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import keras
from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
import numpy as np
from io import BytesIO
from PIL import Image
import logging
import os
from huggingface_hub import hf_hub_download
logging.basicConfig(level=logging.DEBUG)
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
MODEL = None
def initialize_model():
global MODEL
if MODEL is None:
HF_MODEL_DIR = "/tmp/rice_model"
os.makedirs(HF_MODEL_DIR, exist_ok=True)
try:
model_path = hf_hub_download(
repo_id="ShahzadAli44/rice_cnn",
filename="rice_cnn_model.keras",
local_dir=HF_MODEL_DIR,
local_dir_use_symlinks=False
)
MODEL = keras.saving.load_model(model_path)
logging.info("✅ Model loaded successfully.")
except Exception as e:
logging.error(f"❌ Failed to load model: {e}")
initialize_model()
CLASS_NAMES = [
"bacterial_leaf_blight", "brown_spot", "healthy", "leaf_blast",
"leaf_scald", "narrow_brown_spot", "rice_hispa", "sheath_blight", "tungro"
]
DISEASE_DETAILS = {
"bacterial_leaf_blight": {
"symptoms": "Leaves turn yellow with wet-looking spots that spread and dry out.",
"treatment": "Use disease-resistant rice seeds. Spray copper-based fungicides. Apply balanced fertilizers with nitrogen and phosphorus."
},
"brown_spot": {
"symptoms": "Small brown spots appear on leaves, which later turn yellow.",
"treatment": "Use potassium-rich fertilizers. Spray Mancozeb fungicide. Maintain good drainage to avoid water stress."
},
"healthy": {
"symptoms": "Leaves are green and strong, with no signs of disease.",
"treatment": "No treatment needed. Keep the soil healthy by using compost and balanced fertilizers."
},
"leaf_blast": {
"symptoms": "Leaves get white or gray spots that spread and kill the leaf.",
"treatment": "Spray Tricyclazole fungicide. Keep the right water level in the field. Use silica-based fertilizers to strengthen plants."
},
"leaf_scald": {
"symptoms": "Leaf edges turn yellow or brown, and the leaf dries up.",
"treatment": "Use resistant rice varieties. Avoid too much nitrogen fertilizer. Spray Propiconazole fungicide if needed."
},
"narrow_brown_spot": {
"symptoms": "Thin, dark brown streaks appear on leaves.",
"treatment": "Reduce plant overcrowding. Spray Propiconazole fungicide. Use potassium and phosphorus fertilizers to improve plant health."
},
"rice_hispa": {
"symptoms": "Leaves get white scars and small holes due to insect feeding.",
"treatment": "Remove infected leaves. Spray Chlorpyrifos insecticide. Keep fields clean to reduce insect attacks."
},
"sheath_blight": {
"symptoms": "White or gray patches appear on the lower part of the plant, leading to weak stems.",
"treatment": "Keep enough space between plants. Apply Azoxystrobin fungicide. Use compost and phosphorus-rich fertilizers."
},
"tungro": {
"symptoms": "Plants grow slowly, and leaves turn yellow or orange.",
"treatment": "Use virus-free seedlings. Spray insecticides like Imidacloprid to control pests. Apply nitrogen fertilizers to strengthen plants."
}
}
def read_file_as_image(data) -> np.ndarray:
try:
image = Image.open(BytesIO(data)).convert("RGB")
logging.debug(f"Image size: {image.size}")
return np.array(image)
except Exception as e:
logging.error("Error reading image file: %s", str(e))
raise ValueError("Invalid image data")
@app.get("/")
def home():
return {"message": "Agrico API is live!"}
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
if MODEL is None:
return {"error": "Model failed to load."}
try:
image_data = await file.read()
image = read_file_as_image(image_data)
img_batch = np.expand_dims(image, 0)
logging.debug(f"Image batch shape: {img_batch.shape}")
predictions = MODEL.predict(img_batch)
logging.debug(f"Predictions: {predictions}")
predicted_class = CLASS_NAMES[np.argmax(predictions[0])]
confidence = np.max(predictions[0])
if confidence < 0.5:
return {"error": "The uploaded image does not appear to be a rice crop leaf."}
disease_details = DISEASE_DETAILS.get(predicted_class, {})
return {
'class': predicted_class,
'confidence': float(confidence),
'symptoms': disease_details.get("symptoms", "No symptoms available."),
'treatment': disease_details.get("treatment", "No treatment information available.")
}
except Exception as e:
logging.error("Error during prediction: %s", str(e))
return {"error": "An error occurred while processing the image."}
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