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afd5f0f 416e9c2 afd5f0f 416e9c2 bf21edf b3c6e89 bf21edf 416e9c2 afd5f0f 416e9c2 afd5f0f 416e9c2 afd5f0f 416e9c2 afd5f0f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | import os
os.environ["KERAS_BACKEND"] = "jax"
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
import keras
import numpy as np
import cv2
from PIL import Image
import io
from huggingface_hub import hf_hub_download
app = FastAPI()
# Add root route for testing
@app.get("/")
def read_root():
return {"message": "Facial Emotion API is running 🚀"}
# Load model and config
model = None
desired_emotions = ['happy', 'sad', 'neutral']
original_emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
desired_indices = [original_emotion_labels.index(emotion) for emotion in desired_emotions]
@app.on_event("startup")
def load_emotion_model():
global model
print("🔄 Downloading model from HuggingFace Hub...")
model_path = hf_hub_download(
repo_id="Shees7/facial_7_cat",
filename="trained_model.keras",
cache_dir="/tmp"
)
print("✅ Model file downloaded at:", model_path)
model = keras.saving.load_model(model_path)
print("✅ Model loaded successfully.")
def preprocess_face(image_bytes):
np_img = np.array(Image.open(io.BytesIO(image_bytes)).convert('RGB'))
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(np_img, cv2.COLOR_RGB2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
if len(faces) == 0:
return None
x, y, w, h = faces[0]
face = np_img[y:y+h, x:x+w]
face_resized = cv2.resize(face, (224, 224))
face_normalized = face_resized / 255.0
face_expanded = np.expand_dims(face_normalized, axis=0)
return face_expanded
@app.post("/predict")
async def predict_emotion(file: UploadFile = File(...)):
if model is None:
return JSONResponse(content={"error": "Model not loaded."}, status_code=500)
image_bytes = await file.read()
processed_face = preprocess_face(image_bytes)
if processed_face is None:
return {"emotion": "neutral"}
predictions = model.predict(processed_face)[0]
filtered_predictions = [predictions[i] for i in desired_indices]
predicted_index = np.argmax(filtered_predictions)
predicted_emotion = desired_emotions[predicted_index]
return {"emotion": predicted_emotion}
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