import os from tensorflow.keras.models import load_model from tensorflow.keras.utils import img_to_array os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0' os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' import tensorflow as tf from fastapi import FastAPI, File, UploadFile from fastapi.responses import JSONResponse from fastapi.middleware.cors import CORSMiddleware # from tensorflow.keras.preprocessing import image import numpy as np import uvicorn from PIL import Image import io import logging # Suppress TensorFlow warnings #tf.get_logger().setLevel(logging.ERROR) app = FastAPI() # Add CORS middleware app.add_middleware( CORSMiddleware, allow_origins=["*"], # Adjust as needed allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # Replace with your actual model path model_path = 'face_expression_detection_model3.h5' # Update with your new model's path model = load_model(model_path) # def preprocess_image(img: Image.Image, target_size=(224, 224)): # img = img.resize(target_size) # x = image.img_to_array(img) # x = np.expand_dims(x, axis=0) # x = x / 255.0 # Assuming normalization was done during training # return x def preprocess_image(img: Image.Image, target_size=(224, 224)): img = img.resize(target_size) x = img_to_array(img) x = np.expand_dims(x, axis=0) x = x / 255.0 # Assuming normalization was done during training return x @app.get("/") async def read_root(): return {"message": "Welcome to the Emotion Classification API"} @app.post("/predict") async def predict(file: UploadFile = File(...)): contents = await file.read() img = Image.open(io.BytesIO(contents)) x = preprocess_image(img) prediction = model.predict(x) predicted_class = np.argmax(prediction[0]) class_names = ['angry', 'happy', 'sad', 'surprised', 'neutral', 'disgusted', 'fearful'] stress_levels = { 'angry': 'High', 'happy': 'Normal', 'sad': 'Low', 'surprised': 'Low', 'neutral': 'Low', 'disgusted': 'Low', 'fearful': 'Low' } emotion = class_names[predicted_class] stress_level = stress_levels[emotion] result = { #"predicted_class": emotion, "predicted_class": stress_level } return JSONResponse(content=result)