api

SATIN API/main.py

@@ -0,0 +1,48 @@
+# main.py
+
+from fastapi import FastAPI, File, UploadFile
+import uvicorn
+import numpy as np
+from io import BytesIO
+from PIL import Image
+import tensorflow as tf
+
+
+app = FastAPI()
+
+
+class ImageClassifier:
+    def __init__(self, model_path):
+        self.MODEL = tf.keras.models.load_model(model_path) # Load model
+        self.CLASS_NAMES = ["Not Coffee Land", "Coffee Land"]
+
+
+# Process input data
+    def read_file_as_image(self, data) -> np.ndarray:
+        image = np.array(Image.open(BytesIO(data)))
+        return image
+
+# Return output
+    def predict(self, file: UploadFile):
+        image = self.read_file_as_image(file.file.read())
+        img_batch = np.expand_dims(image, 0)
+
+        predictions = self.MODEL.predict(img_batch)
+        predicted_class = self.CLASS_NAMES[np.argmax(predictions[0])]
+
+        return {'class': predicted_class}
+
+classifier = ImageClassifier("model.h5")
+
+# Created this for testing
+@app.get("/ping")
+async def ping():
+    return "Hello World"
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    result = classifier.predict(file)
+    return result
+
+if __name__ == "__main__":
+    uvicorn.run(app, host='localhost', port=8080)