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Dockerfile

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+## use the offical python 3.9
+
+FROM python:3.9
+
+## Set the working directory to /code
+WORKDIR /code
+
+## Copy the current directory content into the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+
+## install the requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd user
+USER user
+
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+COPY --chown=user . $HOME/app
+
+
+CMD ["uvicorn","app:main","--host","0.0.0.0","--port","7860"]

face_expression_detection_model3.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a68ca8cc7c18e6faf820eefeb5f9829984d823b2f3d6b3ca1ecd5a49b2d531ff
+size 86587288

main.py

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+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)
+
+

requirements.txt

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+fastapi
+uvicorn
+tensorflow==2.12.0
+pillow