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app.py — CropGuard FastAPI inference server
============================================
Loads the trained MobileNetV2 model and exposes a REST API used by both
the React frontend and the single-file HTML app.
Endpoints
---------
GET /health -> {"status": "ok", "model_loaded": bool}
POST /predict -> multipart image -> diagnosis JSON
GET /diseases -> the full treatment knowledge base
Diagnosis JSON shape (consumed by the frontends):
{
"class_id": "tomato_late",
"confidence": 0.94,
"severity": "moderate", # null when healthy
"diseased_ratio": 0.42,
"disease": { ...full record from recommendations.json... }
}
Run:
uvicorn app:app --host 0.0.0.0 --port 8000 --reload
"""
import io, json, os
import numpy as np
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from PIL import Image
MODEL_DIR = os.getenv("MODEL_DIR", "model")
IMG_SIZE = 224
MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)
app = FastAPI(title="CropGuard GH API", version="1.0")
app.add_middleware(
CORSMiddleware, allow_origins=["*"],
allow_methods=["*"], allow_headers=["*"],
)
# --- lazy globals ---
_model = None
_classes = None
with open(os.path.join(os.path.dirname(__file__), "recommendations.json")) as f:
RECS = json.load(f)
def get_model():
"""Load the Keras model once and keep it resident in memory (§3.10.2)."""
global _model, _classes
if _model is None:
import tensorflow as tf
_model = tf.keras.models.load_model(os.path.join(MODEL_DIR, "crop_model.keras"))
with open(os.path.join(MODEL_DIR, "classes.json")) as f:
_classes = json.load(f)
return _model, _classes
def preprocess(img: Image.Image):
img = img.convert("RGB").resize((IMG_SIZE, IMG_SIZE))
arr = np.asarray(img, dtype=np.float32) / 255.0
arr = (arr - MEAN) / STD
return np.expand_dims(arr, 0)
def estimate_severity(img: Image.Image):
"""Diseased-area ratio via colour thresholding (§3.8).
Returns (severity_label, diseased_ratio)."""
small = np.asarray(img.convert("RGB").resize((128, 128)), dtype=np.float32)
r, g, b = small[..., 0], small[..., 1], small[..., 2]
lum = small.mean(axis=2)
mx = small.max(axis=2); mn = small.min(axis=2); sat = mx - mn
leaf = ~((lum > 235) & (sat < 25)) # drop white background
yellow = (r > 120) & (g > 100) & (b < 100) & (r - b > 40)
dark = lum < 70
brown = (r > 70) & (r > g) & (g > b) & (r - b > 18) & (lum < 170)
leaf_px = max(int(leaf.sum()), 1)
ratio = float(((yellow & leaf).sum() * 0.85 +
(brown & leaf).sum() * 1.05 +
(dark & leaf).sum() * 1.10) / leaf_px)
ratio = min(ratio, 1.0)
label = "early" if ratio < 0.20 else "moderate" if ratio < 0.55 else "severe"
return label, round(ratio, 3)
@app.get("/health")
def health():
return {"status": "ok", "model_loaded": _model is not None}
@app.get("/diseases")
def diseases():
return RECS
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
try:
raw = await file.read()
img = Image.open(io.BytesIO(raw))
except Exception:
raise HTTPException(status_code=400, detail="Invalid image file")
model, classes = get_model()
probs = model.predict(preprocess(img), verbose=0)[0]
idx = int(np.argmax(probs))
class_id = classes[idx]
confidence = float(probs[idx])
record = RECS.get(class_id, {})
if record.get("healthy"):
severity, ratio = None, 0.0
else:
severity, ratio = estimate_severity(img)
# The uploaded image is never stored — it is discarded here (§3.10.2 privacy).
return {
"class_id": class_id,
"confidence": round(confidence, 4),
"severity": severity,
"diseased_ratio": ratio,
"disease": record,
}
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