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from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
import os
import tempfile
import json
import requests
from io import BytesIO
from dotenv import load_dotenv
load_dotenv()
# Import deepface with error handling
try:
from deepface import DeepFace
import cv2
import numpy as np
DEEPFACE_AVAILABLE = True
except ImportError as e:
DEEPFACE_AVAILABLE = False
print(f"Warning: DeepFace not available. Please install dependencies: {e}")
def convert_to_serializable(obj):
"""Convert numpy types and other non-serializable types to native Python types"""
if DEEPFACE_AVAILABLE:
if isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
elif isinstance(obj, np.ndarray):
return obj.tolist()
# Handle dict and list recursively
if isinstance(obj, dict):
return {key: convert_to_serializable(value) for key, value in obj.items()}
elif isinstance(obj, list):
return [convert_to_serializable(item) for item in obj]
# Try to convert to float if it's a number-like object
try:
if hasattr(obj, 'item'): # numpy scalar
return obj.item()
except (AttributeError, ValueError):
pass
return obj
app = FastAPI(title="Age, Emotion, and Gender Detection API", version="1.0.0")
# Add CORS middleware to allow requests from React app
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # In production, replace with specific origins
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/")
async def root():
return {
"message": "Age, Emotion, and Gender Detection API",
"endpoints": {
"/analyze": "POST - Upload an image to detect age, emotion, and gender",
"/skin-analysis": "POST - Upload an image for comprehensive skin analysis"
}
}
@app.post("/analyze")
async def analyze_image(file: UploadFile = File(...)):
"""
Upload an image and get age, emotion, and gender detection results.
Args:
file: Image file to analyze (supports common image formats)
Returns:
JSON response with age, gender, and emotion information
"""
# Check if DeepFace is available
if not DEEPFACE_AVAILABLE:
raise HTTPException(
status_code=503,
detail="DeepFace module not available. Please install dependencies: pip install -r requirements.txt"
)
# Validate file type
if not file.content_type or not file.content_type.startswith('image/'):
raise HTTPException(status_code=400, detail="File must be an image")
# Create a temporary file to save the uploaded image
tmp_file_path = None
try:
# Read file contents
contents = await file.read()
# Create temporary file and write contents
with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as tmp_file:
tmp_file.write(contents)
tmp_file_path = tmp_file.name
# Analyze the image
try:
results = DeepFace.analyze(
img_path=tmp_file_path,
actions=['age', 'gender', 'emotion'],
enforce_detection=False # Continue even if face detection fails
)
# Handle both single result and list of results
if isinstance(results, list):
result = results[0]
else:
result = results
# Extract age (convert to native Python int)
age = int(result.get("age", 0))
# Extract gender and convert numpy types
gender_dict = result.get("gender", {})
gender_dict = convert_to_serializable(gender_dict)
if gender_dict:
dominant_gender = max(gender_dict, key=gender_dict.get)
gender_confidence = float(gender_dict[dominant_gender])
else:
dominant_gender = "Unknown"
gender_confidence = 0.0
# Extract emotion and convert numpy types
emotion_dict = result.get("emotion", {})
emotion_dict = convert_to_serializable(emotion_dict)
if emotion_dict:
dominant_emotion = max(emotion_dict, key=emotion_dict.get)
emotion_confidence = float(emotion_dict[dominant_emotion])
else:
dominant_emotion = "Unknown"
emotion_confidence = 0.0
# Prepare response with all values converted to native Python types
response = {
"success": True,
"age": age,
"gender": {
"prediction": dominant_gender,
"confidence": round(gender_confidence, 2),
"all_predictions": gender_dict
},
"emotion": {
"prediction": dominant_emotion,
"confidence": round(emotion_confidence, 2),
"all_predictions": emotion_dict
}
}
return JSONResponse(content=response)
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"Error analyzing image: {str(e)}"
)
finally:
# Clean up temporary file (close it first on Windows)
if tmp_file_path and os.path.exists(tmp_file_path):
try:
# On Windows, we need to ensure the file is closed before deletion
import time
time.sleep(0.1) # Small delay to ensure file is released
os.unlink(tmp_file_path)
except (PermissionError, OSError) as e:
# If deletion fails, try to delete on next attempt or ignore
# The OS will clean up temp files eventually
pass
@app.post("/skin-analysis")
async def skin_analysis(file: UploadFile = File(...)):
"""
Upload an image and get comprehensive skin analysis results.
Args:
file: Image file to analyze (supports common image formats)
Returns:
JSON response with detailed skin analysis information
"""
# Validate file type
if not file.content_type or not file.content_type.startswith('image/'):
raise HTTPException(status_code=400, detail="File must be an image")
# Get API key from environment variable
api_key = os.getenv("RAPIDAPI_KEY", "")
if not api_key:
raise HTTPException(
status_code=500,
detail="RAPIDAPI_KEY environment variable is not set"
)
try:
# Read file contents
contents = await file.read()
# Prepare request to RapidAPI
url = "https://skin-analysis-api.p.rapidapi.com/predict"
querystring = {"lang": "en"}
# Use BytesIO to create a file-like object from the contents
file_obj = BytesIO(contents)
# Try "file" as field name first (common for RapidAPI endpoints)
files = {"file": (file.filename or "image.jpg", file_obj, file.content_type)}
headers = {
"x-rapidapi-key": api_key,
"x-rapidapi-host": "skin-analysis-api.p.rapidapi.com"
}
# Make request to RapidAPI
# Note: When using files parameter, requests automatically sets Content-Type to multipart/form-data
response = requests.post(url, files=files, headers=headers, params=querystring)
# If "file" doesn't work, try "image" as fallback
if response.status_code == 400 and "No file found" in response.text:
file_obj.seek(0) # Reset file pointer
files = {"image": (file.filename or "image.jpg", file_obj, file.content_type)}
response = requests.post(url, files=files, headers=headers, params=querystring)
if response.status_code != 200:
raise HTTPException(
status_code=response.status_code,
detail=f"External API error: {response.text}"
)
data = response.json()
return JSONResponse(content=data)
except requests.exceptions.RequestException as e:
raise HTTPException(
status_code=500,
detail=f"Error calling external API: {str(e)}"
)
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"Error processing image: {str(e)}"
)
@app.get("/health")
async def health_check():
"""Health check endpoint"""
return {"status": "healthy"} |