Spaces:
Running
Running
File size: 2,774 Bytes
6a5c7ef 49183b2 bef811e 6a5c7ef 49183b2 65d5227 49183b2 65d5227 49183b2 6a5c7ef 49183b2 65d5227 49183b2 65d5227 49183b2 6a5c7ef 49183b2 6a5c7ef 7bc8f9c 49183b2 bef811e 49183b2 bef811e 59ce7be bef811e 49183b2 bef811e 59ce7be bef811e 49183b2 bef811e 49183b2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
import numpy as np
import gradio as gr
import tensorflow as tf # version 2.13.0
from keras.models import load_model
import cv2
import json
def analyse(img, plant_type):
# Load label_disease.json
with open('data/label_disease.json', 'r') as f:
label_disease = json.load(f)
# Load plant_label_disease.json
with open('data/plant_label_disease.json', 'r') as f:
plant_label_disease = json.load(f)
HEIGHT = 256
WIDTH = 256
modelArchitecturePath = 'model/model_architecture.h5'
modelWeightsPath = 'model/model_weights.h5'
# Load the model
dnn_model = load_model(modelArchitecturePath, compile=False)
dnn_model.load_weights(modelWeightsPath)
# Preprocess the image
process_img = cv2.resize(img, (HEIGHT, WIDTH), interpolation=cv2.INTER_LINEAR)
process_img = process_img / 255.0
process_img = np.expand_dims(process_img, axis=0)
# Predict using the model
y_pred = dnn_model.predict(process_img)
y_pred = y_pred[0]
# Identify plant-specific predictions
plant_label_ids = plant_label_disease[plant_type.lower()]
plant_predicted_id = plant_label_ids[0]
for disease in plant_label_ids:
if y_pred[disease] > y_pred[plant_predicted_id]:
plant_predicted_id = disease
# Determine overall prediction
overall_predicted_id = int(np.argmax(y_pred))
overall_predicted_name = label_disease[str(overall_predicted_id)]
overall_predicted_confidence = float(y_pred[overall_predicted_id])
# Determine plant-specific prediction
plant_predicted_name = label_disease[str(plant_predicted_id)]
plant_predicted_confidence = float(y_pred[plant_predicted_id])
# Determine health status
is_plant_specific_healthy = "healthy" in plant_predicted_name.lower()
is_overall_healthy = "healthy" in overall_predicted_name.lower()
# Return results as a JSON object
result = {
"plant_specific_prediction_id": plant_predicted_id,
"plant_specific_prediction_name": plant_predicted_name,
"plant_specific_confidence": plant_predicted_confidence,
"is_plant_specific_healthy": is_plant_specific_healthy,
"overall_prediction_id": overall_predicted_id,
"overall_prediction_name": overall_predicted_name,
"overall_confidence": overall_predicted_confidence,
"is_overall_healthy": is_overall_healthy
}
return result
# Gradio interface
demo = gr.Interface(
fn=analyse,
inputs=[
gr.Image(type="numpy"),
gr.Radio(["Apple", "Blueberry", "Cherry", "Corn", "Grape", "Orange", "Peach",
"Pepper", "Potato", "Raspberry", "Soybean", "Squash", "Strawberry", "Tomato"])
],
outputs=gr.JSON()
)
demo.launch(share=True, show_error=True)
|