app.py

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+import cv2
+import numpy as np
+from tensorflow import keras
+import uvicorn
+
+app = FastAPI()
+
+@app.get("/")
+async def app_status():
+    return {"message": "APP is running"}
+
+# model = keras.models.load_model('savedModel/facialemotionmodel.h5')
+
+# def prepare_image(img):
+#     img = cv2.cvtColor(np.array(img), cv2.COLOR_BGR2GRAY)  # Convert to grayscale
+#     img = cv2.resize(img, (48, 48))  # Resize image to 48x48
+#     img = img / 255.0  # Normalize pixel values to 0-1
+#     img = img.reshape(1, 48, 48, 1)  # Reshape for the model
+#     return img
+
+# def predict_emotion(img):
+#     prediction = model.predict(img)
+#     return prediction
+
+# def interpret_prediction(prediction):
+#     emotions = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+#     max_index = prediction.argmax()
+#     return emotions[max_index]
+
+# @app.post("/detect-emotion/")
+# async def detect_emotion(file: UploadFile = File(...)):
+#     if file.content_type.startswith('image/'):
+#         # Read image through file stream
+#         image_data = await file.read()
+#         image_array = np.frombuffer(image_data, np.uint8)
+#         image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+#         if image is None:
+#             raise HTTPException(status_code=400, detail="Could not read the image")
+        
+#         prepared_image = prepare_image(image)
+#         prediction = predict_emotion(prepared_image)
+#         emotion = interpret_prediction(prediction[0])
+#         return JSONResponse(content={"detected_emotion": emotion})
+#     else:
+#         raise HTTPException(status_code=400, detail="File format not supported")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+fastapi
+uvicorn
+requests
+python-multipart
+opencv-python-headless
+tensorflow
+numpy