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from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
import tensorflow as tf
import numpy as np
from PIL import Image
import io
import uvicorn
import tempfile
import cv2
# Initialize FastAPI app
app = FastAPI(title="Plant Disease Detection API", version="1.0.0")
# Add CORS middleware to allow requests from your frontend
app.add_middleware(
CORSMiddleware,
allow_origins=["*"], # In production, replace with your frontend URL
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Load your model
model = tf.keras.models.load_model('trained_modela.keras')
# Define your class names (update with your actual classes)
class_name = ['Apple___Apple_scab',
'Apple___Black_rot',
'Apple___Cedar_apple_rust',
'Apple___healthy',
'Blueberry___healthy',
'Cherry_(including_sour)___Powdery_mildew',
'Cherry_(including_sour)___healthy',
'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',
'Corn_(maize)___Common_rust_',
'Corn_(maize)___Northern_Leaf_Blight',
'Corn_(maize)___healthy',
'Grape___Black_rot',
'Grape___Esca_(Black_Measles)',
'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',
'Grape___healthy',
'Orange___Haunglongbing_(Citrus_greening)',
'Peach___Bacterial_spot',
'Peach___healthy',
'Pepper,_bell___Bacterial_spot',
'Pepper,_bell___healthy',
'Potato___Early_blight',
'Potato___Late_blight',
'Potato___healthy',
'Raspberry___healthy',
'Soybean___healthy',
'Squash___Powdery_mildew',
'Strawberry___Leaf_scorch',
'Strawberry___healthy',
'Tomato___Bacterial_spot',
'Tomato___Early_blight',
'Tomato___Late_blight',
'Tomato___Leaf_Mold',
'Tomato___Septoria_leaf_spot',
'Tomato___Spider_mites Two-spotted_spider_mite',
'Tomato___Target_Spot',
'Tomato___Tomato_Yellow_Leaf_Curl_Virus',
'Tomato___Tomato_mosaic_virus',
'Tomato___healthy']
@app.get("/")
async def root():
return {"message": "Plant Disease Detection API", "version": "1.0.0"}
@app.post("/predict")
async def predict_disease(file: UploadFile = File(...)):
"""
Predict plant disease from uploaded image
"""
try:
# Validate file type
# Validate file type
if not file.content_type.startswith('image/'):
raise HTTPException(status_code=400, detail="File must be an image")
# Save uploaded file temporarily
with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tmp:
temp_path = tmp.name
contents = await file.read()
tmp.write(contents)
# Read image using OpenCV
img = cv2.imread(temp_path)
if img is None:
raise HTTPException(status_code=400, detail="Invalid image file")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
image = tf.keras.preprocessing.image.load_img(temp_path,target_size=(128, 128))
input_arr = tf.keras.preprocessing.image.img_to_array(image)
input_arr = np.array([input_arr]) # Convert single image to batch
# Predict
prediction = model.predict(input_arr)
result_index = np.argmax(prediction)
confidence = prediction[0][result_index]
disease_name = class_name[result_index]
return {
"success": True,
"disease": disease_name,
"confidence": confidence
}
except HTTPException as he:
raise he
except Exception as e:
raise HTTPException(status_code=500, detail=f"Prediction error: {str(e)}")
@app.get("/health")
async def health_check():
return {"status": "healthy"}
@app.get("/classes")
async def get_classes():
"""Get all available disease classes"""
return {"classes": class_name}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860) |