app.py

import numpy as np
import gradio as gr
import tensorflow as tf  # version 2.13.0
from keras.models import load_model  # version matching your TensorFlow
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 predictions
    p_id = plant_label_disease[plant_type.lower()][0]
    for disease in plant_label_disease[plant_type.lower()]:
        if y_pred[disease] > y_pred[p_id]:
            p_id = disease

    overall_predicted_id = np.argmax(y_pred)
    overall_predicted_name = label_disease[str(overall_predicted_id)]
    overall_predicted_acc = y_pred[overall_predicted_id]

    plant_predicted_id = p_id
    plant_predicted_name = label_disease[str(plant_predicted_id)]
    plant_predicted_acc = y_pred[plant_predicted_id]

    # Return results as a JSON object
    result = {
        "plant_predicted_id": int(plant_predicted_id),
        "plant_predicted_name": plant_predicted_name,
        "plant_predicted_accuracy": float(plant_predicted_acc),
        "overall_predicted_id": int(overall_predicted_id),
        "overall_predicted_name": overall_predicted_name,
        "overall_predicted_accuracy": float(overall_predicted_acc),
    }

    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)