Initialize CV Parser project with FastAPI, Docker support, and document parsing capabilities

.dockerignore

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+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+venv/
+.env
+.venv
+env/
+ENV/
+.git
+.gitignore
+.idea/
+.vscode/
+*.pdf
+*.jpg
+*.png
+*.jpeg
+
+data/

.gitignore

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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+data

.python-version

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+3.10

Dockerfile

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+FROM python:3.12-slim-bookworm
+
+# The installer requires curl (and certificates) to download the release archive
+RUN apt-get update && apt-get install -y --no-install-recommends curl ca-certificates libreoffice libreoffice-java-common
+
+# Download the latest installer
+ADD https://astral.sh/uv/install.sh /uv-installer.sh
+
+# Run the installer then remove it
+RUN sh /uv-installer.sh && rm /uv-installer.sh
+
+# Ensure the installed binary is on the `PATH`
+ENV PATH="/root/.local/bin/:$PATH"
+
+ADD . /app
+WORKDIR /app
+RUN uv sync --frozen
+CMD ["uv", "run", "api.py"]

README.md

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+# CV Parser
+
+A FastAPI service that parses resumes and CVs from various document formats.
+
+## Setup
+
+1. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+2. Set up the Gemini API key:
+```bash
+export GEMINI_API_KEY=your_api_key_here
+```
+
+3. Run the API server:
+```bash
+python api.py
+```
+
+The server will start on http://localhost:8000
+
+## API Usage
+
+### Upload a Document
+
+**Endpoint:** `POST /upload/`
+
+**Supported formats:** PDF, DOCX, JPG, JPEG, PNG
+
+**Example using curl:**
+```bash
+curl -X 'POST' \
+  'http://localhost:8000/upload/' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: multipart/form-data' \
+  -F 'file=@/path/to/your/resume.pdf'
+```
+
+## Command Line Usage
+
+You can also use the parser directly from the command line:
+
+```bash
+python main.py /path/to/your/resume.pdf
+```

api.py

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+import os
+import tempfile
+import uuid
+from pathlib import Path
+from typing import List, Optional
+
+
+import httpx
+from google import genai
+
+from fastapi import FastAPI, File, HTTPException, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+import uvicorn
+
+from parser import parse_pdf, parse_image
+from docx_utils import convert_docx_to_pdf
+
+# Initialize FastAPI app
+app = FastAPI(
+    title="CV Parser API",
+    description="API for parsing CVs from various document formats",
+    version="0.1.0",
+)
+
+# Configure CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Update for production
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Mount static directory
+static_dir = Path(__file__).parent / "static"
+static_dir.mkdir(exist_ok=True)
+app.mount("/static", StaticFiles(directory=str(static_dir)), name="static")
+
+# Allowed file types
+ALLOWED_EXTENSIONS = {
+    "pdf": "application/pdf",
+    "docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
+    "jpg": "image/jpeg",
+    "jpeg": "image/jpeg", 
+    "png": "image/png"
+}
+
+@app.get("/")
+async def root():
+    """Serve the web UI for CV parsing"""
+    return FileResponse(str(static_dir / "index.html"))
+
+app.client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])
+
+@app.post("/upload/")
+async def upload_file(file: UploadFile = File(...)):
+    """
+    Upload a document for parsing (PDF, DOCX, or image)
+    """
+    # Check file extension and content type
+    file_ext = file.filename.split(".")[-1].lower() if file.filename else ""
+    if file_ext not in ALLOWED_EXTENSIONS:
+        raise HTTPException(
+            status_code=400, 
+            detail=f"File type not supported. Supported types: {', '.join(ALLOWED_EXTENSIONS.keys())}"
+        )
+    
+    # Create temp file to save the uploaded content
+    with tempfile.NamedTemporaryFile(delete=False, suffix=f".{file_ext}") as temp_file:
+        temp_file_path = temp_file.name
+        contents = await file.read()
+        temp_file.write(contents)
+    
+    try:
+        if file_ext == "pdf":
+            result = parse_pdf(temp_file_path, app.client)
+            return result
+        elif file_ext == "docx":
+            pdf_path = convert_docx_to_pdf(temp_file_path)
+            result = parse_pdf(pdf_path, app.client)
+            return result
+        elif file_ext in ["jpg", "jpeg", "png"]:
+            result = parse_image(temp_file_path, app.client)
+            return result
+        else:
+            # Basic placeholder for other file types
+            return {
+                "message": f"{file_ext.upper()} parsing not fully implemented yet",
+                "filename": file.filename,
+                "content_type": file.content_type,
+                "size": len(contents)
+            }
+    finally:
+        # Clean up the temp file
+        if os.path.exists(temp_file_path):
+            os.unlink(temp_file_path)
+        
+        if os.path.exists(temp_file_path.replace('.docx', '.pdf')):
+            os.unlink(temp_file_path.replace('.docx', '.pdf'))
+
+if __name__ == "__main__":
+    uvicorn.run("api:app", host="0.0.0.0", port=7860, reload=True)

docx_utils.py

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+import os
+import subprocess
+from logger import logger
+
+def convert_docx_to_pdf(input_path):
+    logger.info(f"Converting DOCX file to PDF: {input_path}")
+    output_path = input_path.replace('.docx', '.pdf')
+    try:
+        subprocess.run(['soffice', '--headless', '--convert-to', 'pdf', '--outdir', os.path.dirname(input_path), input_path], check=True)
+        logger.info(f"Converted DOCX to PDF: {output_path}")
+        return output_path
+    except subprocess.CalledProcessError as e:
+        logger.error(f"Error converting DOCX to PDF: {e}")
+        raise RuntimeError(f"Conversion failed: {e}")

logger.py

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+import logging
+import os
+from datetime import datetime
+
+def setup_logger(log_level=logging.INFO):
+    """Configure and return a logger instance for the application."""
+    # Create logs directory if it doesn't exist
+    log_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'logs')
+    os.makedirs(log_dir, exist_ok=True)
+    
+    # Create unique log filename with timestamp
+    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+    log_file = os.path.join(log_dir, f'cv_parser_{timestamp}.log')
+    
+    # Configure logging
+    logging.basicConfig(
+        level=log_level,
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+        handlers=[
+            logging.FileHandler(log_file),
+            logging.StreamHandler()  # Also log to console
+        ]
+    )
+    
+    return logging.getLogger('cv_parser')
+
+# Create a default logger instance
+logger = setup_logger()

parser.py

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+import argparse
+import json
+import os
+import pathlib
+
+from google.genai import types
+import PIL.Image
+
+from schema import DeveloperResume
+from prompts import parsing_prompts
+from logger import logger
+
+
+def parse_pdf(pdf_path: str, genai_client) -> dict:
+    filepath = pathlib.Path(pdf_path)
+
+    logger.info(f"Parsing PDF file: {filepath}")
+
+    response = genai_client.models.generate_content(
+        model="gemini-1.5-flash",
+        contents=[
+            types.Part.from_bytes(
+                data=filepath.read_bytes(),
+                mime_type="application/pdf",
+            ),
+            parsing_prompts,
+        ],
+        config={
+            "response_mime_type": "application/json",
+            'response_schema': DeveloperResume,
+        },
+    )
+
+    json_data = json.loads(response.text)
+    return json_data
+
+def parse_image(image_path:str, genai_client) -> dict:
+
+    image = PIL.Image.open(image_path)
+
+    logger.info(f"Parsing image file: {image_path}")
+
+    response = genai_client.models.generate_content(
+        model="gemini-1.5-flash",
+        contents=[
+            image,
+            parsing_prompts,
+        ],
+        config={
+            "response_mime_type": "application/json",
+            'response_schema': DeveloperResume,
+        },
+    )
+
+    json_data = json.loads(response.text)
+    return json_data

prompts.py

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+
+parsing_prompts = """You are an AI assistant tasked with extracting and structuring information from a developer's resume into a JSON format based
+Please extract the following information from the provided resume text and structure it according to the schema:
+
+Contact Information:
+- Name
+- Phone Number
+- Email
+- LinkedIn Profile (if available)
+- Resume Summary (if available)
+
+Work Experience:
+- Company Name
+- Job Title
+- Dates of Employment
+- Responsibilities
+- Achievements (if available)
+
+Education:
+- Institution Name
+- Degree
+- Dates of Attendance
+- Relevant Coursework (if available)
+
+Skills:
+- Technical Skills (list)
+- Soft Skills (list)
+- Certifications (if available)
+
+Projects (if available):
+- Project Name
+- Description
+- Technologies Used (list)
+- Outcome (if available)
+
+Languages and Technologies (if available)
+
+Extracurricular Activities (if available)
+
+Interests (if available)
+"""

pyproject.toml

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+[project]
+name = "cv-parser"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "fastapi>=0.115.12",
+    "google-genai>=1.8.0",
+    "pillow>=11.1.0",
+    "pydantic>=2.11.1",
+    "python-multipart>=0.0.20",
+    "uvicorn>=0.34.0",
+]

schema.py

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+from pydantic import BaseModel
+from typing import List, Optional
+
+class ContactInformation(BaseModel):
+    name: str
+    phone_number: str
+    email: str
+    linkedin_profile: Optional[str] = None
+
+class WorkExperience(BaseModel):
+    company_name: str
+    job_title: str
+    dates_of_employment: str
+    responsibilities: str
+    achievements: Optional[str] = None
+
+class Education(BaseModel):
+    institution_name: str
+    degree: str
+    dates_of_attendance: str
+    relevant_coursework: Optional[str] = None
+
+class Skills(BaseModel):
+    technical_skills: List[str]
+    soft_skills: List[str]
+
+class Project(BaseModel):
+    project_name: str
+    description: str
+    technologies_used: List[str]
+    outcome: Optional[str] = None
+
+class DeveloperResume(BaseModel):
+    contact_information: ContactInformation
+    resume_summary: Optional[str] = None
+    work_experience: List[WorkExperience]
+    education: List[Education]
+    skills: Skills
+    certifications: Optional[List[str]] = None
+    projects: Optional[List[Project]] = None
+    languages_and_technologies: Optional[List[str]] = None
+    extracurricular_activities: Optional[List[str]] = None
+    interests: Optional[List[str]] = None
+

static/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>CV Parser</title>
+    <script src=https://cdn.jsdelivr.net/npm/[email protected]/dist/pretty-print-json.min.js></script>
+    <link rel=stylesheet href=https://cdn.jsdelivr.net/npm/[email protected]/dist/css/pretty-print-json.css>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            max-width: 800px;
+            margin: 0 auto;
+            padding: 20px;
+        }
+        .container {
+            border: 1px solid #ddd;
+            padding: 20px;
+            border-radius: 5px;
+        }
+        .upload-area {
+            border: 2px dashed #ccc;
+            padding: 20px;
+            text-align: center;
+            margin-bottom: 20px;
+            cursor: pointer;
+        }
+        .upload-area:hover {
+            border-color: #aaa;
+        }
+        .upload-area.highlight {
+            border-color: #2196F3;
+            background-color: #f0f8ff;
+        }
+        .progress {
+            display: none;
+            margin-top: 10px;
+        }
+        button {
+            background-color: #4CAF50;
+            color: white;
+            padding: 10px 15px;
+            border: none;
+            border-radius: 4px;
+            cursor: pointer;
+        }
+        button:hover {
+            background-color: #45a049;
+        }
+        .error {
+            color: red;
+            margin-top: 10px;
+        }
+
+        /* JSON formatting styles */
+        .json-container {
+            background-color: #f8f9fa;
+            border-radius: 4px;
+            padding: 15px;
+            font-family: 'Consolas', 'Monaco', monospace;
+            font-size: 14px;
+            overflow: auto;
+            max-height: 600px;
+            border: 1px solid #e0e0e0;
+            position: relative;
+        }
+        
+        .json-key {
+            color: #0366d6;
+            font-weight: bold;
+        }
+        
+        .json-string {
+            color: #22863a;
+        }
+        
+        .json-number {
+            color: #005cc5;
+        }
+        
+        .json-boolean {
+            color: #d73a49;
+        }
+        
+        .json-null {
+            color: #6a737d;
+        }
+        
+        .collapsible,
+        .collapsible::before,
+        .collapsible.collapsed::before,
+        .json-toggle-all {
+            display: none; /* Hide these elements */
+        }
+        
+        .json-copy-btn {
+            position: absolute;
+            top: 10px;
+            right: 10px;
+            background-color: #f0f0f0;
+            border: 1px solid #ddd;
+            border-radius: 3px;
+            padding: 3px 8px;
+            font-size: 12px;
+            cursor: pointer;
+        }
+        
+        .copied-message {
+            position: absolute;
+            top: 10px;
+            right: 55px; /* Updated position */
+            background-color: #4CAF50;
+            color: white;
+            border-radius: 3px;
+            padding: 3px 8px;
+            font-size: 12px;
+            opacity: 0;
+            transition: opacity 0.3s;
+        }
+        
+        .copied-message.show {
+            opacity: 1;
+        }
+
+        .results-container {
+            position: relative;
+            margin-top: 20px;
+        }
+    </style>
+</head>
+<body>
+    <h1>CV Parser</h1>
+    <div class="container">
+        <div class="upload-area" id="drop-area">
+            <p>Drag & drop your CV file here or click anywhere in this area to select</p>
+            <input type="file" id="fileInput" accept=".pdf,.docx,.jpg,.jpeg,.png" style="display: none;">
+            <p>Supported formats: PDF, DOCX, JPG, PNG</p>
+        </div>
+        <div id="fileName"></div>
+        <div class="progress" id="uploadProgress">Uploading...</div>
+        <div class="error" id="errorMessage"></div>
+        <button id="uploadBtn" style="display: none;">Upload</button>
+        
+        <h2>Results</h2>
+        <div class="results-container">
+            <pre id="results" class="json-container"></pre>
+        </div>
+    </div>
+
+    <script>
+        document.addEventListener('DOMContentLoaded', function() {
+            const dropArea = document.getElementById('drop-area');
+            const fileInput = document.getElementById('fileInput');
+            const uploadBtn = document.getElementById('uploadBtn');
+            const fileNameDisplay = document.getElementById('fileName');
+            const progressIndicator = document.getElementById('uploadProgress');
+            const resultsContainer = document.getElementById('results');
+            const errorMessage = document.getElementById('errorMessage');
+
+            // Open file dialog when clicking the drop area
+            dropArea.addEventListener('click', () => fileInput.click());
+
+            // File selection handler
+            fileInput.addEventListener('change', handleFileSelection);
+
+            // Drag and drop handlers
+            ['dragenter', 'dragover', 'dragleave', 'drop'].forEach(eventName => {
+                dropArea.addEventListener(eventName, preventDefaults, false);
+            });
+
+            function preventDefaults(e) {
+                e.preventDefault();
+                e.stopPropagation();
+            }
+
+            ['dragenter', 'dragover'].forEach(eventName => {
+                dropArea.addEventListener(eventName, () => {
+                    dropArea.classList.add('highlight');
+                }, false);
+            });
+
+            ['dragleave', 'drop'].forEach(eventName => {
+                dropArea.addEventListener(eventName, () => {
+                    dropArea.classList.remove('highlight');
+                }, false);
+            });
+
+            dropArea.addEventListener('drop', (e) => {
+                const dt = e.dataTransfer;
+                const files = dt.files;
+                if (files.length) {
+                    fileInput.files = files;
+                    handleFileSelection();
+                }
+            }, false);
+
+            // Upload button event
+            uploadBtn.addEventListener('click', uploadFile);
+
+            function handleFileSelection() {
+                if (fileInput.files.length > 0) {
+                    const file = fileInput.files[0];
+                    fileNameDisplay.textContent = `Selected file: ${file.name}`;
+                    uploadBtn.style.display = 'block';
+                    resultsContainer.style.display = 'none';
+                    errorMessage.textContent = '';
+                }
+            }
+
+            function uploadFile() {
+                if (!fileInput.files.length) return;
+
+                const file = fileInput.files[0];
+                const formData = new FormData();
+                formData.append('file', file);
+
+                // Show progress indicator
+                progressIndicator.style.display = 'block';
+                uploadBtn.disabled = true;
+                
+                fetch('/upload/', {
+                    method: 'POST',
+                    body: formData
+                })
+                .then(response => {
+                    if (!response.ok) {
+                        return response.json().then(data => {
+                            throw new Error(data.detail || 'Error uploading file');
+                        });
+                    }
+                    return response.json();
+                })
+                .then(data => {
+                    // Display results with enhanced formatting
+                    resultsContainer.innerHTML = '';
+                    resultsContainer.style.display = 'block';
+                    
+                    // Use the pretty-print-json library
+                    const jsonHtml = prettyPrintJson.toHtml(data, {
+                        indent: 4,
+                        lineNumbers: true,
+                        linkUrls: true,
+                        quoteKeys: true
+                    });
+                    
+                    resultsContainer.innerHTML = jsonHtml;
+                    
+                    // Add only copy button control
+                    addCopyButton();
+                    
+                    errorMessage.textContent = '';
+                })
+                .catch(error => {
+                    errorMessage.textContent = error.message;
+                    resultsContainer.style.display = 'none';
+                })
+                .finally(() => {
+                    progressIndicator.style.display = 'none';
+                    uploadBtn.disabled = false;
+                });
+            }
+            
+            // Updated function to only add copy button
+            function addCopyButton() {
+                const parent = resultsContainer.parentNode;
+                
+                // Add copy button
+                const copyBtn = document.createElement('button');
+                copyBtn.className = 'json-copy-btn';
+                copyBtn.textContent = 'Copy to Clipboard';
+                parent.appendChild(copyBtn);
+                
+                // Add copied message element
+                const copiedMsg = document.createElement('span');
+                copiedMsg.className = 'copied-message';
+                copiedMsg.textContent = 'Copied!';
+                parent.appendChild(copiedMsg);
+                
+                // Copy to clipboard functionality
+                copyBtn.addEventListener('click', () => {
+                    try {
+                        // Extract the raw JSON from the HTML
+                        const jsonText = JSON.stringify(
+                            JSON.parse(resultsContainer.textContent),
+                            null,
+                            2
+                        );
+                        
+                        navigator.clipboard.writeText(jsonText).then(() => {
+                            copiedMsg.classList.add('show');
+                            setTimeout(() => copiedMsg.classList.remove('show'), 2000);
+                        });
+                    } catch (err) {
+                        console.error('Failed to copy JSON:', err);
+                    }
+                });
+            }
+        });
+    </script>
+</body>
+</html>

uv.lock

@@ -0,0 +1,601 @@
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