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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")
@app.get("/", response_class=HTMLResponse)
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)
@app.post("/predict")
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)}"
@app.get("/test")
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)
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