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| """ | |
| Simple local FastAPI server for testing face recognition | |
| Run this to test the web interface locally | |
| """ | |
| import sys | |
| import os | |
| from pathlib import Path | |
| # Add the parent directory to Python path | |
| sys.path.append(str(Path(__file__).resolve().parent.parent)) | |
| from fastapi import FastAPI, Request, File, UploadFile | |
| from fastapi.staticfiles import StaticFiles | |
| from fastapi.templating import Jinja2Templates | |
| from fastapi.responses import HTMLResponse | |
| import numpy as np | |
| from PIL import Image | |
| import uvicorn | |
| # Import face recognition | |
| from app.Hackathon_setup import face_recognition | |
| app = FastAPI(title="Local Face Recognition Test") | |
| # Mount static files | |
| app.mount("/static", StaticFiles(directory="app/static"), name="static") | |
| # Templates | |
| templates = Jinja2Templates(directory="app/templates") | |
| async def root(): | |
| """Simple HTML interface for testing""" | |
| html_content = """ | |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Face Recognition Test</title> | |
| <style> | |
| body { font-family: Arial, sans-serif; margin: 40px; } | |
| .container { max-width: 600px; margin: 0 auto; } | |
| .form-group { margin: 20px 0; } | |
| input[type="file"] { margin: 10px 0; } | |
| button { background: #007bff; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer; } | |
| button:hover { background: #0056b3; } | |
| .result { margin: 20px 0; padding: 15px; background: #f8f9fa; border-radius: 5px; } | |
| .error { background: #f8d7da; color: #721c24; } | |
| .success { background: #d4edda; color: #155724; } | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <h1>🧠 Face Recognition Test</h1> | |
| <p>Upload a face image to test the classification locally.</p> | |
| <form action="/predict" method="post" enctype="multipart/form-data"> | |
| <div class="form-group"> | |
| <label for="file">Select Face Image:</label><br> | |
| <input type="file" id="file" name="file" accept="image/*" required> | |
| </div> | |
| <button type="submit">🔍 Classify Face</button> | |
| </form> | |
| <div id="result"></div> | |
| </div> | |
| <script> | |
| document.querySelector('form').addEventListener('submit', async function(e) { | |
| e.preventDefault(); | |
| const formData = new FormData(); | |
| const fileInput = document.getElementById('file'); | |
| formData.append('file', fileInput.files[0]); | |
| const resultDiv = document.getElementById('result'); | |
| resultDiv.innerHTML = '<div class="result">Processing...</div>'; | |
| try { | |
| const response = await fetch('/predict', { | |
| method: 'POST', | |
| body: formData | |
| }); | |
| const result = await response.text(); | |
| if (result.includes('UNKNOWN_CLASS')) { | |
| resultDiv.innerHTML = `<div class="result error">❌ Result: ${result}</div>`; | |
| } else if (result.includes('Person')) { | |
| resultDiv.innerHTML = `<div class="result success">✅ Result: ${result}</div>`; | |
| } else { | |
| resultDiv.innerHTML = `<div class="result">📋 Result: ${result}</div>`; | |
| } | |
| } catch (error) { | |
| resultDiv.innerHTML = `<div class="result error">❌ Error: ${error.message}</div>`; | |
| } | |
| }); | |
| </script> | |
| </body> | |
| </html> | |
| """ | |
| return HTMLResponse(content=html_content) | |
| async def predict_face(file: UploadFile = File(...)): | |
| """Predict face class from uploaded image""" | |
| try: | |
| # Save uploaded file | |
| contents = await file.read() | |
| filename = f"app/static/{file.filename}" | |
| with open(filename, 'wb') as f: | |
| f.write(contents) | |
| # Load and process image | |
| img = Image.open(filename) | |
| img_array = np.array(img).reshape(img.size[1], img.size[0], 3).astype(np.uint8) | |
| # Get face class | |
| result = face_recognition.get_face_class(img_array) | |
| return f"Predicted Face Class: {result}" | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| async def test_endpoint(): | |
| """Simple test endpoint""" | |
| try: | |
| from app.Hackathon_setup.face_recognition import CLASS_NAMES | |
| import joblib | |
| # Test model loading | |
| classifier = joblib.load('app/Hackathon_setup/decision_tree_model.sav') | |
| scaler = joblib.load('app/Hackathon_setup/face_recognition_scaler.sav') | |
| return { | |
| "status": "success", | |
| "class_names": CLASS_NAMES, | |
| "classifier_classes": classifier.classes_.tolist(), | |
| "scaler_features": scaler.n_features_in_ | |
| } | |
| except Exception as e: | |
| return {"status": "error", "message": str(e)} | |
| if __name__ == "__main__": | |
| print("Starting local Face Recognition test server...") | |
| print("Open your browser and go to: http://localhost:8000") | |
| print("Press Ctrl+C to stop the server") | |
| uvicorn.run(app, host="0.0.0.0", port=8000) | |