main api file

main.py

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+from fastapi import FastAPI, File, UploadFile
+from starlette.middleware.cors import CORSMiddleware
+from PIL import Image
+import tensorflow as tf
+import numpy as np
+import io
+
+
+app = FastAPI()
+# Enable CORS to allow cross-origin requests
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load the Coffee Land Classifier model
+model_path = "model/model.h5"
+class_labels = ["Coffee Land", "Not Coffee Land"]
+model = tf.keras.models.load_model(model_path, compile=False)
+
+def preprocess_image(image):
+    # Resize and preprocess the image
+    img = image.resize((64, 64))
+    img = np.array(img)
+    img = img.astype('float32') / 255.0
+    img = np.expand_dims(img, axis=0)
+    return img
+
+def predict_class(image):
+    img = preprocess_image(image)
+    predictions = model.predict(img)
+    class_index = np.argmax(predictions)
+    predicted_class = class_labels[class_index]
+    return predicted_class, predictions[0].tolist()
+
+@app.post("/predict/")
+async def predict(upload_file: UploadFile = File(...)):
+    file_contents = await upload_file.read()  # Use upload_file, not request.file
+    print(f"Received file with size: {len(file_contents)} bytes")
+    image = Image.open(io.BytesIO(file_contents))
+    predicted_class, class_probabilities = predict_class(image)
+    return {"predicted_class": predicted_class, "class_probabilities": class_probabilities}
+   
+