main.py

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}