feat: initialize project with Docker, Docker Compose, CI linting, and…

.dockerignore

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+# Git
+.git
+.gitignore
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.env
+.venv
+env/
+venv/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Streamlit
+.streamlit/

.env.example

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+GEMINI_API_KEY=your_gemini_api_key_here

.github/workflows/lint.yml

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+name: Lint Code
+
+on: [push, pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install flake8
+          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+
+      - name: Lint with flake8
+        run: |
+          # stop the build if there are Python syntax errors or undefined names
+          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.10-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code
+COPY . .
+
+# Expose the port Streamlit runs on
+EXPOSE 8501
+
+# Define environment variable to prevent .pyc files
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+# Command to run the application
+CMD ["streamlit", "run", "main.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

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+# ResumeParserAI
+
+A Streamlit-based application that parses resumes (PDF) using Google Gemini AI to generate professional summaries and extract structured data.
+
+## Features
+- **PDF Upload**: Upload your resume in PDF format.
+- **AI Analysis**: Uses Google Gemini Pro/Flash to analyze the content.
+- **Structured Extraction**: Extracts Name, Email, Location, Work Experience, Projects, Skills, and Education.
+- **Professional Summary**: Generates an engaging first-person summary.
+
+## Prerequisites
+- Docker and Docker Compose installed on your machine.
+- A Google Gemini API Key.
+
+## Getting Started
+
+### 1. Clone the repository
+```bash
+git clone <repository-url>
+cd ResumeParserAI
+```
+
+### 2. Environment Setup
+Create a `.env` file from the example:
+```bash
+cp .env.example .env
+```
+Open `.env` and add your Gemini API key:
+```
+GEMINI_API_KEY=your_actual_api_key_here
+```
+
+### 3. Run with Docker Compose
+Start the application:
+```bash
+docker-compose up --build
+```
+Access the application at `http://localhost:8501`.
+
+### 4. Run Locally (Optional)
+If you prefer running without Docker:
+```bash
+pip install -r requirements.txt
+streamlit run main.py
+```
+
+## Project Structure
+- `main.py`: Main application logic.
+- `requirements.txt`: Python dependencies.
+- `Dockerfile`: Docker construction instructions.
+- `docker-compose.yml`: Container orchestration.

docker-compose.yml

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+version: '3.8'
+
+services:
+  resume-parser:
+    build: .
+    ports:
+      - "8501:8501"
+    env_file:
+      - .env
+    volumes:
+      - .:/app
+    environment:
+      - GEMINI_API_KEY=${GEMINI_API_KEY}

requirements.txt

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+streamlit
+python-dotenv
+PyPDF2
+google-generativeai
+flake8