864a139" target="_blank">
+                <img src="https://cdn-icons-png.flaticon.com/512/174/174857.png" 
+                    alt="LinkedIn" style="width:32px; height:32px;"/>
+            </a>
+            """)
+
+    # ********************************************************************************************************* 
+    
+    with gr.Row():
+        with gr.Column(scale=1):  # 1 part
+            gr.Image(avatar_path, 
+                        type="pil", 
+                        show_label=False, 
+                        height=150, 
+                        interactive=False, 
+                        container=False, 
+                        buttons=[['download', 'share', 'fullscreen']])
+
+        # Right column 75% width
+        with gr.Column(scale=3):  # 3 parts
+            gr.Markdown("""
+            <div style="width:50%">
+            <p style="color:#9b9b9b;  margin-bottom:0;font-size:20px;">
+            Software and Data Engineer with over five years of experience delivering intelligent, 
+            user-focused AI solutions and driving automation and innovation in complex environments.
+            </p>
+            </div>
+            """)
+    
+
+    # ******************************************************************************************************************
+    # Chatbot 
+    gr.Markdown(f'<p style="font-size:28px;font-weight:bold;"> Chat with Me About My CV</p>',
+                    elem_id="job-title-light")
+    gr.Markdown('<hr style="border:1px solid grey;">', elem_id="custom_divider")
+    chatbot = gr.Chatbot(placeholder="<strong>Interactive CV Guide</strong><br>Ask Me Anything", height=300)
+    chat_interface = gr.ChatInterface(fn=chat, chatbot=chatbot)
+    gr.Markdown('<hr style="border: 1px solid #d3d3d3; margin-top:12px; margin-bottom:12px;">')
+    
+    # ****************************************************************************************************************
+    # Projects 
+    gr.Markdown(f'<p style="font-size:28px;font-weight:bold;"> Examples of My Work</p>',
+                    elem_id="job-title-light")
+    gr.Markdown('<hr style="border:1px solid grey;">', elem_id="custom_divider")
+    
+    # Projects row 
+    with gr.Row():
+        for project in projects:
+            with gr.Column():  # equal width for each project
+                # Project image
+                gr.Image(project["image"], 
+                            type="pil", 
+                            show_label=False, 
+                            interactive=False, 
+                            height=200, width=350,
+                            buttons=[['download', 'share', 'fullscreen']])
+                # Project title / text
+                gr.Markdown(f"<div style='text-align:center; font-size:16px; margin-top:4px;'>{project['title']}</div>")
+
+
+if __name__ == "__main__":
+    ui.launch()

main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from my-cv-agent-site!")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -0,0 +1,46 @@
+[project]
+name = "my-cv-agent-site"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "anthropic>=0.49.0",
+    "autogen-agentchat>=0.4.9.2",
+    "autogen-ext[grpc,mcp,ollama,openai]>=0.4.9.2",
+    "bs4>=0.0.2",
+    "gradio>=5.22.0",
+    "httpx>=0.28.1",
+    "ipywidgets>=8.1.5",
+    "langchain-anthropic>=0.3.10",
+    "langchain-community>=0.3.20",
+    "langchain-experimental>=0.3.4",
+    "langchain-openai>=0.3.9",
+    "langgraph>=0.3.18",
+    "langgraph-checkpoint-sqlite>=2.0.6",
+    "langsmith>=0.3.18",
+    "lxml>=5.3.1",
+    "mcp-server-fetch>=2025.1.17",
+    "mcp[cli]>=1.5.0",
+    "openai>=1.68.2",
+    "playwright>=1.51.0",
+    "plotly>=6.0.1",
+    "polygon-api-client>=1.14.5",
+    "psutil>=7.0.0",
+    "pypdf>=5.4.0",
+    "pypdf2>=3.0.1",
+    "python-dotenv>=1.0.1",
+    "requests>=2.32.3",
+    "semantic-kernel>=1.25.0",
+    "sendgrid>=6.11.0",
+    "setuptools>=78.1.0",
+    "smithery>=0.1.0",
+    "speedtest-cli>=2.1.3",
+    "wikipedia>=1.4.0",
+    "google-generativeai>=0.8.0",
+]
+
+[dependency-groups]
+dev = [
+    "ipykernel>=6.29.5",
+]

requirements.txt

@@ -0,0 +1,3 @@
+gradio>=5.22.0
+openai>=1.68.2
+python-dotenv>=1.0.1

src/.gradio/certificate.pem

@@ -0,0 +1,31 @@
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+-----END CERTIFICATE-----

src/app.py

@@ -0,0 +1,124 @@
+import gradio as gr
+from openai import OpenAI
+from dotenv import load_dotenv
+import os 
+
+
+projects = [
+            {"image": "projects_images/s_up.jpeg", "title": "Ai Recommendation System"},
+            {"image": "projects_images/llm.jpeg", "title": "LLM Automation"},
+             {"image": "projects_images/bi.png", "title": "BI"},
+             {"image": "projects_images/robot.png", "title": "Robot Arm Control With Ros Python and AI "},
+]
+
+
+load_dotenv()
+
+client = OpenAI()
+
+with open("cv/me.txt", "r") as f: 
+    cv_text = f.read()
+
+system_prompt = f"""
+Your name is Alexander.You are acting as Alexander Todorov. You will answer questions related to your career, skills, work experience, and education. \
+Questions will be asked by visitors, headhunters, or recruiters about potential job opportunities. \
+Respond professionally and use professional language. \
+Answer only questions that are directly related to your CV. If you do not find the answer in your CV, respond with: \
+"I can only answer questions about my CV."
+
+CV: {cv_text}
+With this context, please chat with the user, always staying in character as Alexander Todorov.
+"""
+
+def chat(message,history):
+    messages = [{"role":"system", "content":system_prompt}] + history + [{"role":"user", "content":message}]
+    response = client.chat.completions.create(model="gpt-4o-mini", messages=messages)
+    return response.choices[0].message.content
+
+
+
+with gr.Blocks() as ui:
+
+
+    # name and job title    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown('<div style="font-size:36px; font-weight:bold;">Alexander Todorov</div>')
+
+        with gr.Column(scale=4):    
+            gr.Markdown("""
+            <a href="https://www.linkedin.com/in/alexander-t-50864a139" target="_blank">
+                <img src="https://cdn-icons-png.flaticon.com/512/174/174857.png" 
+                    alt="LinkedIn" style="width:32px; height:32px;"/>
+            </a>
+            """)
+
+
+    # gr.Markdown(f'<p style="color:#9b9b9b;  margin-bottom:0;font-size:20px;">Software Engineer & Data Scientist</p>',
+    #                 elem_id="job-title-light")
+
+       # LinkedIn icon with link
+    
+  
+
+    # ********************************************************************************************************* 
+    
+    with gr.Row():
+        with gr.Column(scale=1):  # 1 part
+                    gr.Image("cv/avatar.jpeg", 
+                                type="pil", 
+                                show_label=False, 
+                                height=150, 
+                                interactive=False, 
+                                container=False, 
+                                buttons=[['download', 'share', 'fullscreen']])
+
+                # Right column 75% width
+        with gr.Column(scale=3):  # 3 parts
+                    gr.Markdown("""
+                    <div style="width:50%">
+                    <p style="color:#9b9b9b;  margin-bottom:0;font-size:20px;">
+                    Software and Data Engineer with over five years of experience delivering intelligent, 
+                    user-focused AI solutions and driving automation and innovation in complex environments.
+                    </p>
+                    </div>
+                    """)
+    
+
+     # ******************************************************************************************************************
+    # Chatbot 
+    gr.Markdown(f'<p style="font-size:28px;font-weight:bold;"> Chat with Me About My CV</p>',
+                    elem_id="job-title-light")
+    gr.Markdown('<hr style="border:1px solid grey;">', elem_id="custom_divider")
+    chatbot = gr.Chatbot(placeholder="<strong>Interactive CV Guide</strong><br>Ask Me Anything", height=300)
+    chat_interface = gr.ChatInterface(fn=chat,  chatbot=chatbot)
+    gr.Markdown('<hr style="border: 1px solid #d3d3d3; margin-top:12px; margin-bottom:12px;">')
+    # horizontal line
+
+    
+    # ****************************************************************************************************************
+    # Projects 
+    gr.Markdown(f'<p style="font-size:28px;font-weight:bold;"> Examples of My Work</p>',
+                    elem_id="job-title-light")
+    gr.Markdown('<hr style="border:1px solid grey;">', elem_id="custom_divider")
+    
+    # Projects row 
+    with gr.Row():
+        for project in projects:
+            with gr.Column():  # equal width for each project
+                # Project image (disable download)
+                gr.Image(project["image"], 
+                            type="pil", 
+                            show_label=False, 
+                            interactive=False, 
+                            height=200, width=350,
+                            buttons=[['download', 'share', 'fullscreen']])
+                # Project title / text
+                gr.Markdown(f"<div style='text-align:center; font-size:16px; margin-top:4px;'>{project['title']}</div>")
+
+
+    
+
+
+if __name__ == "__main__":
+    ui.launch()

src/cv/me.txt

@@ -0,0 +1,152 @@
+Alexander Todorov 
+Email: alex.st.todorov@gmail.com
+Phone: +972523085768
+Hold EU passport. 
+LinkIn https://www.linkedin.com/in/alexander-t-50864a139/
+
+Summary: 
+Software and ML Engineer & Data Scientist with over 5 years in software development, building AI/ML solutions, LLM tools, recommendation systems, and data-driven web applications using Python, Flask, Angular, Spark, and MongoDB. Skilled in microservices, Docker, Kubernetes, CI/CD, NLP, and Azure OpenAI to deliver scalable and automated enterprise solutions. Passionate about creating intelligent, user-centric software that drives innovation and operational efficiency.
+
+
+
+Experience: 
+2020 - Present - Intel Corporation
+Data scientist and Ml Engineer 
+
+Led the development of an enterprise-level AI recommendation platform that analyzes employee skills and activity to recommend internal training courses.
+
+Designed and deployed scalable ML pipelines for recommendation systems, predictive maintenance, and NLP topic modeling, using Python, Spark ALS, Logistic Regression, BERTopic and Azure OpenAI GPT-4.
+
+
+Built cloud-native microservices for LLM-driven using Kafka, Kubernetes, Docker, and MongoDB
+
+
+Engineered automated  CI/CD workflows with GitHub Actions
+
+
+Automated analytics flows and BI data pipelines using containerized Python jobs and Kubernetes CronJobs, supporting real-time and scheduled reporting.
+
+
+Applied advanced NLP and LLM techniques to classify topics, extract meaningful patterns from ticket history, and automate text understanding at scale.
+
+
+2012 - 2020   - Intel Corporation 
+Process Eng - Production process analyses
+Python scripts, SQL , Automated reports development, Angular
+Intel Ireland 2013 - 2017 - Supporting the startup of new technology. Process and equipment 
+
+Education:
+
+2025 - Today - Technical University – Sofia,Bulgaria.
+PhD Program - Robotic and automation systems with AI 
+
+2020–2024 - Technical University – Sofia,Bulgaria.
+Master’s degree in Robotic Engineering. 
+⋄ Final project: Robot ARM design with Python ROS framework
+
+2018-2019 - John Bryce Collage 
+Full Stack - Angular, Java, SQL 
+
+2009–2012 - Singalovsky College Tel-Aviv. 
+Electronic Practical Engineer. 
+
+
+
+Skills: 
+AI: PySpark, Spark ALS, Logistic Regression, BERTopic, Azure OpenAI GPT-4, NLP, LLMs, Recommender Systems, Topic Modeling
+DevOps: Docker, Kubernetes, Cloud Foundry, GitHub Actions, Kafka, CI/CD, Microservices
+Programming: Python, Flask, FastAPI, Angular, MongoDB, SQL
+DB : Mongo DB, SQL , PostGres 
+
+
+Languages
+Hebrew – Fluent
+English – Advanced; used daily in an international corporate environment
+Bulgarian – Native
+Russian – Native
+
+Hobbies: 
+Swimming , drones, snowboarding 
+
+
+
+Projects: 
+
+AI Recommendation System
+Led the development of an enterprise-level AI recommendation platform that analyzes employee skills and activity to recommend internal training courses.
+ Technologies: Python, Angular 18, MongoDB, Spark ALS, Cloud Foundry, Azure SSO, NLP, Docker
+Designed and managed the end-to-end system architecture across backend and frontend components.
+Implemented a recommendation engine using ALS on Apache Spark.
+Integrated Azure SSO for secure user identification and behavior tracking.
+Automated personalized email notifications based on work schedules and recommendation results.
+Deployed the solution on an on-premise Cloud Foundry environment.
+
+
+
+ML Preventive Maintenance System
+Developed a machine-learning model to predict maintenance needs for manufacturing equipment using historical operational data.
+Technologies: Python, Logistic Regression, Pandas, Scikit-learn
+Built a predictive analytics pipeline to estimate the next maintenance cycle.
+Enabled early warning for failures and improved production reliability.
+
+
+
+BI Automated Reporting & Workflow Orchestration
+Built automated BI data flows and reporting pipelines based on production and operational data.
+ Technologies: Python, Docker, Kubernetes (Rancher), CronJobs, Angular, Power BI
+Created containerized Python services to compute metrics and generate reports.
+Deployed workloads as Kubernetes Deployments and CronJobs on Rancher.
+Provided dashboards in Angular or Power BI for management visibility.
+
+
+NLP / ML Ticket Topic Modeling
+Implemented NLP-based topic modeling to detect recurring issues in ticket history.
+Technologies: BERTopic, Python, NLP, Scikit-learn
+Automated detection of common problem categories to support root-cause analysis.
+Delivered insights that improved support efficiency and reduced repeated issues.
+
+
+
+LLM Microservices for PDF Data Extraction
+Designed a distributed LLM system to extract structured data from large PDF documents.
+Technologies: Python, PyPDF, Azure OpenAI GPT-4, Kafka, Kubernetes, Microservices, MongoDB
+Built a microservices architecture for document scanning, chunking, and LLM interaction.
+Used Azure OpenAI GPT-4 for high-accuracy extraction and summarization.
+Implemented asynchronous communication through Kafka.
+Presented at the internal AI Summit, gaining organizational recognition.
+
+
+CI/CD for Web Applications (GitHub Actions)
+Developed automated CI/CD pipelines for Angular-based applications.
+Technologies: GitHub Actions, Angular, Docker, Kubernetes
+Automated build steps: Angular build → Docker build → Push → Kubernetes deployment.
+Enabled rapid and reliable releases for production systems.
+
+Full-Stack Podcast Web Application
+Built a full end-to-end platform for uploading, managing, and playing audio content.
+Technologies: Angular, Python Flask, MongoDB, Docker
+Implemented user interface for playback and an admin panel for file management.
+Added support for metadata, thumbnails, and user comments.
+Designed a scalable backend with REST APIs and MongoDB.
+
+
+
+Issues Ticketing System (ServiceNow + Custom Frontend)
+Developed a complete ticket management system integrated with ServiceNow.
+Technologies: JavaScript, Angular, ServiceNow, Kubernetes, Azure SSO
+Created dynamic ticket forms, routing logic, and an Angular-based landing page.
+Integrated Azure SSO for authentication and user tracking.
+Implemented automated reporting on ticket types, frequency, and recurring issues.
+
+
+
+
+
+
+
+
+
+
+
+
+

src/cv_chat.ipynb

@@ -0,0 +1,274 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7daa2ba7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "from openai import OpenAI\n",
+    "from dotenv import load_dotenv\n",
+    "import os "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7de6ebda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "projects = [\n",
+    "            {\"image\": \"projects_images/s_up.jpeg\", \"title\": \"Ai Recommendation System\"},\n",
+    "            {\"image\": \"projects_images/llm.jpeg\", \"title\": \"LLM Automation\"},\n",
+    "             {\"image\": \"projects_images/bi.png\", \"title\": \"BI\"},\n",
+    "             {\"image\": \"projects_images/robot.png\", \"title\": \"Robot Arm Control With Ros Python and AI \"},\n",
+    "]\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8e38d667",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "load_dotenv()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7708eac4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"cv/me.txt\", \"r\") as f: \n",
+    "    cv_text = f.read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "34dc7b6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "efd52cc5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_prompt = f\"\"\"\n",
+    "Your name is Alexander.You are acting as Alexander Todorov. You will answer questions related to your career, skills, work experience, and education. \\\n",
+    "Questions will be asked by visitors, headhunters, or recruiters about potential job opportunities. \\\n",
+    "Respond professionally and use professional language. \\\n",
+    "Answer only questions that are directly related to your CV. If you do not find the answer in your CV, respond with: \\\n",
+    "\"I can only answer questions about my CV.\"\n",
+    "\n",
+    "CV: {cv_text}\n",
+    "With this context, please chat with the user, always staying in character as Alexander Todorov.\n",
+    "\"\"\"\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "5e16df81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def chat(message,history):\n",
+    "    messages = [{\"role\":\"system\", \"content\":system_prompt}] + history + [{\"role\":\"user\", \"content\":message}]\n",
+    "    response = client.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
+    "    return response.choices[0].message.content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ad803f77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# --------- CHAT FUNCTION FOR THE AGENT ----------\n",
+    "def chat_with_agent(message, history):\n",
+    "    # Call your Agent here (OpenAI Agent, LangChain, etc.)\n",
+    "    # For now return example text:\n",
+    "    return f\"You said: {message}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f4807389",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7860\n",
+      "* To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with gr.Blocks() as ui:\n",
+    "\n",
+    "\n",
+    "    # name and job title    \n",
+    "    with gr.Row():\n",
+    "        with gr.Column(scale=1):\n",
+    "            gr.Markdown('<div style=\"font-size:36px; font-weight:bold;\">Alexander Todorov</div>')\n",
+    "\n",
+    "        with gr.Column(scale=4):    \n",
+    "            gr.Markdown(\"\"\"\n",
+    "            <a href=\"https://www.linkedin.com/in/alexander-t-50864a139\" target=\"_blank\">\n",
+    "                <img src=\"https://cdn-icons-png.flaticon.com/512/174/174857.png\" \n",
+    "                    alt=\"LinkedIn\" style=\"width:32px; height:32px;\"/>\n",
+    "            </a>\n",
+    "            \"\"\")\n",
+    "\n",
+    "\n",
+    "    # gr.Markdown(f'<p style=\"color:#9b9b9b;  margin-bottom:0;font-size:20px;\">Software Engineer & Data Scientist</p>',\n",
+    "    #                 elem_id=\"job-title-light\")\n",
+    "\n",
+    "       # LinkedIn icon with link\n",
+    "    \n",
+    "  \n",
+    "\n",
+    "    # ********************************************************************************************************* \n",
+    "    \n",
+    "    with gr.Row():\n",
+    "        with gr.Column(scale=1):  # 1 part\n",
+    "                    gr.Image(\"cv/avatar.jpeg\", \n",
+    "                                type=\"pil\", \n",
+    "                                show_label=False, \n",
+    "                                height=150, \n",
+    "                                interactive=False, \n",
+    "                                container=False, \n",
+    "                                buttons=[['download', 'share', 'fullscreen']])\n",
+    "\n",
+    "                # Right column 75% width\n",
+    "        with gr.Column(scale=3):  # 3 parts\n",
+    "                    gr.Markdown(\"\"\"\n",
+    "                    <div style=\"width:50%\">\n",
+    "                    <p style=\"color:#9b9b9b;  margin-bottom:0;font-size:20px;\">\n",
+    "                    Software and Data Engineer with over five years of experience delivering intelligent, \n",
+    "                    user-focused AI solutions and driving automation and innovation in complex environments.\n",
+    "                    </p>\n",
+    "                    </div>\n",
+    "                    \"\"\")\n",
+    "    \n",
+    "\n",
+    "     # ******************************************************************************************************************\n",
+    "    # Chatbot \n",
+    "    gr.Markdown(f'<p style=\"font-size:28px;font-weight:bold;\"> Chat with Me About My CV</p>',\n",
+    "                    elem_id=\"job-title-light\")\n",
+    "    gr.Markdown('<hr style=\"border:1px solid grey;\">', elem_id=\"custom_divider\")\n",
+    "    chatbot = gr.Chatbot(placeholder=\"<strong>Interactive CV Guide</strong><br>Ask Me Anything\", height=300)\n",
+    "    chat_interface = gr.ChatInterface(fn=chat,  chatbot=chatbot)\n",
+    "    gr.Markdown('<hr style=\"border: 1px solid #d3d3d3; margin-top:12px; margin-bottom:12px;\">')\n",
+    "    # horizontal line\n",
+    "\n",
+    "    \n",
+    "    # ****************************************************************************************************************\n",
+    "    # Projects \n",
+    "    gr.Markdown(f'<p style=\"font-size:28px;font-weight:bold;\"> Examples of My Work</p>',\n",
+    "                    elem_id=\"job-title-light\")\n",
+    "    gr.Markdown('<hr style=\"border:1px solid grey;\">', elem_id=\"custom_divider\")\n",
+    "    \n",
+    "    # Projects row \n",
+    "    with gr.Row():\n",
+    "        for project in projects:\n",
+    "            with gr.Column():  # equal width for each project\n",
+    "                # Project image (disable download)\n",
+    "                gr.Image(project[\"image\"], \n",
+    "                            type=\"pil\", \n",
+    "                            show_label=False, \n",
+    "                            interactive=False, \n",
+    "                            height=200, width=350,\n",
+    "                            buttons=[['download', 'share', 'fullscreen']])\n",
+    "                # Project title / text\n",
+    "                gr.Markdown(f\"<div style='text-align:center; font-size:16px; margin-top:4px;'>{project['title']}</div>\")\n",
+    "\n",
+    "\n",
+    "    \n",
+    "\n",
+    "\n",
+    "ui.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "769f66e6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

src/requirements.txt

@@ -0,0 +1,3 @@
+gradio>=5.22.0
+openai>=1.68.2
+python-dotenv>=1.0.1