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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}
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